text:
Amazingly, complete strangers have been emailing me every week since to ask where the update is. I'm not sure whether the ideas struck a chord, or whether I just pulled the blog equivalent of going into a bookstore and ripping the last pages out of all the detective novels.
Either way, here are some things that happened:
I spent two months in London. Much of that time was spent looking at various companies, labs, jobs and courses online. I bookmarked 49 companies and 13 schools and contacted none of them. I realize after the fact that I procrastinated on the next steps because deep down inside I'm some kind of feral man-ape that can't even handle committing to a single country, let alone showing up at an office at the same time every morning. More on that later.
I did apply for Cognitive and Decision Sciences at UCL. The rolling application process filled all the places before they even reached my application, but a few months later they looked at it and liked it enough to offer me a place in advance for fall 2017. I don't have to make a decision until October, but I'm leaning towards saying no simply because the gap between the decision point and the actual start of the course is so long. I don't want to commit to something and then sit around twiddling my thumbs for 12 months.
I was staying with a friend in Beijing when I wrote the last post and I spent my last week there mostly reading, climbing and eating ridiculous amounts of food. I read 9 books that week, and also started a habit of publishing notes. I'm surprised at how many people seem to find those notes valuable, far more so than the blog posts where I try to communicate ideas that I think are valuable and/or novel. I've also ended up referring back to the notes myself multiple times, especially when trying to dig up citations.
I spent a week in Budapest with another friend where, thanks to forgetting to pack a phone charger, I was offline for the entire week. This was such a pleasant experience that I've since brutally cut down my online activity. (RescueTime confirms that I've consistently stuck to this for two months.) The biggest change was learning to recognize the feeling of craving distraction and closing my laptop/phone in response.
I spent two weeks in NYC for the 'Tools for Thought' workshop. I was really unsatisfied with the talk I gave but the workshop was otherwise fantastic and I got to meet some amazing people. I don't like the city itself though, and I spent a lot of the time there fretting about how much money I was spending without any idea yet of where the next paycheck is going to come from.
I visited Berlin for a week. I met lots of interesting people and wandered into many interesting nooks and crannies, and decided to live here. The quality and cost of living are amazing for such a vibrant international city and the tech scene has more artists and anarchists than startup cultists.
I took an intensive German course in the first month. Contrary to literally every single thing I've heard about it, German is a delight. There are a lot of tables to memorize but it's complicated, not hard, and after that you get to play with the non-linear grammar and compound words, which tickle my brain delightfully. My favorite word so far is Betriebsblindheit – the state of being blinded by routine.
I also spent six weeks on a consulting contract that is not really working out, in part because of the aforementioned feral man-ape thing. Let's talk about that some more.
I have a really strong visceral reaction to obligations, such as having to be in a specific country at 9am every morning. This affects me to the extent that being obliged to do something that I already wanted to do can be enough to make me revolt, flip the table over, sell everything I own and move to Germany.
People have a tendency to tell me that this is just the way the world works and I had better get used to it, to which I tend to reply that this is the way your world works and I didn't vote for it.
I think there is a trick to dealing with this. There are essentially two ways to make money:
Most of the things that bother me come about because of the second option. As soon as someone starts giving me money in advance I suddennly feel obliged to keep doing whatever it is that they wanted me to do, which means that I no longer want to do it.
The first option seems to be much better. There is still a degree of accountability, but it's much more delayed. If I decide to dedicate all my time to adding cat pictures to my work, people may well stop giving me money. But they can't insist that because they are giving me money I can't decide to dedicate all my time to adding cat pictures to my work. The obligation is asserted through my bank account rather than through managerial permission and that somehow makes it better. If that doesn't make sense, it's probably because I'm a feral man-ape and you're thinking in econ. I'm not supposed to make sense.
Let's take a brief empirical detour and look at things that made me happy in the last year. I noticed recently that all the highlights were holidays. That's not surprising in itself, but what is surprising is that those same holidays were also much more productive that the surrounding time. What seems to happen is that most of the year I sit around at home in front of my laptop, refreshing hacker news and beating myself up about not being productive. When I'm on holiday, I give myself permission to do whatever I feel like doing, and occasionally I feel like reading a book or writing some code. And because I don't have to do those things, and because there are plenty of guilt-free holiday alternatives, I don't procrastinate at all.
So if I decide that I'm going to embark on a grand project to make a thing that people will give me money for, the most likely outcome is that I will sit around beating myself up about not doing that.
Instead, I'm going to take a long holiday. I transferred some of my savings to my freshly opened hi-tech German bank account – enough to live on for 3-6 months, depending on how thrifty I am. During that time I'm going to wake up each morning and spend the day doing whatever I feel like doing.
The reason that this is a good plan is that when the money comes to an end and I look back at what I did with the summer, I'd be pretty happy if I spent the summer climbing, or reading, or coding, or learning to juggle, or pretty much anything except for:
But I seem to have got a handle on 1 lately, and 2 is not a problem because I'm on holiday. It may turn out that I do something interesting that leads to people giving me money, but I'm very explicitly not relying on or planning for that. I'm just going to do things I enjoy while I have money because that's the whole point of having money in the first place, and it completely defies the point if I waste all my money on worrying about how to get more money.]]>
Some interesting ideas about human augmentation. Published in 2004, so many of the specific examples come across as quaint and endearing rather than high-tech. Spends a lot of time repeating itself without adding new arguments, and attempting to contest popular fears without really going behind "yes, that could happen, so let's be careful". Nethertheless, the parts on distributed cognition are interesting and it's much more readable than the authors more recent book on the topic.
Argues that augmenting our minds and bodies with technology is a uniquely human ability, and one that has been going on since the beginnings of language.
Basic example of augmentation – long addition using combination of biological pattern matching for single digit sums and pen + paper + trained motor control + vision for short-term memory and problem structure.
Alarms, calendars, notepads, whiteboards, smartphones, computers etc. We structure our environments to support cognition. Take a highly-functioning executive and strip them of their calendars and notebooks and then watch them flounder – like giving them a partial lobotomy.
Body image is plastic. Readily incorporates tools eg brain scans of tennis players show that the racquet is included in their body map. Can easily alter body shape eg fool brains into modelling a meter-long nose or into anticipating pain when a plastic arm is struck with a hammer.
Senses are plastic too eg can turn vision upside down and subjects can function normally after a few weeks.
Language allows concretizing abstract concepts, making them easier to work with. eg chimps only learned to make higher-order analogies if they were taught some symbolic system first. Mathematics similarly grounds abstract concepts so that they can be processed with symbol-shuffling. Hypothesizes that language is required to be able to think reflectively eg "why do I believe X?".
Vision uses environment as it's own best model – repeated saccades to retrieve data rather than caching it in short-term memory. Some kinds of processing require real vision eg subjects who were briefly shown a dual-interpetation image could only figure out one interpretation, but some of them could find the second after drawing the image from memory.
What does it mean to 'know' something:
Can be said to 'know' something if it can be quickly and reliably retrieved, whether from the visual field, from a reliably tool (like a watch), from biological long-term memory or from a smartphone. The latter is currently high-latency and unreliable, but it's only a matter of degree and it will improve over time.
(Human long-term memory can do interesting things like making connections in the background. Machine long-term memory can do interesting things like large-scale search, software agents, collaborative editing etc. No reason to see the former as 'real' memory just because it happens to come preinstalled.)
Similarly, what does it mean to control something:
What seems to matter in these cases is the presence of some kind of local, circular process in which neural commands, motor actions and sensory feedback are closely and continuously correlated.
The difference is only in the degree of bandwidth and latency, not innate. No reason why direct brain interface is special – mechanical/visual/auditory/haptic interfaces can be just as transparent. Beginners play the guitar – experts play music. Tools can become transparent, to the point that we forget that there is a brain stem, nervous system, motor control circuits, muscles, wood and strings between us and the music.
Useful to be able to flip between transparent and visible. Can watch physical actions on the guitar and figure out how to improve them. Can't introspect on most of our brains mechanisms though – they are stuck in transparent mode.
Self is about (reliable) control and (high-bandwidth, low-latency) feedback. Not flesh-and-blood connection.
If we try to divide the system into user and tools we quickly get into trouble. Even inside the brain, no single part is in charge.
Oh, poor soul – she is not really responsible for that painting/theory/poem; for don't you see how she had to rely on pen, paper, and sketches to offset the inadequacies of her own brain?
Give up on trying to locate the self and just accept that we are a dynamic conglemerate of feedback loops (kind of a Katamari Damacy vision of the self).]]>
On the effects of culture and education on intelligence.
Definition of intelligence. Mentions that IQ and g are not the same but doesn't explain the difference. As far as I can tell, the distinction is that g is an unobserved factor responsible for the high correlation between scores on different areas of IQ tests.
Distinctness of Gf and Gc demonstrated by PFC damage (normal Gc, low Gf) and by autism (normal Gf, low Gc). Also by trajectories over lifespan – Gf begins declining long before Gc (but controversy over exact trajectories).
Executive function and crystallized knowledge are separately inheritable. Wait, Gc is heritable? This argues that this is mediated by culture – educated parents are likely to have educated children. I would also hypothesize that need-for-cognition could be heritable.
In young children, reading and math skills are more correlated with Gf than Gc. In adults, reading and math skills are more correlated with Gc than Gf.
PFC is inhibited by limbic system. Sustained stress may result in permanently reduced Gf.
Robert Sternberg distinguishes:
IQ measures the first. Tests for the latter two are competitive in explaining variation in school grades.
Howard Gardner adds emotional intelligence, musical intelligence, kinesthetic intelligence etc. Uncorrelated with IQ.
Duckworth/Seligman – self-discipline correlates with grades much higher than IQ (.67 vs .32), although difference is much smaller for SAT (.43 vs .36 – not statistically significant). Which makes sense, because SAT is basically an IQ test. Tested at a magnet school, not yet replicated.
Yet another set of evidence that IQ is a very limited measure of effectiveness.
IQ is correlated with many life outcomes, controlling for many variables. But this is shaky MRA – 'what nature hath joined together, multiple regress cannot put asunder'.
Study of siblings raised together but with different IQs show high correlation with adult income. Still does not control for eg educational opportunities which are gated by IQ.
So the book is basically going to focus on IQ because it's the only thing that has been heavily studied.
Strong-heriditarian position – at least ¾ of variation in IQ is genetic, some of the remainder is due to uncontrollable within-family factors (eg birth order) and by adulthood almost none of the variation is due to between-family differences. Based on studies of various relationships (twins, siblings, single parents etc) living alone or apart. Implies not much parents can do to improve IQ of children.
But twins raised apart are not in randomly chosen environments – often raised by eg relatives in similar social class / culture. When environments differ, correlation has been reported as low as .27. So actual environmental effect could be .73 or even higher.
Twins share same uterine environment, and also share same features etc which may result in similar treatment.
Lastly, heritability differs depending on class and twin studies biased towards classes with high heritability.
Scarr, McCartney, Dickens, Flynn – genes and environment also interact eg taller kids more likely to be picked for basketball team, leading to better basketball skills over time. Some environments act as multipliers for small genetic differences.
Stoolmiller – adoptive families tend to have high socioeconomic status and rate much higher than average on measures of Home Observation for Measurement of Environment. Low variation in these important factors means that studies based on adoption underestimate the impact of between-family factors. Estimates that correcting for this could put between-familiy factors as high as .5.
Turkheimer – heritability ~0.7 for upper-middle class and ~0.1 for lower class. Likely because environmental variation is lower in upper classes, so heriditary variation is all that remains.
MRA not good enough. Need natural experiments.
Capron, Duyme – tracked down 'cross-fostering' – children fostered into a family of different class. Children born to high-SES parents average 12 IQ points more than children born to low-SES parents (genetics + early env). Children adopted by high-SES parents average 12 IQ points more than children adopted by low-SES parents (late env).
Similar experiment tracked low-SES children adopted into high-SES families, comparing them to their non-adopted siblings. Difference of 12-16 IQ points, depending on test. School failure rate was 13% for adopted vs 56% for non-adopted. Presumably a families that give up a child for adoption are not the best environments to begin with, so this may exaggerate the difference that would be seen from SES alone.
Another looked at low-IQ abused children who were tested at adoption and again 10 years later. No correlation between IQ at adoption and SES. Gains on later test were low SES +8 IQ, middle +16 IQ, upper-middle +20 IQ. Also underestimates effects because even low-SES adoptive families have better than average environments for their class.
Similar findings from meta-analysis of well-designed adoption studies.
Takeaway – environmental factors characteristic of low-SES families reduce IQ 12-18 points below it's potential. Plus whatever confounding effects adoption has!
Degree of heritability places no constraints on mutability anyway eg heritability of height is ~.85 but several countries have seen gains in average height of >1SD in the last generation. This is a fantastic intuition pump.
Education improves IQ: Reduced growth or even drop in IQ over summer holidays (worse for low-SES children – explains much of the academic gap between low-SES and high-SES). Many natural experiments show that missing school years results in steady decline in IQ. Using school cutoff dates shows that a year of age is worth ~½ as much as a year of school in terms of IQ points.
Western-style schooling improves results on memory and spatial perception tasks that are supposed to only test raw intelligence. This is usually interpreted as showing that IQ tests have a cultural bias. Not sure how one would separate the two.
Flynn effect – gain of ~1/3 IQ point per year. Can't be explained by familiarity with tests – steady increase of IQ vs rapid explosion in testing, plus repeated exposure to tests does not increase scores enough to account for the effect. Can't be explained by nutrition – US and Western Europe have not seen significant nutrition deficits during that period (except for two world wars – how did rationing affect IQ?), increases are across the board rather than only at the (presumably nutritionally deficient) lower end.
Taking this literally, the average IQ in 1917 would have been around 73 – retarded by modern judgment. This seems unlikely. Worth comparing this to Stanovich' work – it makes sense that massive increase in IQ doesn't yield massive increase in intelligence because IQ is a very narrow measure of intelligence. Having better abstract reasoning skills doesn't help you much if you don't notice when you need to apply them.
Increase of 2 SD on Raven matrices. Blair, Williams etc note that early schooling today includes much more abstract and visual reasoning, and that math problems have more steps and require more working memory. Similarly, controlled experiments show that certain kinds of video games increase attentional control. Rosario Rueda – used computer games to train attention management and executive control in 4-year olds – showed dramatic improvements on Raven matrices after only five days. Adele Diamond – used structured play that involved memory aids and inhibition control – showed improvement in executive-function tests.
Increase of 1.6 SD on similarities test. Notes that although it's seen as test of crystallized intelligence, it also has significant fluid component in determining which similarity is maximally informative.
Increase of .25 SD on vocab test and 1/3 SD on comprehension test.
Little increase on information test – no extra time spent memorizing.
Little increase on arithmetic test, which is strange because material is successfully taught at earlier and earlier ages and scores on standardized tests have increased substantially. Tentatively notes that arithmetic test mostly measures rote application of skills, whereas standardized tests also stress reasoning and working memory.
Studies showing that voucher programs work are flawed – parents self-select. Intent-to-treat experiments show improvement of .10 SD (in grades?). Similarly weak results of randomized entry into charter schools.
MRA on class size usually reveals little effect, but a randomized controlled experiment in Tennessee found that smaller classes (average 15 students) had results .19 to .28 SD better than larger classes (average 22 students), and effects persisted for at least three more years afterwards and were greated for low-SES and minority children.
Teacher experience matters, but most improvements happen in the first year of experience. Estimates of impact of teacher quality (as defined by increase in their class scores vs average increase) is .27 SD (and measured at .20 SD within schools).
Unions oppose rewarding teachers based on measures other than certification or seniority.
Educational research is poor. Mostly anecdotal. Actively opposed to experimental method (reminds me of the situation in medicine previously, were doctors were opposed to experiments on the grounds that denying treatment to the control group is unethical).
Studies of whole school interventions are not conclusive or convincing.
Kulik – across 16 controlled studies of computerized teaching of maths (description sounds similar to Khan Academy), found median effect size of .40 SD. Similarly dramatic results for writing and natural/social sciences.
Student Teams Achievement Divisions – students work/study together in heterogenous groups of 4 – controlled experiments show effect size of .30 SD. Structured dyad – students alternating role of tutor/tutee – similarly impressive effect sizes.
Richard Herrnstein – taught [long list of techniques] (much overlap with list of rationality mindware in Stanovich' work – very exciting) – .62 SD for language comprehension, .46 SD on representing 'problem spaces' (what are this?), .77 SD on decision making, .50 SD on inventive thinking, .35 SD on general-abilities test, .43 SD on IQ test, .11 SD on Cattell Culture Fair Intelligence Test (similar to Raven matrices). Then Venezuela government changed and program was cancelled!
Lepper gives advice on effective tutoring:
Biological influences associated with low-SES: Some evidence that supplementing micro-nutrients in low-SES children increases IQ. Lead exposure very strongly linked to IQ loss, and exposure is somewhat correlated with class eg inner-city low-SES kids. Even small amounts of alcohol (< 2 ounces) in pregnant women affects IQ, attention and memory, and drinking during pregnancy is more common in low-SES mothers. Lower health in general in low-SES children eg dental health, exposure to pollution, vision and hearing – compounded by worse medical care. Some pesticides still common in low-SES households are associated with lower IQ. Low-SES mothers less likely to breast-feed, which may impact IQ – still debated.
Environmental influences associated with low-SES: Less stable environment eg move homes more frequently – extra stress plus disruption to schooling. Behavioral problems more common. McLoyd – low-SES parents less likely to be warm and supportive, more likely to use strict punishment. Early emotional trauma damages the PFC (why?). These influences don't have to be that widespread to have a large impact – knock-on effects on low-SES classes, neighborhoods and social peers.
Skill disparity between US social classes – math, lit, sci as measured by OECD show almost 1 SD difference between bottom class quartile and top class quartile. In Scandinavia only 2/3 SD, mostly bringing up the bottom. In South Kora only ½ SD. US pattern is closer to developing countries.
US economic gap also bigger. Would raising income reduce IQ gap? Maybe, but cognitive culture is also a problem.
Hart + Risley – verbal behavior towards kids across classes. Working class parents converse less and demand more, include kids in conversation less. 1300 words / hour vs 200 words / hour => 50% more vocab. Ratio of reprimand to encouragement 6:1 vs 2:1.
Heath, and later Lareau. More reading to kids. More books. Begin reading earlier. Relate content to outside world. Encourage analysis (why did X, what if X etc, valuation). More active conversation vs reactive. => Better prepared for early school, less likely to be left behind.
Activity instruction more verbal, less physical. Low SES children do find with 'what' questions, but struggle with non- known-answer questions – categorization, analysis, evaluation, counterfactuals.
IQ drops over sumer.
Gains from income improvement don't show up in first generation.
Black/white IQ gap in US was 15 points in 80s ie 1 SD! Gap exists even controlling for SES and stereotype threat.
Default belief is black/white gap is genetic. Have to break taboo on discussion in order to change minds. Strong ethical argument – refusing to acknowledge black/white gap is preventing understanding which prevents intervention.
Rush – smaller brains in blacks – brain size to IQ correlates at 0.4. But brain size not causal eg in siblings. Brain size gap exists between male/female but no IQ gap. Exists small brained community in Ecuador with normal IQ. Evolutionary direction is towards smaller brains anyway.
Genetic mixing as natural experiment. Skin color and Caucasian features each correlate with IQ ~0.1, despite social advantage. In WW2, black/white soldiers raising children in Germany – IQ gap in children is only 0.5 points. IQ in black kids in Chicago not correlated with number of European ancestors. Similarly for blood groups found more often in whites.
B/W adoption studies show gap but many flawed eg different average adoption ages, which is known to affect IQ. Moore adoption study accounts for this and found no difference.
Eagon + Holland – test B/W community college students. More vocab for W but no difference in ability to learn new vocab. Better comprehension and verbal scores, but no difference when tests are restricted to common vocab. So difference in prior knowledge but not in raw learning ability. Learning ability predicts existing knowledge within B/W but not across. Not entirely sure how to parse the stats here. Needs replication though.
Flynn effect. B IQ now > W IQ in 50s. B/W IQ gap dropped from 15 in 80s to 7.5 now. More improvement for B than W in NAEP long-term trend for reading and maths.
Gap has huge effect at tail – 15 pt difference means 18x as many W at IQ 130+, 7.5 pt difference means 6x as many W as IQ 130+. Same effect for dropouts at other tail.
B:W income 67% but B:W wealth only 12%. Redlining – keeping B out of desirable neighborhoods.
Single-parent rates higher in B.
Negative beliefs about employability of poor, young, black men. Application experiment – W resumes with felonies got better responses than otherwise identical B resumes with clean record for entry level jobs. (Might lower incentive to finish education/qualification if it doesn't have as much value due to prejudice).
Black females 30% more likely to graduate than males (in 1965). 2x more likely to have IQ 120+. Is B/W IQ gap lower between females?
Similar effects for other low-status groups worldwide eg untouchables in India.
Involuntary minority vs autonomous minority. Argues involuntary minorities lack 'effort optimism' – even anti-effort eg 'acting white'.
Early US history. 'Colored men preferred, no Irish need apply.' Higher occupational status and higher income for black vs irish (evidence presented is thin).
19th cen Boston blacks had ¾ college attendance. B high school scored higher than all other schools on city-wide standard tests. School produced many elite members eg first black general.
Post- Civil War migration overwhelmed existing Northern culture. Slavery, sharecropping, Jim Crow, denied entry to most unions. No reward for work so no effort optimism in culture.
Similar effects for Irish under English landlords. But Catholic church put heavy weight behind education and opportunity.
Whole historical section is interesting but weak evidence. Skipped section on West Indies culture which was especially weak.
2/3 black mothers are single earners => lower IQ kids, more dropouts => next generation of men don't have work => more single earners.
Heath again (in 60s/70s). Similar parenting differences between B/W with same SES. B welfare kids – 600 words / day (previous were per hour, which is correct?). 3 yr children talking to professional parents show wider vocab than black mothers on welfare talking to children!
Don't talk to children directly, or interpret their early noises as words. No manipulative toys/puzzles. No books. No known-answer questions.
Uses very absolute terms here – what were effect sizes? What about effects of bias in observers?
Philips + Brooks-Gun (80s/90s). CNLSY and IHDD both confirm that B families score lower on HOME. Within B families, HOME scores correlate highly with IQ.
Schooling alone can't fix gap, and even schooling can't be fixed by mandate.
Head Start. 3-4yo poor kids. 5 half-days per week. Has almost closed SES mortality gap. Initial gain of ~.35 SD on cog tests but fades over time, gone by late elem school. (Not well-controlled – groups outside program also went to some pre-kinder). Very little long-term evaluation, but 2-5% higher HS completion and 3-6% higher college attendance.
Meta-review of best (best by performance? that seems like a poor way to select data) small-scale interventions focusing on black children shows larger effect sizes but still fading to nothing.
Which programs produce long-term gains?
Perry Preschool Program. 1962-7 in Michigan. Selected black kids in poverty with low IQ mothers. Start at 3-4yo, 2yr program. Focused on cognitive and social development. 6:1 teacher ratio. Weekly 90min home visit to involve mother in education. Controlled randomized! Mean IQ 95 vs 83 at end, reverting to mean by 10yo. But huge gains in achievement eg at 14yo reading, math, language tests .50-.75 SD higher, HS grades .57 SD higher. Higher income, home ownership, marriage rates. Lower welfare, arrests. Improvement presumably due to temperament/motivation rather than raw intelligence.
Milwaukee project. Same conditions, except lasting from 6mo to first grade. 7 hrs/day, 5 days/week. Tried to reproduce middle class environment. Provided food and medical care for kids and training for mothers. Mean IQ 124 vs 94 (at end of program?) (vs 113 for not-at-risk kids)! In grades 1-4 had higher test scores at ~.75 SD (but p ~ 0.1).
Abecadarian Program. Poor, mostly black, low IQ mothers, other risk factors for retardation. Two interventions, four groups.
Kinder intervention. Starts at 6mo, year-round, full-day. 6:1 teacher ratio. (Note control group also had some form of daycare.)
School intervention. Home-school teacher, biweekly parent-teacher meetings, assist parents with employment, paperwork and doctors visits.
All groups were moved to good schools.
Not much effect from school intervention.
At 21yo, still a 4.5 IQ gap from kinder intervention. Huge achievement change. At 15yo, reading 1.4 SD, math .86 SD. 2/3 vs ½ HS graduation. 1/3 vs 12% college admission. At 21yo, 2/3 vs 40% in higher ed or skilled job.
Several partial replications. Project Care paired kids with identical scores on Bayley Mental Development at 6mo. By 12mo, test group scored 11pts higher (what is SD?). Another replication only ran 1yo to 3yo but still showed 3.8-5.3 IQ effect at 18yo (but achievement difference was minimal and only significant in very undersized babies). Are these deliberate replications, or just similar studies?
Not much research on home interventions. Anecdotally, seem to work best when coaching specific behaviors.
Laundry. 10-20 1.5hr sessions. Coaching response patterns to infants. High effect size, including on emotional/behavioral measures.
Charter schools make no measurable difference. Similarly for most whole-school interventions.
Project SEED – hire STEM professionals to teach poor minority kids. Socratic questioning, dialogue, debate. Test in Dallas on CAT scores showed .37 SD over comparable schools, or .19 SD over non-SEED students in same school.
Reading Recovery. Low-performing first-graders. Daily 1:1 30 min lessons for 12-20 weeks. Randomized found effect sizes of .57-72 SD across different measures, fading to .20 SD by third-grade.
Knowledge Is Power Program. 7:30am – 5:00pm weekdays. Three extra weeks in summer. Home visits. Set social/behavioral expectations. Rewards and penalties for achievement eg points redeemable for trips and snacks. Teacher burnout from high hours / effort. Evidence is mixed. Independent study by SRI international showed large improvements in number of kids scoring above median on SAT over years of schooling (this is a weird measure to report – is the mean effect less impressive?). Similarly, large improvements on number of 'proficient' scores on CAT. Self-selected application to school though and attrition rate is 9% / year, but at least effects are seen within-subject. Randomized controlled replication is needed.
Dweck and mindset.
Aronson et al. Poor Texas HS kids assigned mentors. Control group mentors focus on drugs. Test group mentors focus on change mindset. Reinforced by web site presenting same message, and by assignment where students represent the message. TAAS math scores .64 SD higher for males, 1.13 SD higher for females. TAAS reading scores .52 SD higher.
Oyserman et al. Poor minority HS kids. Several sessions on life planning, predicted difficulties, planning to counter difficulties and thinking which friends could best help. Small group discussions on dealing with social and academic problems. What did control group do? Improved .23 SD for GPA, .36 SD for test scores, .60 SD for retention odds. SD seems like the wrong measure for retention odds – not interested in a significant improvement if it's still small in absolute terms.
Walton and Cohen. Intervention designed to convince participants that worries over social acceptance are normal and not a sign of racial acceptance ie countering stereotype threat for social acceptance. Grades in black students show ~1 SD improvement. Minimal effects for white students.
NLSY shows B/W AFQT (comparable to IQ) gap grows throughout HS, from ~3/5 SD to ~1 SD. Hereditarian argument is that genes have more and more effect throughout development. If the gap stayed the same, they could argue it was because education has no effect. If it was reduced, which way would we fall? Myerson et al found that B/Q IQ gap shrinks in college though. Could be differences in school quality, peer pressure, stereotype threat / self-identity. Surprised the author doesn't suggest self-selection eg kids who don't self-identify as academic/intelligent are probably less likely to apply to college.
Societal cost-benefit ratios of these programs. Heckman estimates 8:1 for Perry Preschool Program in less special ed classes, less extra schooling, lower crime, lower welfare, higher income. Similarly, Abecedarian program estimated at 3.78:1.
Total cost of PPP for poorest 1/3 children ~$105b. But existing public pre-kinder care is already $20b, not to mention private programs and the income gains for mothers. By comparison, 2001 tax cuts for richest 1% estimated to cost $94b.
KIPP much cheaper, but free teacher labor not sustainable. If it paid public school rates, would cost an extra $35b over existing schools.
Author not advocating for installing specific programs, just for further and better research into the area.
Generally concerned with jelly-bean effects in the mentioned studies – mentioned that many interventions fail, cherry-picked those with good results and then told a story about them. I need to learn how to correct significance testing for multiple post-hoc hypotheses. The hypothesis does at least have plausible causal mechanism based on eg known correlations between HOME and IQ, and discipline/conscientiousness predicting achievement, and poor home environment predicting poor delay of gratification.
Flynn. Meta-review with corrected methodology finds mean East Asia IQ slightly lower than US. But huge achievement gap eg on TIMS US are .75-1.0 SD lower on math and .33-.50 SD lower on science than Japan, Korea, Taiwan, Singapore, Hong Kong. This gap starts small and grows throughout schooling. (Also incidentally demonstrates that achievement tests are not just disguised IQ tests.)
Asian children are more likely than American children to attribute success to hard work over natural gifts. American-Chinese children fall somewhere in the middle.
In repeated tests, Asian college students were more likely to respond to failure on the first test by working even harder, whereas Canadian students were more likely to work harder if they did well on the first test.
In the case of Chinese culture, the author attributes this to a long history of tying success to academic achievement and to a strong sense of familial obligation.
Compared to American students, Asian students pay more attention to environment in pictures, are more likely to attribute behavior to environment, are less prone to FAE, are less likely to attribute the confuse the contents of a forced essay with the opinions of the writer, more likely to group objects by relationship rather than category, less able to separate logical statements from the concrete grounding. Some of these are also corroborated by brain scans showing more/less activity in the corresponding areas.
Asian-Americans and residents of Hong Kong fall in the middle, implying that these differences are social rather than genetics.
Like the previous section, there is are also many cultural just-so stories that are interesting but largely unsupported.
Jews were the first national group to achieve universal male literacy, by about 1700 years. I looked this up and the very first result argues that this was a no-true-scotsman kind of affair – 2/3 of the population simply stopped being Jewish rather than learning to read.
Ashkenazi Jews are massively over-represented among Nobel Prize winners (15-200x, depending on how you calculate it), and similarly for Turing award and Fields medal. Similarly over-represented among Ivy League students. In 1931 Polish census, Jews were 9.8% of the population but owned 22.4% of the wealth. In 1938 they owned 55% of medium-large businesses. Average IQ is 2/3 – 1.0 SD above average for white population.
Notes that the over-representation in various fields is much larger than would be naively expected from IQ alone.
Average IQ of Sephardic and Oriental Jews is no higher than average, even in Israel. Suggests a genetic source, but the various theories are problematic:
Have to put this in the context of many cultures which have similar levels of literacy and wealth but radically different levels of achievement. Similarly, various cultures have dominated in science and technology at different periods in time with no accompanying massive genetic shift.
So, what could contribute to a cultural explanation? The author gives a pile of anecdotes, acknowledges that that's all he has, and quickly moves on the next chapter.
Can draw some obvious conclusions about how to raise your child's intelligence from the various correlations on previous chapters.
No evidence that particular toys or music will help. Studies that concluded that stimulation is important for neuron growth compared rats locked in dark cages to rats allowed to play outside with other rats. Similar gains are observed for children moving from drastically poor environments to good environments, but no data on whether stimulation past that point is beneficial.
Exercise is plausibly beneficial. Bigger, healthier babies are more intelligent later in life. Increases growth of neurons and blood supply to brain. Staves off age-related decline. Consistent strong correlations between daily exercise and fluid intelligence.
Breast-feeding for the first nine months is beneficial for most populations, especially for premature babies.
Anticipation exercises, stimulus discrimination, conflict resolution and inhibitory control exercises all seem to have an effect, and can be found at http://www.teach-the-brain.org/learn/downloads/index.htm
Working memory and attention exercises have improved scores on Raven IQ tests.
Meditation has improved scores on Raven Progressive Matrices.
Self-control (tested by delayed gratification) is a better predictor of achievement than IQ. Don't know if it is causal, but seems plausible. No strong evidence on how to improve self-control, but children appear to learn from the examples of adults around them. Growth vs fixed mindset also appears to matter.
Giving rewards reduces intrinsic motivation. Probably better to cultivate the latter where possible rather than using bribery, where possible.
Tutoring: control, challenge, confidence, curiosity, contextualize.
Try to get children into classrooms with experience, effective teachers. Certain computer programs for teaching reading, math and science are strongly supported by the evidence and should be used. Similarly for cooperative learning tasks. Visit the What Works Clearinghouse for evidence.
No evidence that teachers with certificates or higher degrees are any better.
Heritability of intelligence is not a fixed value. It's high (up to 70%) for high-SES families where relevant environmental variation is lower. I don't actually recall any direct evidence in the book that environmental variation is lower in high-SES families.
Heritability doesn't place limits on mutability – it's only measured within a particular set of environmental variations.
Variation between social classes is a result of environmental differences more than genetics.
The evidence for a genetic role in IQ difference between races is strongly refuted by more careful attention to environmental variation in adoption studies, by the shrinking IQ gap between in the US and by Flynn effect lifting up all races past previous highs.
Differences between races begin in the home and in early childhood and so are unlikely to be erased by changes in schools alone.
There are promising interventions for low-SES and minority children, some of which are practically free.
By far the most useful part of this book is the comparison to height, which is ~90% heritable in any given study and yet which has seen huge environmental effects in practice. It's a fantastic intuition pump for what would otherwise be a subtle point of statistics.
The observation that cross-race and cross-class adoption studies neglect to account for lower environmental variation in adoptive families is fairly convincing, especially when combined with the fact that the few studies that control for environment find stronger environmental effects.
The general lesson that observed variation limits observed correlation is well-worth remembering. I wonder how many places I will notice this mistake now that I have been given such a good intuition pump.
The overall case that intelligence is more mutable than generally believed seems pretty strong, but I haven't read anything from the opposing side. The section on Ashkenazi Jews seemed weaker than the others – the criticism of the genetic theories wasn't matched by much in the way of evidence and the cultural explanation was even weaker.
The chapter 'Raising your child's intelligence… and your own' did not in fact discuss raising intelligence in adults. Come to think of it, I don't think there was any discussion in the entire book of whether intelligence is mutable in adults.
I am certainly convinced now that much more research into early life interventions is warranted. If the examples here aren't just cherry-picked flukes then the potential knock-on benefits for society far outweigh the costs.]]>
The whole premise of the book is to discuss the disadvantages of "the self". It defines "the self" as "the mental apparatus that allows people to think consciously about themselves" and attributes these abilities to it:
In the same section it also seems to equate the self with System 2 in general – "One important difference between automatic and controlled processes involves the fact that controlled processes require a self, whereas automatic processes do not. To make deliberate decisions to control their natural reactions, people must be able to think consciously about themselves and the implications of their behavior."
So why did the author lump simulation, planning, executive control, introspection, empathy, identity and status into a single concept? I think the problem probably came right at the start – "I believe that the defining difference between human beings and other animals involves the nature of the human self." The desire to find a single defining difference between two extremely complex information processing systems seems misguided, at best.
I notice computational approaches to cognition fall into this sort of trap less often because it distances us from our built-in mind-simulating machinery. If we think about the list of abilities above from the point of view of an information processing system, it seems far less clear that the word 'self' refers to the same representations or processes in each one.
For example, we can imagine a mind that can make accurate theories/predictions about the knowledge and physical abilities of itself and others, but not about motives/values/personality. Such a mind would be able to make complex plans to deceive it's peers (reasoning about knowledge), but would be constantly surprised by it's own behavior (reasoning about motives). Does this mind have "the mental apparatus that allows people to think consciously about themselves". It's too fluffy a concept to decide.
(If this mind seems implausible, bear in mind that there is a fairly compelling argument that most human introspection is actually posthoc story-telling, and that we have very little access to our actual thought processes. Every time you make plans contingent on getting up early in the morning and then find yourself sleeping in, you have failed to predict your own behavior.)
The book then rambles on through the disadvantages of 'the self', such as self-serving biases, self-chatter, interference with practiced skills, many of which seem to have little connection even with the authors definition of self. It's a morass of linguistic confusion, held together only by the fact that the language it's written in came long before any kind of understanding of cognition and so happens to use the same word for many different capabilities and experiences. They intuitively feel connected, but it falls apart as soon as one tries to imagine the underlying machinery.
Not every book has to be rigorous, but this book doesn't have anything else to offer. No interesting thesis. No actionable advice. Nothing to take away.]]>
Authors perspective – not scientific consensus!
Trying to explain the actions of another person is not so different from trying to explain your own actions. In both cases, we are trying to infer the internals based on observable clues. For our own thoughts we have somewhat more observations, but on the other hand we are significantly more biased towards flattering, rational and/or coherent explanations of ourselves. I keep expecting someone to coin 'naive introspection' as a counterpart of naive realism.
I argue that we should draw a radically different moral. That interpretation of the motivations of real people is no different from the interpretations of fictional characters. There is no 'deep' truth about the real Anna's motivations, any more than there is for the fictional Anna. No amount of therapy, clever experiment, or neuroscientific measurement can recover true motives, not because the search is too difficult, but because there is nothing to search for.
I quote this in it's entirety, because I don't think I can fairly paraphrase it at this early point.
Rather than deep planning or motives, think of your mind as improvising as it goes along. Like case law – we create opinions on the fly and then try to be consistent – to stay in character. This sounds like it is heading towards the logic of appropriateness – making decisions via identity / via the question "what would someone like me do in this situation".
Hindsight bias and post-hoc rationalization make it very difficult to accurately recall past expectations, motivations and reasoning. But there may not have been any to recall in the first place. We are so good at spinning up plausibly stories on the fly that it feels as if we are exploring a great web of reasoning and motivation, but it's an illusion.
Perception illusion as analog – can only see detail in a tiny fraction of our field of vision, but we anything that we call attention to is swept by that fraction, creating the illusion that we can see in detail in a much wider field.
Give shots of adrenaline to test subjects and then annoy them. If they don't know they have been given adrenaline, they attribute the physical feelings to annoyance at the confederate. If they know they've been given adrenaline, this effect is reduced. That is, when trying to figure out what they are feeling, the lack of information is causing them to mistakenly attribute feelings that they would not have perceived otherwise.
Commitment effect. Get people to make a choice, change their choice without them noticing, and they defend the new choice.
Decisions based on heuristics, justified on-the-fly afterwards. Heuristics very sensitive to framing etc.
"Be true to yourself" bugs me for these reasons – it relies on the idea that there is some hidden immutable 'true self' that you just need to uncover. In reality, it appears that our selves are more of an ongoing narrative imposed over past actions and events.
Self-discovery => self-writing.
Easterlin paradox – richer nations are not happier on average, but richer people within each nation are. This is heavily disputed. I have another set of notes coming up on how ridiculously hard it is to justify anything that claims to measure happiness within a single population and experiment, let alone comparing data from different surveys across different countries and timespans.
Maybe what this is really telling us is that we don't have an absolute sense of how happy we are, so we come up with a number on the fly based on whatever comparisons come to mind easily. This would explain why framing and current mood have such a big impact on reported numbers.
Certainly for senses we can easily measure in the lab, like colour or weight, human perception is much better at dealing with comparisons than with absolute measures. The illusions demonstrating that colour perception is relative work because our visual system adjusts for illumination, to more correctly perceive the actual absolute colour of the material. Similarly, it's hard to directly perceive the size of something in your visual field because the visual system is using environmental cues to figure out it's actual absolute size. These both seem like terrible examples for arguing that perception is fundamentally relative rather than absolute – they are caused by postprocessing that's trying to remove relative effects and determine the underlying absolute property.
By changing the options presented, can change peoples risk/reward sensitivity. A given person might generally choose one of the middle options presented, rather than having a fixed sensitivity against which all the options are compared. The author discusses experiments they ran with presumably small or simulated losses and generalises to major life decisions. But say I give people a series of options wagering their house against small amounts of cash on a coin toss – noone would take any of these options, regardless of the framing. So there must be a point at which absolute values of risk start to be felt, but the author talks as if decisions are always totally relative.
People who are given a bigger pile of cash are willing to pay more to avoid electric shocks.
You don't normally walk around shops continually assessing how much money is in your wallet and thinking, well, that chocolate bar does seem a bit expensive. But on the other hand, I've got 20 pounds in my wallet. So I'll buy it.
Of course people are more price-sensitive when they have less money! And this makes perfect sense eg if my salary doubles then suddenly my time-money tradeoff has changed and I should more willing to spend money to save time. The interesting thing here is that the perceived value of money can be distorted by proximity, not that people don't have an absolute value attached to money.
I really enjoyed the thesis and I would love to read a carefully written text on the subject. Unfortunately, this isn't it. The discussion is irritatingly sloppy – possibly because it's transcribed video rather than edited writing. Either way, it's pretty low value reading.]]>
Changing peoples behavior is hard. Things that don't work well: just have more self-control, put stricter punishments in place, nag repeatedly.
Need to attack on three fronts simultaneously:
There are a lot of interesting, in-depth examples in the book. I'm not going to type them all out :)
Example: Donal Berwicki wanted to reduce rate of death due to accidents in medical procedures.
Started with a completely clear goal:
Here is what I think we should do. I think we should save 100,000 lives. And I think we should do that by June 14, 2006 – 18 months from today. Some is not a number; soon is not a time. Here's the number: 100,000. Here's the time: June 14, 2006, 9am.
Brought family of patients who had died due to errors to give speeches:
I'm a little speechless, and I'm a little sad, because I know that if this campaign had been in place four or five years ago, that Josie would be fine… But I'm happy, I'm thrilled to be part of this, because I know you can do it, because you have to do it.
Focused on six interventions that were simple to implement and had the potential for a large impact. Made it easy to join the campaign – hospital CEO just has to sign a one-page form to get started.
Estimated number of deaths avoided was 122,300.
Find bright spots – places that are already working. Eg fighting malnutrition in Vietnam – look for villages with low rates of malnutrition and see what they are doing differently. Advantage of having an already implemented example to work form, and comes from inside the community so faces less resistance or cultural surprises. Gives clear direction – do what they are doing. Counters the tendency to focus on what's going wrong, blinding you to potential solutions.
Script the critical moves. Making decisions depletes mental resources, eventually leading to overload and paralysis. Give people clear frameworks, flowcharts, defaults etc to avoid the paralysis. Eg railroad CEO created list of four triage rules to bootstrap out of bankruptcy: only invest money in projects that produce short-term revenue, always pick the solution that is cheapest up-front (even if it costs more later), refer quick short-term fixes to slow long-term fixes, reuse or recycle materials instead of buying new. A simpler example is setting a specific time to carry out a new habit you are trying to create, rather than just trying to do it 'regularly'.
The book keeps referencing the food pyramid as an example of poor behavioral change, so I'll point out the Precision Nutrition meal guide as a example of how to do it right.
Point to the destination. Provide an inspirational destination rather than focusing on what's wrong with the current state. Bad: "increase production rate" or "lower defect rate". Good: "democratize the automobile". BHAG – big hairy audacious goal. There seems to be a clear danger of killing motivation if the goal is too hairy. Focusing on the successful case studies is a clear survivorship bias.
Set black and white goals to avoid the possibility to rationalizing your way into failure. Bad: "drink less wine". Good: "no wine ever". No wiggle room in the latter. Eg BP announcing "no dry holes" rather than "increase success rate of drilling".
Find the feeling. Combine intellectual arguments with emotional appeal. Eg rather than "we lose money because our factories don't standardize equipment purchases" go for "here are 16 tables piled high with examples of all the different kinds of rubber gloves we buy" – immediate impact.
Tailor message to create an appealing identity / self-image. Eg chemo isn't the cause of sickness, "it's the weapon you use to get your life back – take the pills and you can stop being a cancer kid forever".
Rule of thumb: negative emotions are useful for inducing quick, specific actions whereas positive emotions are useful for driving long-term effort.
Shrink the change. Find the smallest possible starting step, and build up momentum from there. Eg rather than deciding to stop being a slop and live in a perfect palace of cleanliness, decide to spend five minutes after lunch every day cleaning. I set a rule that I have to go outside first thing every morning, which has led to having a regular exercise routine. The trick is that when I wake up I feel shitty but will grudgingly agree to go outside just for a minute to satisfy the rule, but once I get outside in the sun and fresh air I start to feel like moving. Now that I think about it, it's kind of strange to have a routine that's built around successfully lying to myself every morning.
Grow your people. Create and reinforce the identity that will lead to the behavior you want. Commitment / consistency effects can be abused to this end. Create the expectation of short-term failure to make motivation less fragile. "This is going to be challenge. There will be setbacks along the way. We will ovecome them."
Growth mindset blah blah blah.
Place reminders and nudges in the environment. Eg giving nurses special vests to wear when giving out medication, so that everyone knows not to distract them. I worked at a startup that happened to have blue floor-level lights in the office, so they decided to have 'blue days' twice a week. When the lights are on, the main office is silent and the chat room is offline. Helped carve out distraction-free periods for deep concentration.
Remove unwanted temptations and make dangerous behaviors impossible. Eg industrial machines that require two high-up buttons to be pressed before they activate, ensuring that both hands are well out of the way.
Action triggers – specify the precise environment (time, place etc) that will trigger the action you want to remember to perform eg "I will do this homework sitting at my kitchen table after finishing breakfast". Both a reminder and an artificial deadline.
Create habits that promote the behavior you want eg eating two cups of soup per day as part of a diet, to increase feelings of fullness. Similarly, create rituals to help set up particular mindsets eg morning routine of assembly etc in schools helps switch kids into school mindset.
Use checklists.
Rally the herd. Use peer pressure eg surround the worst offenders with good examples. Place highly visible confederates to kick off behaviors eg find a few people in your company who behave well and make their success visible, so that it feels like everyone else is doing it. Report peer behavior when it isn't visible eg hotels leaving signs saying "most guests reuse their towels" leads to more towel reuse than signs asking guests to reuse towels. Create fake peers if necessary eg Winstens successful campaign to have as many TV show as possible mention the concept of a designated driver in the dialogue, eg creating a despicable sugar daddy character on Tanzanian radio whose name became an insult.
Keep the switch going. Reinforce desired behaviors with rewards, rather than punishing undesired behaviors. Begin by rewarding an improvement, and then slowly raise the bar.
(There is a final chapter on examples of typical obstacles to change and how to overcome them, which is effectively just more case studies.)
I don't know what to with all of this. It's the same reaction I have with all the Heath books, where it all sounds like reasonable, self-evident advice but I have no way to judge it.
Probably the only thing I will attempt to remember is the breakdown into direct the rider + motivate the elephant + shape the path, as well as the idea of scripting critical moves. I tend to forgot one or more of those.]]>
I really wanted to like this book, but it spends page after page applying basic fill-in-the-blank HCI principles while totally ignoring the fact that the problem is not that the highly paid designers at big tech companies don't understand these principles – it's that they are paid to optimize for distraction and addiction, not for calm.
I don't need a book to tell me not to drown people in notifications. I need a book to tell me how to make people care.]]>
Given the authors credentials, I would read all of the brain science in this book at the level of newspaper reporting – interesting, but wants expert confirmation. However the conclusions they draw from that science are, in my anecdotal experience, self-evident, so the book is still valuable.
Deep work: Professional activities performed in a state of distraction-free concentration that push your cognitive capabilities to their limit. These efforts create new value, improve your skill, and are hard to replicate.
Shallow work: Non-cognitively demanding, logistical-style tasks often performed while distracted. These efforts tend to not create much new value in the world and are easy to replicate.
Digital lifestyle tends to push towards shallow work/interaction – social media, instant connectivity, infinite supply of infotainment.
But new technologies also increase value of deep work. Firstly, specialization and rate of change have both increased, which means that the ability to quickly learn complex subjects is increasingly valuable. Secondly, easy distribution provides leverage, increasing the value of the best work and reducing the competitiveness of everything but the best.
Increasing division between high skill jobs and everything else, due to distribution, automation and globalisation. 'Average is over', winner takes all. This 'great restructuring' favors:
Deliberate practice as the foundation of expert ability. Requires that:
Not compatible with persistent distractions and diffused attention.
At the neurological level, learning requires repeatedly isolating circuits. Distracted attention fires too many different circuits.
Task residue – in serial single-tasking, performance on each task is affected by the previous task. This residue is worse if the previous task was unbounded, low intensity or left uncomplete. Context switches in humans are costlier than we are consciously aware of. Implies that eg checking emails while your code compiles is not a pleasant break, but actually inhibits performance for long periods afterwards.
The type of work that optimises your performance is deep work. Slightly fuzzy arguments in places, but I think it's driving at focusing on increasing comparative advantage – being able to do some particular thing far better than the average person.
This only applies to some kinds of work eg CEOs are necessarily engaged in shallow work most of the time because their job is to act as a decision hub. The ideas in this book do not make sense if your job requires constant, diffuse attention. 'Shallow work' might hit pushback because of the connotations. Would make more sense to contrast focused attention vs diffuse attention, but that would be a less catchy title.
Several anecdotes about people who believed their work fell under this category but who found that when forced to disconnect regularly their value actually improved. This suggests experimenting with different ratios of time spent on focused vs diffuse attention, to determine what balance works best for your situation.
Open plan offices. Company chat rooms. Social media presences as part of professional responsibilities.
If deep work is so valuable, why the move towards distraction?
Metric black hole – can't easily measure productivity for most knowledge workers. Insecurity results in pressure towards appearance of productivity instead – look busy, send lots of emails etc.
Cochran experiment measuring time spent on email, resulting in company-wide guidance on how to reduce email to only the most valuable.
Consulting group forced to spend one day per week without email access. Resulted in better satisfaction all round.
Knowledge workers generally don't have a clear metric by which to judge their work, and even when they do it's usually a very long causal chain away from their actual actions.
Principle of Least Resistance: in a business setting, without clear feedback on the impact of various behaviors to the bottom line, we will tend towards behaviors that are easiest in the moment.
Planning and prioritizing is hard. Responding to the top email in your inbox is easy. I notice that many people, myself included, tend to start the day by opening their email and then often don't make it to actual work until lunch.
Same goes for excessive status meetings as a form of deadline / motivation control, and to provide constant reassurance of progress.
Metric black hole + principle of least resistance + busyness as proxy for productivity = shallow work.
I find myself in the weird position of believing the claims to be roughly true, but still not being convinced by the argument as presented in the book.
Cult of the Internet. Technopoly. Assumption that anything high-tech is inherently good, and is the future. Refusal to adopt seen as Luddism, couldn't possibly be the result of consciously weighing costs and benefits.
Attention determines experience, as much as circumstances. Some links between mindfulness and happiness / resistance to depression. Also some evidence that executive function can be increased with practice, with far transfer. With intense enough focus, there is not enough leftover attention for anxiety or worry.
Shallow work is draining, unsatisfying, stressful. Evidence provided is slim, but anyone who is reading this can probably conjure up a contrast between memories of good work days vs memories of days spent refreshing inboxes. Fairly confident that experience sampling would confirm that diffuse attention at least correlates with stress.
Flow experiments – experience sampling finds that "best moments usually occur when a person's body or mind is stretched to its limits in a voluntary effort to accomplish something difficult and worthwhile." Huh, well there you go.
Combat nihilism by finding meaning in quality work eg programmers finding aesthetic satisfaction in code. Shades of Zen and the Art of Motorcycle Maintenance here, except that this book isn't a muddy mess :P
It's as if our species has evolved into one that flourishes in depth and wallows in shallowness.
I notice that everything in this chapter seems obvious to me, and yet I still behave as if internet time is a reward for the punishment of hard work, even though I know I generally enjoy the latter more. How did this idea worm it's way into my head?
Willpower is a depletable resource – can't rely on it to avoid distraction. Instead cultivate habits / rituals / routines around deep work and design your environment to remove distraction.
1) Decide on your depth philosophy: * Monastic – completely remove shallow work from your life eg Knuth. * Bimodal – carve out long periods of deep work eg a professor might be available during term-time but disappear in the summer. * Rhythmic – carve out regular periods in your daily routine eg the first three hours of each morning, before everyone else arrives in the office. * Journalistic – given enough discipline and motivation, simply engage in deep work whenever free time presents itself.
2) Ritualize. Plan out the structure of your deep work periods – where you will work, for how long, how you will work, what you will work on, how to remove distracting obstacles ahead of time.
3) Make grand gestures. Can be big, like buying a round-the-world flight to force yourself to disconnect. Can be small, like buying a fancy notebook to plan your schedule, making it seem more important.
4) Don't work alone. Collaborative work in a good environment can be deep, and a hub-and-spoke -style office can support serendipity without constant distraction.
5) Execute like a business. Use the 4DX framework:
6) Be lazy. Downtime appears to be important for both creativity and concentration. Good downtime is playing with your kids, taking a walk in nature, going for a run. Bad downtime is browsing facebook etc. Not clear what the dividing line is – perhaps something like mindfulness/presentness vs compulsive consumption.
Create a shutdown ritual. The authors is: check email one last time, dump brain onto todo list, skim calendar for important deadlines, plan the next day, announce out loud "shutdown complete". After the shutdown, NO WORKING! Not even checking email.
Focus is trainable, and conversely distraction is an addiction. If you pull out your phone every time you have a spare minute, you are feeding the addiction and missing the chance to practice focus.
Constant stimulus/distraction causes long-term changes in the brain that prohibit the ability to focus deeply.
Practice resisting distraction. Divide the day into focused and distracted periods, and enforce that separation. Even if your job requires eg regular email responses, you can still divide the day into 20 minutes offline and 20 minutes online. The important part seems to be installing a check between feeling the urge of distraction and acting on it – so that instead of just finding yourself mysteriously on facebook you first have to consciously decide if it is currently allowed.
It's not shallow interaction itself that is dangerous, it's the constant switching back and forth. Need to learn to tolerate an absence of novelty.
I once spent a week in Budapest with a good friend where I forgot to bring my phone charger. As a result I only turned my phone on when we were totally lost and needed the map. I was surprised by the end of the week how much that affected my experience. I felt strangely calm and relaxed.
"Interval training for the brain". Pick a deep task and give yourself a challenging deadline to complete it. Provides an immediate motivation to avoid distraction.
A few months ago I decided to dedicate a week to reading. I set myself what I thought was a steep challenge – to read and publish notes on five books. I ended up finishing nine, whilst only spending 6-7 hours per day on the task. The fact that I was able to focus for that long was down to an unusual environment and motivation, but it did make me wonder what I could do to replicate that in general.
Since then, I've toyed with the practice of setting extreme deadlines on small programming projects eg one hour. This has two interesting effects. The first is that it drives one towards just solving the core of the problem and pushes out any busywork that doesn't contribute to the solution. The second is that it really highlights and inefficiencies or flow-breaking in the tools. Normally a 3-minute compile is just a chance to check my email, but when I only have 60 minutes to begin with it's suddenly a massive imposition.
Meditate productively – during time that is otherwise physically occupied (eg walking, commuting) set yourself a mental task to attack. Like mindfulness meditation, notice when you have drifted away from the task and gently pull yourself back. Avoid looping around the same thoughts by noticing and banning the loop. Structure your process to avoid stalls eg begin with a review of the relevant variables, list the next step questions and then attack each question one by one.
Memory training – appears to actually train concentration as much as memory. Any process which requires unwavering concentration might have the same effect. In a similar vein, I've been using rail balancing to practice mindfulness. It provides a very clear signal – if my mind wanders, I fall off.
Social media is explicitly designed to be addictive. The metric that companies like Facebook and Youtube optimize for is time spent on the site. See Is Anything Worth Maximizing?_
But part of what makes social media insidious is that the companies that profit from your attention have succeeded with a masterful marketing coup: convincing our culture that if you don't use their products you might miss out.
The any-benefit approach to network tool selection: You're justified in using a network tool if you can identify any possible benefit to its use, or anything you might possible miss out on if you don't use it.
The craftsman approach to toll selection: Identify the core factors that determine success and happiness in your professional and personal life. Adopt a tool only if its positive impacts on those factors substantially outweigh its negative impacts.
In other words, consider carefully the opportunity cost, especially on your ability to focus.
Note that just like Decisive it encourages explicitly choosing your values and then using them to weigh choices about how you spend your time.
The core factors should be non-completable, but still specific enough to be actionable. 'Do better research' is too vague. 'Finish paper for X conference' is too completable. 'Regularly read and understand cutting-edge results in my field' is good.
Can be hard to tell what impact social media has on your life, so determine it experimentally – go cold turkey for one month and keep track of the answers to these two questions:
Don't use the internet to entertain yourself (at least, don't use it unboundedly). Plan your free time in advance to ensure you make good use of it, instead of falling into low-value time sinks like social media.
"The great and profound mistake which my typical man makes in regard to his day", he elaborates, is that even though he doesn't particularly enjoy his work (seeing it as something to "get through"), "he persists in looking upon those hours from ten to six as 'the day', to which the ten hours preceding them and the six hours following them are nothing but a prologue and epilogue."
This is a perfect opportunity to plug Microadventures. Most people think they don't have time for interesting adventures because they are busy working 9-5. Alastair turns that around and points out that you have all of 5-9 to do something memorable.
Deep work is tiring. You may only be able to manage four hours per day. But shallow work is surprisingly pervasive and, combined with task residue, can easily crowd out those four hours.
Put a notebook on your desk with an hour-by-hour schedule. Fill the whole schedule at the start of the day, and adjust it as needed during the day. You don't have to rigidly stick to the plan eg if inspiration strikes feel free to spend the rest of the day mining it, other plans be damnded. The idea here seems to be a combination of intentionality about your time and creating a more explicit awareness of where your time goes. I like the idea of planning the whole day in advance more than the short-term planning I have built into monolog at the moment.
Quantify depth of activities by asking how many months would it take to train a smart college grad to do this job. Then bias your time towards activities where you provide more value than a smart college grad! This is intended as a thought exercise but, being self-employed, paying a smart college grad to take over some of my work is actually an option.
Ask your boss what percentage of your time should be spent on shallow work, and then use this to fight back against shallow demands on your time. Your boss is unlikely to commit out loud to the idea that, say, 80% of your time should be spent on shallow work (if they do, start looking for another job). This is genius – weaponizing the consistency effect.
Fixed-schedule productivity – rather than working until the work is done, pick a schedule and make the work fit into the time available. Like setting a deadline for individual projects, this has the effect of squeezing out non-essential activities and motivating focus throughout the day. Also motivates guarding your time more preciously – treating it as a highly limited resource rather than saying yes to every request that comes to you.
Become hard to reach:
These suggestions probably make more sense if you are a famous author and professor with a huge inbox. Personally, I've found the opposite effect – that I get a lot of value out of explicitly encouraging people to contact me. I do often ignore thoughtless emails though eg I regularly get inquires about web dev projects, which clearly indicates that the sender didn't so much as glance at my website or portfolio before contacting me.
I think the most valuable thing I got from this was having the inconsistency pointed out between my experience and my actions – I know that deep work is what I find satisfying, but I motivate myself as if random internet browsing is a reward for the punishment of work.
I used to make a daily schedule as the book suggests. It's a habit that got lost when I spent a few years in a regular job and didn't have the power to reject interruptions. Monolog currently has a task tracker that requires to state what I am working on and for how long, and then nags me if I go over time. I think that planning the whole day in advance is even more valuable, because it highlights the opportunity costs in spending too much time on less valuable activities.
A daily plan is also the most actionable idea here. It would be very easy for me to agree that all of these things are good ideas, but not get around to making them into habits. Planning the whole day in advance provides a perfect nudge to consider including all these other ideas.
Explicitly writing down personal values and then mapping them to activities is a suggestion I've seen in a few places now. I have some cute ideas about software that encourages managing time well according to your stated values, and makes it clear if some value is being neglected, but for now I'm just going to use paper and a one hour timer.
Deadline tasks are something I've already been doing occasionally, but casting it as 'interval training for the brain' makes me even more bullish on the idea.
The concepts of fixed-schedule productivity and shutdown rituals are well-timed, because I've just started consulting again and in the past I always struggled with switching off from work.
I want to set up a proper office but I'm currently constrained by living situation. I'm looking at local coworking spaces but I don't think it's likely that any of them will have private rooms.
Speaking of rituals, I want to buy a thinking hat. The rule will be that I can answer email or chat online or whatever I want, but first I have to take off the thinking hat. I don't get to pretend that this is still included in deep work time. I've found little speedbumps like this, that place conscious action between impulse and gratification, are really valuable for creating awareness of my own behavior.
"I'll live the focused life, because it's the best kind there is."
This book is written in a very motivational-anecdote-you-too-can-change-your-life style which immediately rings alarm bells for me. But it's on CFAR's reading list so I stuck with it and ended up reading several other books by the same authors on how to change behavior and on how to craft sticky ideas. Those other books explain the writing style here – it's carefully structured to be persuasive, memorable and actionable.
That style of writing makes sense from a paternalistic point of view – the authors are genuinely trying to convey sound advice in a way that will maximize it's impact. It's just not very convincing for a reader who is starting from a position of active doubt. In all three books they often criticize some existing wisdom as completely flawed and give their own replacement instead, but only justify it with a single anecdote or experiment.
The books are clearly carefully researched and built on long experience in the field, and choosing not to drown the reader in the fine details of that research makes for a more effective educational tool. But it's frustrating for a reader who is trying to be a little more epistemically careful than average.
Typical talk about bias, overconfidence and introspection. Rather than an exhaustive list of biases though, they focus on four major villains of decision making:
The classic 'make a list of pros and cons' approach does not address any of these villains.
The book instead presents the WRAP process (which has a handy poster you can put on your wall):
I didn't realize on first reading, but the mapping between the villains and the process is a little forced.
The process is aimed at decisions where you have time to think, gather evidence, consider alternatives etc. Decisions that occur in a matter of seconds or minutes are the domain of intuition, and that's a totally different story.
You are still human, so this process won't magically prevent you from blundering. But small improvements can make a big difference, especially in competitive fields where the difference between average and world-class might be a 25% success vs 35% success. The authors example is baseball batting, which is a weird choice coming right out of the explaining how such short-term calls are not the subject of this book. A better example might be poker, where even world-class players still lose the majority of hands. They just win slightly more of the hands that matter.
Finally, the fifth villain is failing to start the decision process, or even to notice that there is a decision to be made. They introduce 'tripwires' – pre-determined signals that kick you out of autopilot and into the decision-making process eg a threshold weight which, when crossed, triggers you to rein in your diet. A common example is people staying in jobs they hate because there is never a single point where they are forced to decide whether to leave. After reading this book I started using snoozed emails to send decision points in the future eg in three months I'll receive an email from myself asking if I'm still happy about my move to Berlin, and prompting myself to apply the WRAP process to deciding if I need to change anything.
Look out for "whether or not…" decisions – there are never only two options.
Consider opportunity costs. Instead of "do X or not do X" think "do X or (not do X and do something else with those resources)". Instead of "should I buy the fancy $1000 stereo or the basic $700 stereo" think "should I buy the fancy $1000 stereo or the basic $700 stereo plus $300 of albums". Econs think this way by default, but humans benefit from a nudge.
You will never encounter a car salesman who says, "Hey, why not buy the entry-level model and use the savings to take your family on vacation?"
Imagine the 'vanishing options genie' appears in a puff or smoke, and declares that if you pick any of the options you are currently considering he will make your head explode. Are you sure there are no other options? Anecdotally, this is hilariously effective, and it would have been worth reading the whole book just for this idea.
I'm mildly surprised by how effectively people can be nudged out of narrow framing. Most cognitive biases are very difficult to overcome, but this one seems to hardly put up a fight at all. I wonder if there is some fundamental reason for this.
Multitrack – explore multiple options in parallel. "This AND that" rather than "this OR that".
What's the right number of options? Too many creates choice overload / paralysis by analysis. But 2 is certainly much better than 1, and 3 is a nice round number.
Options need to be meaningfully distinct, not just tweaks (the vanishing options genie can help here), and they need to be real competitors rather than just strawmen opposition to the favourite.
As a personal example of multitracking, I'm currently paying the bills with consulting, working on my own research projects and applying to part-time grad school programs. A few months ago, I was thinking of those as exclusive options and trying to decide which one to commit years of my life to, without taking time to explore each.
The classic case would be to confront the policymaker with the choices of nuclear war, present policy, or surrender.
Another cause of narrow framing is being locked into a single mindset – 'prevention focus' or 'promotion focus'. Leads to focusing on avoid cons or pursuing pros, respectively. Need to actively shift between both when generating options eg in response to recession, some companies reacted defensively by cutting costs and reducing risks, others reacted offensively by betting big and investing in new markets, but the most successful companies where those that did both. There isn't a clear suggestion on how to do this, but I imagine that just dividing options into prevention vs promotion would make it clear whether or not you have neglected one side.
Looks for bright spots – find places, people or practices that are successful and figure out how to replicate them eg rather than "how do we fix malnutrition in poor villages" think "which villages are not suffering from malnutrition and what are they doing differently". I guess the advantage here is, in addition to generating options you might not have thought of, the option already has some field testing. There's no guidance though on how to separate causation from correlation, or how to figure out if the solution is reproducible.
Playlists – list of heuristics / categories for generating options eg "can you personify the product", "is there a key color for the brand". Useful triggers for widening the search. A beautiful example of this is How to solve it, which I often turn to when I'm stumped by an analytic problem.
Ladder up – look for solutions to similar problems, starting with those that are near-identical and progressively abstracting more. For example, trying to solve waiting times in school cafeteria – are there situations when the waiting times are lower, how do other school cafeterias handle lines, how do other businesses handle lines, how are large crowds managed in general, how are other flow control problems handled etc.
Seek out disagreement, especially for high-risk decisions – eg create a murder board.
For each option, consider "what would have to be true for this option to be the correct answer" or "what evidence would persuade me to change my mind". Refocuses the conversation from competing sides to facts that can be researched, and primes people to notice when the evidence in question pops up. Allows people to disagree with out being disagreeable. Reminds me for some reason of the use of bots by the Rust team to refocus the interaction from 'me vs you' to 'us vs the bot'. Rather than arguing the merits of the code, you are now working together to see if this objective bar can be met.
Ask disconfirming questions eg rather than "do you get lots of holiday time" ask "how many holiday days have you taken in the last year, where did you go, how much notice did you give, were any holidays canceled on you" etc. Page 923 of On Being a Happy, Healthy, and Ethical Member of an Unhappy, Unhealthy, and Unethical Profession has many great examples of such questions eg "who were the last three associates to leave the firm, what are they doing now, how can I contact them".
Ask questions that have concrete, factual answers and do not rely on a shared reference frame (eg how much autonomy is a lot?). And decide in advance what answers you want to hear, so you can't later talk yourself into thinking it isn't that bad. Also ask broad, open-ended questions to uncover problems you might not have predicted eg "if there was one thing you could change about this company, what would it be".
Consider the opposite. List key assumptions / beliefs and find low-risk ways to test them.
Consider both the inside view and outside view. Outside view provides base rates, which then anchor your assessment of the inside evidence eg you might naively put your chances of business success at 50%, but once you find out that 99% of similar startups fail, you may be forced to realise that you don't have sufficient evidence about your own uniqueness to update that base rate so far.
A simple way to obtain an outside view is to find someone with more experience than you and ask them for a base rate. Beware that asking for a specific prediction about your case might trigger the same inside view distortion that you are trying to avoid. Good questions: "what are the important variables in a case like this", "what kind of evidence can tip the verdict", "what percentage of cases like this get settled before trial". Bad questions: "do you think I can win this case", "what would you do if you were me".
Superforecasting goes into a lot more detail on how to balance inside and outside views.
Break out of filter bubbles. Seek out multiple independent points of view and don't contaminate them by sharing data between them. See how many of those views turn up the same points.
Sample texture. Eg rather than only looking at percentage of good reviews, read a few random reviews to see if the pros and cons apply to you. Eg rather than only looking at defect rates in factory, visit some of the workers and see how the defects happen.
(The section from inside/outside view to here is summarized as 'zooming in and out'.)
Ooch – find cheap, fast ways to test theories and get feedback. For some reason I have violent aesthetic objections to this word. Is there an alternative? Eg before committing years to studying for a particular career, find a way to intern or shadow someone for a few weeks. Eg hire several candidates for trial periods rather than relying on interviews to pick one.
Counters overconfidence (refers to Tetlock's work on prediction).
Also works as a way to overcome fear – can inch your way into scary decisions, at each step finding that your fears are not confirmed.
Also good for dealing with disagreement. If you are convinced someone is wrong, let them try an experiment and agree beforehand on the criteria for success. One you will be surprised. And it may even take less time and energy than arguing to consensus.
Doesn't work for decisions that require commitment though eg a three week internship can't tell you if you have the grit for a 7 year medical degree.
Short-term emotions lead to short-term thinking. Emotion is what assigns values to outcomes, but we overweight short-term emotions compared to long-term emotions eg being too scared to ask someone out.
10/10/10 – how will you feel about this decision ten minutes from now? How about ten months? Ten years? Eg asking someone out – in ten minutes might be happy or embarrassed, in ten years might be married or might have totally forgotten about the whole thing. Explicitly considering different time-frames helps correctly weigh the impact of short-term emotions. The main value here seems to be a good heuristic for dealing with fear – it's easily to otherwise systematically fail to take options which gamble short-term downsides against long-term upsides. Simply ignoring fear does not distinguish situations where the fear is sensible.
Another similar idea I learned at Jane St is to collect similar decisions into a category and think about the whole collection at once. So the decision is not based on 'what will happen if I take this leap' but 'what will my life be like if I habitually make these kinds of leaps' (and when it comes to parkour these are decisions about literal leaps!). It's similar to the idea of bundling habits, so instead of thinking 'should I sleep in today' you think 'should I habitually sleep in for the rest of my life', which makes the correct decision somewhat clearer.
Yet another similar idea I read somewhere recently is to explicitly imagine the worst case outcome and then figure out how you would respond. For example, when lead climbing I might look at a particular section and feel afraid, but then realize that the worst thing that could realistically happen is I sprain an ankle, and if it came to that I would only be out of training for a month or two and would still be glad that I tried. It's oddly soothing.
Mere-exposure effect. Things that seem obvious or true may just be familiar. I could rant for hours about folk truths in programming. Loss aversion. status quo bias.
Useful questions to counter – "what would our successors do?", "what would I advise my best friend to do in this situation". Attains distance by simulating a person who does not have our emotional attachments to the status quo?
Figure out your core priorities and how to weight them. Don't be distracted by pros/cons that aren't priorities. Agonizing over decisions is often a sign of unclear priorities.
In organizations, make sure everyone knows what the core priorities are. Company value statements are useful to the extent that they prioritize values and aid decisions about tradeoffs. "Courage, integrity and love" does not help anyone make decisions. "First, do no harm", on the other hand, is crystal clear about priorities (you could argue about whether it is the correct priority, but that's a different matter).
Seems obvious, but most people have not explicitly thought about their core priorities and are instead driven by day-to-day nudges.
If forensic analysts confiscated your calendar, email records and browsing history for the past six months, what would they conclude are your core priorities?
Can't make time for everything, so also make a 'stop doing' list, containing everything you do that is dominated by opportunity costs. Figure out how to remove those things from your life, to make room for the core priorities.
Suggests setting a one hour timer and when it goes off, ask if what you are doing right now is what you most need/want to be doing. Comparing this to eg Pomodoro reminds me of productivity vs effectivity – the difference between doing more things and doing the right things.
Bookend the future – in addition to point estimates, also explicitly consider worst- and best-case scenarios. Each estimate taps different pool of knowledge / different kinds of simulation.
Prospective hindsight – imagine the event in consideration has already happened, and weave a story about how/why. Better at generating stories about concrete scenarios than we are at pulling probablilities out of the air.
Premortem – "imagine we are X months into the future and this plan has failed horribly – explain why" – helpful counter to the planning fallacy, generates risks that can now be warded off.
Preparade – similar practice – are you prepared to deal with unexpected levels of success. Can you get enough parts supplied? Can you ship that many units? Can prevent unexpected success from overrunning you and becoming failure.
Safety factors – premorterm and prepared help with forseeable problems – safety factors guard against unforseeable problems.
Vaccinate against disappointment eg 'realistic job interviews' in call centres that are up-front about the level of stress and abuse lead to higher retention.
Mental simulation – rehearse possible scenarios and your responses in your head eg different ways a difficult conversation might go.
No-one ever sits down and decides to live their life without ever traveling. But often the decision to travel right now simply never happens.
Tripwires are forced decision points to kick you out of autopilot eg Zappos offered employees $1000 to quit – which forces them to stop and consider their future right now rather than putting it off indefinitely. (Also provides psychological benefits of committing to the decision rather than being in state of wavering forever.)
I'm trying to figure out how you would set a tripwire for traveling. Perhaps something like "if I haven't made the trip three years from now, I immediately quit my job and do it, no matter what".
When a decision is made based on current evidence, set a tripwire to trigger reexamining the evidence at some fixed point in the future. Eg Kodak stuck with print because customers weren't happy with the quality of digital pictures – they should have setup a yearly survey with a trigger such as "if the survey indicates that 10% of customers are happy with digital, we re-examine the space".
Deadlines are also a kind of tripwire, forcing you to commit to some action rather than eternally putting it off.
Similarly, employee reviews are a kind of tripwire on self-examination. If you get a terrible review, it's time to look at your work and see if there is anything you can do to improve.
Partitions eg dividing food into small, sealed portions so that opening each portion acts as a tripwire nudging you to think about whether to keep eating. If a bucket of popcorn came as 20 individual packets, you would never eat all 20. Investment rounds, personal budgets etc.
Tripwires can help make safe spaces for risky experiments eg "I'll launch my own business, but I won't invest more than $10k until I have my first paying customer". Capping your risk allows you to stop worrying about it and focus on the experiment itself.
Can give tripwires to other people to prime them (eg "if you see someone using our product in a way we might not have anticipated, come tell us about it") or give make them feel safe expressing intuition (eg "if a nurse is worried about a patient, they SHOULD call the rapid-response team"). These don't seem to quite match the previous definition of a tripwire. Is there a better name?
(I've skipped the chapter on 'trusting the process', recommended reading, case studies for practice, example of common obstacles and ways to overcome them and a ton of end notes.)
I'm not sure how to evaluate things like this. The advice all seems self-evident. Unfortunately, most advice does, even contradictory advice. There is probably a lot of evidence and experience behind the advice, but it's not presented here so I don't know which parts are well-supported and which parts are extrapolation or guesswork.
A lot of the advice is low risk/commitment though, so I can at least pick out things that I a) want to try or that I have found useful in the past:
I've also been thinking a lot about autopilot and nudges vs core priorities, especially after watching Designing For Agency. The one hour alarm suggestion is a start, but I want to figure out how to ensure the success of priorities which require planning and preparation, or which don't have a regular rhythm to them. It's easy to figure out what kind of system might lead to spending more time reading books and less time reading twitter. It's much harder to figure out what kind of system might lead to spending more trying new activities, because it's the kind of thing that happens irregularly and requires some kind of premeditation to pick an activity and figure out how to arrange it.
I dunno. Research continues…]]>
The core idea of the book is captured in a few early quotes: 'Almost everything is noise', 'Only a few things really matter', 'What are the trade-offs?'. It's about ruthlessly focusing only on the things that actually matter to you, and being painfully aware of the opportunity costs of low value activities and constant distraction.
A quick chapter-by-chapter summary:
Choose. Decision fatigue and learned helplessness prevent people from realizing that they even have choices. Schedule time to step back and reflect.
Discern. Push only on the levers that matter most. Don't keep pushing harder on the levers that are in front of you in the hope that things will get better if you work hard enough. Be willing to lose instead of taking on every challenge that is given to you.
Trade-off. Pretty much every choice is a trade-off. You need to have to have clear priorities to be able to choose between trade-offs. Trying to win at everything doesn't work.
Escape. Need space to think to even realize that decisions exist.
Look. Don't give in to information overload. Look for signal. Don't feel like you have to absorb everything. Make goals/questions/problems explicit to avoid paralysis by analysis.
Play. Important for creativity.
Sleep. Can't trade sleep for productivity – simply doesn't work. Sleep deprivation is invisible to suffers – they believe they are at peak performance. See sleep as an investment in productivity instead.
Select. 'Fuck-yes or no'. Don't choose options because they are good, choose them because they are the best use of your limited time. Focus on comparative advantage – not where you can contribute but where you can do better than anyone else.
Clarify. 'Essential intent' – goals that are both concrete and inspirational eg "get everyone in the UK online by 2012". Needs to be testable and completable so that you know when you are done.
Dare. Find ways to say 'no' gracefully. Better to say 'no' than under-commit. Don't be driven by fear of missing out.
Uncommit. Cut losses to make room for success in other areas. Combat sunk cost fallacy, endowment effect, status quo bias etc by reframing eg "if I didn't already own this, how much would I pay for it?".
Edit. Subtraction and perfection etc.
Limit. If you don't prioritize your life, someone else will. Set boundaries, clearly communicate them and stick to them.
Buffer. Reserve time/money/resources to account for unforeseen problems. A system at max capacity is fragile. Explicitly consider worst-case eg "imagine we are one month in the future and this project has failed – what happened?".
Subtract. Don't fix symptoms – figure out what the essential problem is. Push the biggest lever.
Progress. Don't try to do everything all at once. Create small wins to build momentum.
Flow. Design routines and triggers that lead to the behavior you want. Fix one routine at a time.
Focus. Multi-tasking vs multi-focusing – can do multiple things but can only apply conscious attention to one at a time. Context-switching is devastating to performance. Figure out what is the most important thing right now, and dump everything else out of your memory (eg onto a notepad). Regularly pause to just be present/mindful and then re-prioritize.
Be. Don't just pay lip service to these ideas. Apply them to your entire life.
For a book about essentialism, it has an awful lot of filler. All the best advice on changing behavior hammers on the importance of focusing on a few, specific, high-impact, actionable items rather than drowning people in a tsunami of information – advice that this book itself repeats but does not follow. So here is my alternate, condensed advice:
Schedule regular periods of time to think, whether that's a day every month to think about your life or ten minutes every hour to plan your work. Put it in your calender, use a pomodoro timer, whatever. During those periods:
That's it. Being explicit about goals and making time to reflect and correct course is 90% of this book. The rest is noise. Not worthless, but not worthwhile enough to spend time reading.]]>
I didn't take detailed notes when reading this book, because I didn't find much content.
We want advice on how to live our lives. This is a subject that can be informed but not decided by empiricism. It would seem to be the domain of philosophy and ethics, but the author scores a marvelous own-goal by choosing to use the term spirituality and then spending much of the rest of the book defending against the unfortunate connotations of the term.
The premise is reasonable enough. Most religions are not a great source of learning on this subject, because even once the ludicrous metaphysics are stripped away the remaining message is, at best, mixed. The Bible, for example, talks a great deal about unconditional love and forgiveness, but also has a great deal to say about stoning people to death. Buddhism, however, is unusual in that the underlying skeleton has a good deal of wisdom that has been validated by modern science and so it's worth exploring even if you choose to ignore the more dubious claims.
There follows a rambling course of history, philosophy, neuro-science and personal anecdotes. I found most of it fairly uninteresting, but there were a few sections that at least led to interesting thoughts:
In patients whose corpus callosum whose is severed, there is somewhat convincing evidence that both hemispheres of the brain are independently conscious. Even in a healthy brain, the bandwidth of the corpus callosum is sufficiently low relative to the rest of the brain that there is a measurable difference in performance when responding to stimuli presented to the left or right eye, depending on which side of the brain is needed to handle the response. This raises some interesting questions about whether it makes sense to think of a person as having a single cohesive mind. For example, if we reconnected a split brain, how would each hemisphere experience the transition into a new joint consciousness?
One of the difficulties of teaching meditation, mindfulness and similar practices is that there is no good way to know when you get there. Physical and simple mental skills can be easily demonstrated. Even very abstract mental skills such as high-level mathematics can be effectively transmitted by continuous application to problems and correction of the results. But skills which revolve around internal experience run up against fundamental problems around the nature of consciousness. The author uses this to argue that you ought to study under a guru who can correct your experience, without really explaining how this solves the problem at all. I think it would be much more interesting to explore whether recent results in neuro-imaging and neuro-feedback might lead to much more effective ways of teaching such skills.
The author discusses the use of various drugs to induce specific experiences. A student practicing Metta meditation might have no basis for describing how strong their experience is, or whether it's the desired experience at all, and their instructor can't do much to help beyond repeating exercises and descriptions. On the other hand, MDMA is a very reliable way to induce the desired state, and thereafter both the student and instructor have a common yardstick for comparing their experiences. Refreshingly, this section of the book is also one of the most balanced arguments I've seen on the subject, and is quite frank about both the potential benefits and the clear risks of various drugs.
Overall, I don't find this book very valuable or interesting. It tries to be too many different things and doesn't do a good job at any of them. Instead, I would recommend:
Feeling Good is a guide to Cognitive Behavioral Therapy, which makes for a much better 'scientific Buddhism' than this tangled mess. Rather than focusing on the achievement of some particular inner experience, it concerns itself with simple, practical exercises for avoiding specific undesirable patterns of thought.
Stumbling on Happiness is a more strategic counterpart to Feeling Good's tactical approach, focusing on why people are bad at noticing and predicting what makes them happy, and how you can correct for these systematic mistakes.
Trying Not To Try is a delightful tour of early Eastern philosophy which attempts to draw parallels between the differences in Western and Eastern philosophy and modern understandings of cognitive science. It somewhat fails to actually reach a conclusion, but it's worth reading for the history alone.
Finally, Alan Watts has a wealth of free recordings discussing very similar ideas but in a more pragmatic setting and with a great deal more charm and wit.]]>
Authors research investigates why some people are systematically less prone to failures of rationality.
Bush's IQ estimated to be 120, but even his supporters concede that there is something subpar about many aspects of his thinking. Only confusing because we conflate intelligence and IQ, as if IQ captured all cognitive strengths/weakenesses.
Bush shows poor rationality. Now know about about rationality to begin to conceive of a Rationality Quotient along the same vein as IQ. Important to be able to nail it down so we can talk about is usefully.
Many examples of smart people being excruciatingly dumb. Only seems strange because we are confused about what we mean by smart/dumb in the first place. Motte-and-bailey between IQ and general cognitive effectiveness.
Cattell/Horn/Carrol theory of intelligence is sort of consensus view. Performance across wide range of mental tasks relies on only a few attributes, of which two are dominant.
Gf – fluid intelligence – abstract reasoning, especially in novel domains – intelligence-as-process.
Gc – crystallized intelligence – accumulated declarative knowledge – intelligence-as-knowledge.
Rephrase as 'smart but acting foolishly'. Intelligent but irrational.
Rational – caricatured by Spock. Modern cogsci has more precise definition.
Instrumental rationality – behaving in a way that maximizes your values given your resources. ie winning
Epistemic rationality – how well beliefs map to the structure of the world. Seems to need something about correctly updating on evidence, otherwise you can score well by just having no beliefs.
Dysrationalia as an intuition pump – given that prevailing view is that all mental abilities should correlate highly with IQ, and that failing to do so is labeled a disorder, describe 'smart but acting foolishly' as a similar disorder. Author doesn't actually back this view, just uses it as a way to lead people into thinking of intelligence and rationality as separate, because people keep forgetting that IQ does not encompass all mental ability.
Not claiming that IQ does not measure something important/useful. Just that it doesn't measure everything.
Main strength of Type 2 processing is it's ability to create simulations. So an important capability is the ability to keep those simulations apart from reality – not confusing what is and what might be. "It is the mark of an educated mind to be able to entertain a thought without accepting it."
Representational abuse – "this banana is a phone" – how do we separate such consensual fantasies from reality?
Cognitive decoupling – creating copies of representations and holding them separate. Metabolically expensive, but just as well otherwise we might get lost in simulation. Certainly people who struggle to pay attention instead of daydreaming.
Primary representation has high priority so eg close eyes to free up resources for simulation.
We only bother to measure capabilities which have high variance between people eg face recognition is pretty much same for almost everyone.
Large part of what IQ measures is likely cognitive decoupling eg IQ correlates highly executive function and working memory.
Existing distinction between optimal/maximal performance situation (narrow task, participant instructed to maximize some known measure) and typical performance situation (open-ended, no explicit measure given). IQ tests are measured under maximal performance settings.
Another existing distinction – cognitive abilities vs cognitive styles / thinking dispositions. Former is largely measured by IQ tests. Latter is broadly scattered but generally assesses differences in how people form+update beliefs and decompose+prioritize goals. IQ shows low-to-zero correlation on performance on various thinking disposition tests.
While IQ is more-is-better, thinking dispositions tend to have a sweet spot eg don't want to be too reflective or will never actually act. However, in modern environment most people are way below the sweet spot.
Many typical-performance tests show performance that has strong correlation with certain thinking dispositions even when controlling for IQ ie certain thinking dispositions are not explained away by IQ, and have value in addition to IQ. Eg representative sample of US citizens show negative correlation between scores on rationality test and number of arrests, evictions, bounced checks etc even after controlling for cognitive abilities.
Tripartite model: autonomic mind, cognitive mind, reflective mind. Autonomic maps to System 1. Cognitive maps to Cf. Reflective maps to rationality.
Reflective mind initiates inhibition. Cognitive mind sustains it. No evidence for this yet. Maybe later?
Reflective mind initiates decoupling. Cognitive mind sustains it. Again, no evidence yet.
IQ tests measure the ability to inhibit or decouple but not the tendency to inhibit or decouple. Expensive operations that need to be kicked in at critical moments.
Autonomic mindware – Encapsulated Knowledge Bases, Tightly Compiled Learned Information. These don't appear to be standard terms, but are not explained here.
Cognitive mindware – strategies and production systems. eg long division?
Reflective mindware – beliefs, goals and general knowledge. Includes Gc, but the broad sampling used in Gc tests means that specific important concepts are not measured (eg statistical thinking) so Gc only very loosely correlated with rationality.
Discusses strategies for downsizing focus on intelligence to make space for rationality. Skipping much of this – interesting but repetitive.
Flynn effect – IQ increasing over time. Flynn confused as to why no increase in intelligence eg pace of invention. Again, caused by confusion between IQ and broad-view intelligence.
Quotes from early scientists arguing that low IQ individuals barely human eg can't really feel pain. Tendency to conflate IQ scores with personal worth.
Folk psychology already has some support for distinction eg survey of students shows default confation between the two, but prompting about difference produce 'smart but foolish' -type distinctions.
Overall argument is that there is a motte-and-bailey going on with definitions of intelligence. Wants to concede the IQ motte and carve RQ out of the bailey.
Why irrational? Cognitive misers. Mindware gap.
Cognitive misers – default to low metabolic cost heuristics to an extent that is not adaptive in current environment.
Evolutionary pressures: fast reaction > accurate reaction, false positive > false negative (for threats), reproductive fitness > goals or happiness. Also just a pile of kludges.
Mindware gap – cultural progress in effective cognitive tools but many of these tools not widely possessed (or even valued). Eg college level stats would make one wildly rich in 18th century Europe. Similarly for modern finance/economics.
Contaminated mindware – some mindware is actively harmful.
Hard to decide exactly when someone is being perfectly rational, but easy to measure when they are clearly wrong, so research tends to focus on classifying errors. Argues that education might be better off focusing on fixing known-bad thinking than on trying to define+measure what should be learned.
Fully disjunctive reasoning – examining all possible world states. Most people capable of doing such reasoning but don't do so unless prompted, leading to mistakes. IQ only weakly correlated with spontaneously carrying out disjunctive reasoning.
Attribute substitution – subconsciously replace assessment of attribute, with assessment of some correlated attribute that is easier to assess. Reasonable heuristic, but can lead to clearly wrong decisions eg scope insensitivity or violating dominance principle or basic laws of probability.
Substitute probability for vividness, salience, accessibility.
Cognitive misers are vulnerable to exploitation. Not enough for heuristics to be right 98% of the time when the remaining 2% are being actively exploited.
'Laundry of life' – most decisions have little effect compared to small number of major decisions that change your life. Not totally sold on this – little, repeated decisions such as eating healthily or studying every day also add up to huge impacts. Non-laundry decisions tend to be unique/unfamiliar which means that learned heuristics are more likely to fail.
Status quo bias / default heuristic.
Need to recognize when the decision environment is hostile to heuristics eg if available information/cues are being filtered your heuristics can be heavily influenced.
Strength of framing, priming, anchoring effects is a threat to personal autonomy. Same free will problem I felt was ignored in Nudge. I really like this way of framing dysrationalia as loss of free will – vivid scenario + loss aversion. Cognitive misers allow others to focus their attention.
Eg of framing – tax deductions have high approval, but tax penalty for compliment group would not pass, even though this is the same thing. Anecdotally, I related this idea to some family members and was surprised when they insisted that the two are not the same! Seemed to be a matter of mental accounting – if the taxes deductions are 'paid for' by eg tax raises on large companies it feels like there is no affect on the compliment group.
Equality heuristic. Eg subjects argue for equally dividing profits between partners in law firm. Separate groups argues for equally dividing expenses. But equal expenses implies unequal profits, and vice-versa. Framing to appeal for fairness has reversed political/enconomic views.
Kahneman – "the basic principle of framing is the passive acceptance of the formula given." Subjects presented with both framings tend to realize the equivalence and aim for consistency, suggesting that the habit of generating framings may help counter the bias. Note Tetlocks foxes often considered multiple different framings of the same question at once – this seems like a very important habit.
Consider the example of credit card companies lobbying for difference in card vs cash prices to be framed as discount rather than surcharge – intensely aware of how that framing would affect their success.
Politics is largely a fight to choose the framing eg tax relief – framing where we are relieving suffering.
For between subjects experiments, no correlation with IQ. For within-subjects experiments, small correlation with IQ.
Major point of the book – intelligent people perform better only when you tell them what to do (in the domain of rational decision making). High IQ people are more able to make use of particular cognitive tools when cued, but no more likely to spontaneously self-cue. This is really interesting from the point of view of making people more rational – could we make tools for specific domains that cue the user at the appropriate time?
Myside bias – evaluate evidence, generate evidence, and test hypotheses in a manner biased toward own prior opinions and attitudes. Appears to encompass confirmation bias? Cognitively demanding to switch perspectives. ie to compute 'if the world were this way, what would I expect to see'.
Overconfidence related to confirmation bias – failure to think of reasons one might be wrong.
Wonder how this relates to depression – where self-evaluations are usually negatively distorted. Do depressed people suffer from eg planning fallacy?
Also see biased assessment of bias – subjects uniformly rated themselves as less biased than their peers! Explains the phenomenon I notice where people will be well aware of literature on bias but will fiercely defend their own mistakes. Do we need a buddy system? Pronin suggests this is caused by being deceived by conscious introspection. Can we defeat this by imagining the same decisions in a stranger? Would be better evaluate their bias than our own? TODO try this out!
Illusion of control in traders correlates negatively with income/bonuses.
Egocentricism in processing – leads to overconfidence in how others will perceive/understand our words/actions. Reminds me of the smarties experiment – I guess theory of mind still isn't great even in adults. Often responsible for bad UI – can't imagine that the user will struggle to understand.
No correlation between myside bias and IQ.
Inability to initiate or sustain inhibition of autonomic mind.
Trolley problem as example of inhibition – most people fail to override emotional response when considering pushing someone, but are not consciously aware of this and so vigorously defend it. Leads to many convoluted rationalizations trying to establish consistent moral framework.
Can also occur when cold eg jellybean experiment – represented distributions so that low-win option has more quantity of winning beans that high-win option – >30% of subjects lured into picking the low-win option even though they are given the exact numbers.
I vaguely recall an example given in a room full of traders, where two sets of choices are given. There is no consistent utility function that would lead to choosing differently in each case, but it was still compelling. Presented as the cognitive equivalent of an optical illusions – even knowing the correct answer rationally it was not possible to stop the feeling.
Toy logic problems test ability to separate reasoning from prior belief. Such unnatural de-contextualisation increasingly important eg jury trials, customer is always right, anti-discrimination law. Last one is particularly interesting – to even have the ability to make non-discriminatory decisions have to be able to suspend autonomic responses which rely on stereotype – not a matter of choosing to discriminate so much as struggling not to.
Willpower as ability to inhibit Type 1 response/control.
Hyperbolic discounting.
Bundling rule – succumbing today = succumbing everyday – maps individual temptations into long-term effect. TODO try this.
Debates about rationality have focused on purely cognitive strategies, obscuring the possibility that the ultimate standard of rationality might be the decision to make use of superior tools.
Mindware gap – even when thinking carefully, may not have the appropriate rules/knowledge/strategies to make a good decision eg before probability theory was very hard to reason about risk.
Eg facilitated communication – hard to see flaws without exposure to scientific thinking, especially idea of using control group.
Eg convictions over multiple cot deaths – argument was missing concept of dependence between random variables.
Bayes rule. Likelihood ratio – P(data | hypothesis) / P(data | ~hypothesis). Posterior odds = prior odds * LR (where odds = P(H) / P(~H)). I haven't seen this formulation before. Can think of LR as the shift in likelihood, independent of prior odds – easier to share / pool with others.
Common failure (eg in facilitated communication case) is failure to consider P(data | ~hypothesis). In experiment where subjects where asked to pick which cards contained data needed to make diagnostic decision ~half failed to pick P(D|~H). Is this enough to explain confirmation bias? A belief update rule that is one-sided in this way might tend to confirm itself, because it can't see cases when data is better explained by ~H. Might be interesting to play around with.
Falsification eg 2-4-6 task. Genius intervention that improves performance – make two groups eg RED vs BLUE rather than a single group with pass/fail. Confirming one falsifies the other, cueing subjects to try to put triplets in both groups.
Base rate neglect.
Conjunction error.
^ all declarative knowledge. Let's talk about strategic knowledge too.
Belief identification – thinking disposition measuring whether changing beliefs to get to truth is more important than defending current beliefs.
Reflectivity/impulsivity – measure by Matching Familiar Figures Test – long delay + low error vs short delay + high error.
Suppose we found that low belief identification is correlated with higher income. Would be tempting to take this as support of the idea that low belief identification makes people more effective. But also plausible that belief identification is correlated with religion which (in the US) is correlated with low income. Going to have to be very careful about this sort of evidence when I try to figure out whether rationality is low-hanging fruit for intelligence augmentation.
Other strategic dispositions: typical intellectual engagement, need for closure, belief perseverance, confirmation bias, overconfidence, openness to experience, faith in intuition, counter-factual thinking, categorical thinking, superstitious thinking, dogmatism. These are strategic rather than declarative in that there is no correct setting – their worth is dependent on environment. That said, largely tuned for evolutionary environment.
Some correlation between rational mindware and intelligence (likely due to educational choices), but less than one would hope eg less than half of subjects with above median SAT correctly applied base rate in diagnostic example.
In most professions, people are trained in the jargon and skill necessary to understand the profession, but are not necessarily given training in making the kind of decisions that members of the profession have to make.
We expect doctor, lawyers, engineers etc to make good decisions about likelihood and risk but we don't educate them on the subject at all.
Albania imploded because more than half of the population and half the GDP was invested in Ponzi schemes. …wow._ Explains this as contaminated mindware – irrational beliefs about economics.
Similarly for the rash of child abuse cases 'revealed' through 'recovered memory'. Bad mindware was along the lines of 'patient is always right' – no search for independent confirmation of claims of eg satanic abuse.
Contaminated mindware often spread through enticing narrative. Often complex – more likely to appeal to high intelligence. Evidence given doesn't support this very strongly – single case of investment fraud, seems explainable by class segregation. But certainly intelligence doesn't seem to be a defense either.
Knowledge projects – when current beliefs are mostly true, filtering new evidence through current beliefs leads to learning faster by discarding more false information. Argument that because belief revision has large downstream effects on other beliefs it must be computationally expensive. Not worth doing unless we see evidence that is totally implausible under current belief structure. Reasonable heuristic, but when holding strong beliefs that are not true means actually faster at learning more untrue things. Hence eg holocaust deniers – isolated on 'islands of false belief'.
Folk theory – 'beliefs are like possessions' ie consciously/deliberately acquired to serve our interests. Vs memetics where beliefs are selected for virality/persistence rather than accuracy/usefulness. Question flips from how people acquire beliefs to how beliefs acquire people.
Deal breaker memes – mindware that actively sabotages mental environment to protect/isolate itself – eg faith > evidence. Prevents mixing with other memes.
Falsifiability as defense against parasitic mindware – don't trust ideas that resist evaluation.
Belief traps – believes which, if true, are too expensive to test. Be suspicious.
Regardless of lack of consensus on exact model, seems clear that only a few cognitive features underlie intelligence.
Not so clear for rationality. Proposes a taxonomy of errors.
Adds serial associative cognition to tri-partite model – Type 2 thinking which does not involve simulation/decopling. No examples?
Considers the 4-card falsification problem. No autonomic response that would be useful here, and subjects talking aloud reveal some kind of Type 2 thinking. Or maybe are just rationalizing? SAC but not engaging in simulation (eg of what would world look like in counterfactual). Focal bias – only considers the presented model. Suggests that this is another example of the cognitive miser – if Type 1 can't help, engage in cheap Type 2.
Implies that other features of reflective mind are to interrupt SAC or to refocus it.
Rough taxonomy:
Don't really understand the explanation of 'self'/egocentrism given here – seems to be roughly the tendency towards constructing a myside focal bias. Ah, explained later as tendency to construct models which support self-esteem/self-worth.
Where does the idea about belief preservation / islands of false belief fit into this taxoomy? Seems like 'failure to activate alternate hypotheses', but as a result of the cognitive miser rather than a mindware gap.
Ok, who the fuck put this table sideways?
Many of the examples in the book fall into multiple categories.
Adds a sixth category – Mr Spock Syndrome. Brain damage or mental disorders can interfere with valency judgments. Can't make decisions effectively if can't correctly attach values to them. Some weak evidence that this is present in some otherwise healthy people.
Correlation between categories and IQ as judged by fairly narrow evidence:
When ~0 used, it's because the author doesn't give exact numbers but just says eg 'virtually none' or 'very small'. TODO lookup references.
Argues that if we could increase everyones IQ overnight, little would change. Just make the wrong decisions faster. VS Hive Mind. Fight!
Lots of attention on intelligence, very little on rationality. Irrationality is clearly costly though eg estimates that more spent on pseudo-scientific medicine than scientific medicine.
Mindware seems teachable eg evidence that disjunctive can be taught and is not strongly limited by intelligence. Strategic mindware such as 'think of the opposite' is not taxing and so is likely widely learnable. Similarly, pointers in other books to evidence that reappraisal can be taught, which is more or less the same as 'consider alternative hypotheses'.
Probabilistic / statistical thinking also teachable, although less so. Somewhat counterintuitive. Similarly for scientific principles around establishing causality.
Implementation intentions – making conscious, verbal declarations of 'when X occurs, I will do Y' can place trigger in autonomous mind. Mental bundling also promising.
Strategies for affective forecasting that focus attention on the outside view eg 'ask someone else in the same situation, dont trust imagination'.
Falsifiability can help inoculate against contaminated mindware. Even concept of memetics itself is useful – being able to think about memes makes it easier to accept the idea that some of your beliefs might be parasites.
Can also alter environment to remove opportunities for failure. Nudge-style policies to prevent specific errors. Pre-commitment (eg not having tempting food in the house) works similarly at an individual level.
No known conceptual or theoretical limits to constructing an RQ test. Just a matter of time and money.
Many results emerging on areas where rationality can be taught. But no widespread attempt to teach them.
Fairly compelling evidence towards the end that rationality is sufficiently separate from IQ to be studied/measured independently. The promise of an RQ test is, uh, promising but I wish the author had more directly confronted the problem that we can't measure instrumental rationality, only obvious deviations from it.
Similarly compelling evidence that tendency to initiate decoupling or inhibition is separate from ability to sustain it. It's not a distinction I had even thought to make before.
The tripartite model doesn't actually seem to add anything over dual process theory. It's not clear to me which features belong in the reflective vs cognitive mind or what tests you would use to justify the distinction, whereas Type 1 vs Type 2 processes have clear physiological markers. Any model at this stage is going to be an approximation, of course, but the tripartite model is not giving me any extra insight or predictive power. If IQ does indeed break down to mostly working memory and executive function, I don't see any reason to group those together in one mind while grouping other similar traits in another mind.
The taxonomy of rationality failures is potentially useful. I keep coming across more and more biases – having a systematic way to organize and understand them would help. Some cursory thought doesn't produce anything that doesn't fit the taxonomy.
The list of references on teaching rationality is a valuable starting point, but it doesn't seem to include any negative results so I will have to search from scratch too to avoid that bias.
The idea of decoupling/simulation as a concrete operation is one that I had missed so far. I'm not sure how well supported it is – a quick search for the term mostly leads back to this book. Similarly, not sure how much evidence there is that it is a more expensive operation than other Type 2 thought. It certainly has a useful place in the taxonomy though – there are a lot of biases that fall under failure to simulate other possible worlds.
Though it was a sidenote in the book, I really like the framing of dysrationalia as surrendering free will. It's vivid, clearly negative and framed as a loss – a good meme to combat the emotionless Spock.
Overall, the book seems to be largely an advocacy piece. It's well-written and interesting, but I worry about the strength of the argument (eg missing negative results) and I feel like I could have learned the same information from a much smaller book if the advocacy was toned down. This may just be a symptom of reading pop science books which have to explain everything from scratch – perhaps I should be pulling in more textbooks and literature reviews.]]>
Looks at how new technology is affecting collaboration in science, and argues that this could be a paradigm-shift on the scale of the creation of scientific publishing, if we take advantage of it.
Gower's Polymath Project. Open forum attack on a complex unsolved problem. Solved in 37 days, after 800 comments from 27 people, totaling 170k words.
There is a common phenomenon on the internet where a bunch of people get really excited about a common cause and get together to work on a project. Months later, they have produced 17 different versions of the logo and no actual work. Since this seems to be the default result, it's worth keeping an eye out for what makes successful projects different. I suspect that one necessary feature is that there is a coordinator who is already doing work on which other people can join in, rather than just an excited group.
Many different perspectives on the problem. More eyes on bugs. Creating a Fox.
Tools create shared short-term memory. Why short-term?
Cognitive tools – tools that add new abilities or capacity to human thinking. See mindware for non-tech examples.
Scientific process was one such tool – collection of hard-earned lessons on how to produce useful (predictive) theories about the world. Argues that networked tools could produce similar paradigm shift.
Breaks down into:
Incentives important part of collaboration eg GenBank initially had many leeches. Bermuda Agreement solved the coordination problem for the Human Genome Project – Leviathan won't give you funding unless you agree to share. No such agreement yet for eg flu genome.
Current incentives are strongly against sharing. Can't rely on individual action solving the problem.
Eg Wikipedia was long ignored by scientists, because they are under strong pressure to publish-or-perish and Wikipedia doesn't help towards that goal.
Kasparov vs the World (~50k people, ~5k people per move). Unexpectedly close match.
Many beginners, but also published opinions from strong (but not Kasparov -strong) players.
Smart Chess – Krush and team built public move tree. Helped coordinate, reduce duplicated effort, provided reference point for discussion. Reference point is interesting – having canonical name / url for particular sequence.
Between move 10 and 50 also played Krush' recommended move, but that recommendation was drawing from many inputs. Krush acting as coordination point / gardener.
Previous match Kaspov vs the World did not allow for such coordination. The World got crushed.
Collective brain is a poor metaphor – brain is very specific hardware/organisation/structure – no reason to believe that any collab cognition should follow the same structure.
Not same as Wisdom of Crowd, where average result is good but not as good as best individuals. These are examples where the collab effort was substantially better than any individual, on complex, creative, open-ended problems.
Plenty examples of collab efforts being stupider than individual. Need to figure out what precisely makes the difference to make this a useful tool.
Early tools – most successes (eg wikipedia, linux, invention of blogs) did not come from experts in the field. Note again that Linus' job is mostly coordination and filtering.
Random thought – creating a structure where the workers are exploring/chunking options so leader can make informed decision.
InnoCentive – 'craigslist for science'. Allows placing bounties on solution of scientific/industrial problems.
Makes connections between needs and skills. Networking enabling further specialization / increasing the effectiveness of specialization.
Restructuring expert attention – given need for cognition, find valuable outlet to connect it to.
Right expert to right problem at right time eg Krush move 10 moved game into area which she had previously analysed in a coaching session.
Microexpertise – Krush not better than Kasparov overall, but better on this particular move. Many microexperts combined to play well.
Chess structure is embarrassingly parallel – more search power can make up for worse value heuristics – does a chess team actually need variety or just parrallelisation? Would 10 Krushs perform worse than 10 different players at the same level? How much of the value here is different knowledge vs just being able to explore more options? Certainly some, but how much?
Designed serendipity – create structure that routinely makes such matches between problems and micro-expertise.
Imagine future where people only work in large collectives instead of individual problems. Are there opportunity costs to focusing everyone on same problem? Are the outweighed by faster/better solutions? What is the ideal tradeoff?
Right now probably so far from the ideal that any change would be an improvement, so not directly relevant, but an interesting question anyway.
Eg of serendipity – Einstein couldn't figure out geometry for Special Relativity. Grossman pointed him at Riemannian geometry, where many of the problems had already been solved.
Successful scientists tend to collect unsolved problems, keep lookout for chance encounter with a new tool. Want to optimize this process. Critical mass! Filtering noise vs signal also important in this metaphor – don't want to absorb too many neutrons.
Need diversity of knowledge/expertise/ideas but enough common ground to communicate.
Architecture of attention – task is to focus people on tasks where they have comparative advantage.
Historical strategies for collaboration:
Large formal groups restrict serendipity. Communication limited to hierarchy. Top-down allocation of tasks.
Dynamic division of labor – no fixed responsibility, no no pre-assigned task – to each what they can do best, continual update and reallocation. Does this only work for accretive problems? eg adding more theorems to maths pool is one thing, having 20 plumbers installing pipes at random is another. But the description of the modern construction industry seen in Checklist Manifesto is very dynamic. There is top-dow C&C of goals and responsibilities but decision-making power is pushed to the edges. Same change seen in 20th century militaries. But there is no provision for random volunteers walking in and installing a light fixture. In Smart Chess, who was allowed to update the move tree? Is protection of shared resources / right to filter info an important decision point?
Division of labor vs division of insight. Assembly lines work by controlling and making uniform the interface to individual labor. Doesn't work if you don't know in advance what work will be required. Dynamic division of labor important when you cannot plan the division of labor in advance. Again, modern construction does divide labor dynamically to some extent, but seems to have other important differences. Have to figure out what they are.
Contrast to CERN physicists – huge formal collaboration with fixed roles. Because it's infrastructure? How would you handle maintenance, certification, trust etc? Is the existence of a platonic solution the dividing line?
Markets. (Soviet planners ask 'who is in charge of delivering bread to London'). Prices aggregate knowledge. Dynamic division of labor enabled by using price as signal for supply and demand. Limited structure – not suitable for aggregating complex knowledge. Although maybe suitable for focusing attention eg InnoCentive.
Structure dictates what kid of info can be transmitted eg info about housing bubble was not reflected in prices because of the technical difficult/danger of shorting.
Also reminds me that transaction costs are interesting to consider – they limit the granularity of the minimum contribution.
Collaboration != committee. Members are volunteers – not compelled to be involved in areas where they are not already incentivised. Can also route around problematic members. Aha, can't do this construction. If some idiot is putting pipes everywhere you can't just mute them. Degree of interference dictates how safe it is to allow members without control. 50% in forum => moderate. 50% noise in construction => building falls down.
Is the 'online' part important? Couldn't put 50k chess voters in one place, and couldn't get them to communicate effectively if you did. Interface shapes interaction – allowing control over group dynamics.
Offline interactions have high bandwidth but also high latency (have to go find the person) and limits number of interactees (can only meet people in local network).
Compared to offline interactions, Polymath interactions were better edited/presented, easier to skip and could be returned to again and again. Same arguments for making one good lecture recording instead of suffering through many mediocre live lectures.
Scaling up forces specialization – at some level of complexity can't fit the whole problem in one head so need ways to distribute it effectively.
Linux. Encourage large community of contributions from the beginning. Result runs much of the world.
Open source architecture – access, modify, share designs.
Most software projects aim for a single, canonical version, but architecture example made me realize that it's possible to use similar techniques without any central authority at all (eg small scripts for extending Photoshop/Blender/similar could work like this). The authority is mainly there to act as a ratchet on the value of the solution in cases where many forked efforts would not be able to solve the complex problem.
Idea: authority/control/ownership can be an expense. Don't default to it but consider cost/benefit in each case. Many granularities possible too. Construction controls access to the building – can't cheaply clone a building so makes sense to protect the shared resource. Typical open source project also controls access to the official 'building' but allows clones to do whatever they want. Similarly for eg the authority to bill the client in construction.
Linux, Wikipedia etc are about scaling complexity in breadth/quantity rather than depth/quality. Most Wikipedia articles could be better written by a single expert, but no single expert could match the scale. Somewhat debatable for Linux – much of the kernel is written by experts already, and additionally contains much knowledge learned painfully over many years.
Open Source is:
Linux almost forked because Linus was a bottleneck on review/merge. Solved by delegating to lieutenants who each specialize in some areas. Whole codebase rewritten at great effort to be more modular. Allowed complexity to grow beyond comprehension of single person.
Polymath was monolithic/linear. Wouldn't scale.
Wiki-novels don't work because modular structure (chapters –> pages) does not match important structure of problem (arcs, characters, relationships, setting). Argues that collab novels could potentially work if tools matched the structure.
Issue trackers. Structure limits scope of conversation to single issue at time so not overwhelmed by total volume. Tags used to direct attention. Automation of routine tasks eg bors.
Reuse allows crystallized knowledge/work to accumulate rather than being started from scratch each time. Standing on the shoulders of giants. Compare to printing press – before that point knowledge was actively decaying all the time.
Dynamic division of labor – the writer doesn't even need to know that I used their code – can collaborate without coordination. Passive collaboration.
Scientific publications don't allow for fine-grained reuse / contribution eg taking a paper, improve argument, add data, republish – valuable effort but not rewarded (actually likely punished for plagiarism).
MathWorks competition – coding competition where submissions are automatically scored and published. Rapid feedback – community learns faster.
Micro-contributions – most MathWorks contribution only change one line, but cumulative effect is dramatic.
Task tracking becomes a limit on contribution size. Hammering in one nail in a building does not actually help – still have to check that the work is done, can't track on that level. Can only benefit from contributions that you can keep track of.
Similarly, DVCS reduces overhead of accepting contributions. Prior to that, dealing with many contributors required even more coordination around when to make changes to avoid painful merges.
Gowers asked polymathees to contribute only one idea per post – more modular, better reuse.
Scores in MathWorks act to automatically direct attention to most promising ideas. Similar principle to market prices. Krush/Linus acted similary as expert scoring systems – deciding which work to keep and which to discard.
More sophisticated mechanism might direct people to where their expertise is most valuable eg 'this section of code is new and hasn't seen micro-opt yet' vs 'this section is highly optimized but hasn't seen many different approaches'. Could also add noise to attention signal to prevent getting stuck in local optima.
Need to convert individual insight in collaborative insight to actually ratchet progress. Ability to do this imposes limits on the power of collective intelligence.
Experiment where subjects effectively share confidence rather than information – not effectively making use of collective knowledge. Group discussion focused on information they had in common rather than the unique information each possessed. Since the unique pro information was shared and unique con information was distributed, they each saw a much more pro view than they would have if they shared information well.
Collective intelligence works best on problems where verifying solutions is easier than producing solutions – otherwise not worth reuse. Plus transaction cost of reuse.
Group failed to pool info, but even if they did would have still had unresolvable differences on opinion. Collective intelligence works when group has shared praxis – common body of knowledge and values. High inferential distance adds transaction cost to reuse.
If the students were each interested in voting to effectively achieve their own values, and were collaborating in good faith, they still have shared goal of obtaining predictive accuracy. Tetlocks foxes were more effective at predicting outcomes in groups, without requiring that they share the same desired outcome.
Might make a stronger claim – that disagreements over shared values only interfere to the extent that it interferes with C&C over limited resources. In the architecture example, the pool of designs can still become valuable even if the members have many different design goals.
Important in economics/politics – good faith cooperation in eg thinktanks can still benefit all participants even if they fail to agree on fundamental values. Just difficult to produce when there is so much signalling pressure. Secret think-tanks? Private negotiations?
Think the point about shared praxis is accurate, just that this is a poor example in an important category, so worth contesting :)
Other problems – groupthink, civic breakdown, trolls.
Think of scientific revolution as establishing a shared praxis on how a theory should be judged, what makes a good contribution, when work is accepted/trusted.
Breaks down when no way to score contributions / direct attention eg wars over string theory because not yet any way to falsify.
Long imaginary narrative about potential future which I am too lazy to transcribe.
Swanson – no credentials / training in medicine but made valuable connections by trawling Medline for unnoticed patterns. Medline acting as cognitive prosthesis. Reminds me of the main character in Blindsight, who translates discoveries made by AIs into a form understandable by humans. Doesn't actually understand the science on either side, but is very good at pattern-matching.
Google Flu Trends – as accurate as existing methods but with much less time lag. (Many caveats). Superforecasting notes that google searches for 'housing bubble' track the event quite accurately, making the we-couldnt-have-possibly-seen-it-coming excuses of the CDOs somewhat hard to believe.
Trend is far more data, far more tools to make sense of it.
Sloan Digital Sky Survey – robot telescope with wide-angle lens trying to map entire universe. Have to make sense of this vast amount of raw data and condense it down into human-scale maps.
Algorithmic searches for eg dwarf galaxies, twin black holes.
Previous survey efforts produced photographic plates – could not share easily. SDSS is free online – reduce barrier to entry for asking interesting questions.
Projects like this are question-limited rather than data-limited.
Most scientists would prefer to share data, but there are strong negative incentives. Moloch whose blood is citation counts! Even SDSS has an exclusivity period.
Unrelated to current section, but suddenly struck by the contrast between normal vision of the future where scientists become tiny depressing cogs in a vast machine, and the vision here where they are microexperts whose unique talents are carefully aimed at matching problems. Not necessarily any actual difference day-to-day, just a different way of looking at value.
Unhitching observation from analysis. Rise of informatics.
Big projects are more likely to be open:
Data-driven intelligence – routing mechanical work applied at huge scale – broader than ML in that it doesn't require learning/inference, just massive-scale machine labor.
Freestyle chess is dominated by human-computer teams. Same for weather prediction, surprisingly.
Describes process of sequencing genomes. Not much brains, but a lot of brawn.
Data web. Currently not much open, not much linking. Slowly growing though.
Combining/linking data increases the numbers of questions which can be asked/answered non-linearly.
Change in the nature of explanation. May not always be some simple underlying theory. May be just a massive pile of correlations with good predictive power. Eg statistical translation still beating explicit linguistic models. Maybe end for Occams razor. Rationality: A-Z has a lot to say on the subject, from the point of view of how an AI would form theories about the world. Well worth reading again.
Hanny's Voorwerp. Volunteer on Galaxy Zoo finds a strange blob. Asks in forum – noone knows. Photo is verfied. Other telescopes pointed in same direction to confirm it really exists. Still unsolved.
Green pea galaxies. Originally running joke in forums. Some explained away but a few remain mysteries. Forum members learn spectral analysis to investigate. Professionals come and go but most of the work in the discovery done by amateur voluteers.
Argued that when it comes to rationality large effect sizes on small groups are more important for the world right now than small effect sizes on large groups. Would that continue to be true if large-scale amateur science like this became more common?
FoldIt – crowdsourced protein folding. OS community for scripting interface. Tracking score lends weight to advice of high-scoring players. Taking advantage of microexpertise in 3d spatial reasoning. Biological understanding condensed into game interface so barrier to entry is low.
Open Dinosaur Project – crowdsourcing extracting data from paleo papers into structured db.
Galaxy Zoo classifications now used as training set for machine classifier.
Community build – rapid and helpful feedback vital for maintaining motivation.
Cognitive surplus. New tools overcoming barriers + costs, letting more minds getting involved in large, complex projects.
Managing large-scale collab without money – different structure+incentives from eg corporations.
Ideological opposition to Polio eradication. Measles outbreak in UK after bogus MMR-autism link.
Politically, scientific policy treated as yet another interest to be placated rather than source of actual knowledge.
Both education and markets have improved scientific literacy (markets by making benefits widely available – turning scientific discovery into a passive good).
Can we do more?
Open Access – very hard to become informed through expensive paywall. Last year I read several hundred CS papers at work. If they were priced like eg medical papers, that would have been ~$5000 which is 10% of median US household income.
Especially problematic for data-based intelligence – no affordable options for crawling.
arXiv, PLoS enable construction of new bridging institutes – can link directly to source.
In 2009 Elsevier made $1.1b profit on $3.2b revenue. For context, in 2009 the NSF asked for $6.85b
Much lobbying against Open Access eg want to make it illegal for funding bodies to require Open Access.
Singh libel case. BCA evidence torn apart in a day by a group of bloggers. Also acted as source of expert info for Singh's legal team. 'Wiki litigation'.
Tao's blog has 10k readers.
Vision of science as a 2 way medium instead of untrusted loudspeaker. Certainly can't rely on the media to convey science accurately.
Ingenuity gap – gap between societies problems and it's capacity to solve them. Need to bridge it before we lose.
Fear of not being credited or being falsely accused of plagiarism.
Discoveries used to be closely guarded. Publish-or-perish incentivized sharing of discoveries by rewarding it with prestige and career advance. Scientific revolution could not have happened otherwise.
Bottleneck now is sharing ideas + data. Need to figure out how to provide similar incentives to share.
Qwiki failed – no incentive to be the first to contribute. Similar result for open peer review. Huge selective pressure to focus only on personal reputation.
Why do the programming equivalents work? Maybe reputation in OS comes from public display of knowledge, rather than discovery?
But Amazon reviews work. Sad that we have a situation where review important scientific discoveries is less incentivised than reviewing pokemon cartoons.
Existing collab projects are conservative – downstream goal is still publication. For the scientists at least – the volunteers are in it for the joy of discovery, or the need for cognition, or maybe just to climb the leaderboard.
Influences my thinking on direction – need to be bear in mind community influences – eg are there areas of research that don't fit well into discrete publications.
This is why best examples of collective intelligence come from outside science – publish-or-perish produces too many negative incentives.
Similar disincentives socially – no respect for coordination or infrastructure work.
Patents, NDAs. Bayh-Dole act. Has an influence, but overweighted in discourse – not nearly as strong as publish-or-perish.
Coordination problems, network effects.
Similar problems in early days of journals – solved by funders insisting on publication.
Compulsion – beginning to see Open Data policies as well as Open Access.
Easier upstream – funders don't have to fear losing customers – publishers do.
Need social pressure to follow – otherwise will get bare minimum data dump and nothing else.
Incentives – reputation economy. Give public credit for data, code and infrastructure.
Climate Research Unit hack – working in public could lead to chilling effect on scientific thought, for fear of being used out of context.
More info = more confirmation bias. Less gatekeepers = less defenses against memetic misinformation. But we already have this problem – pseudoscience spreads over all the place but actual science is hard to access.
Increasing complexity of all fields may make open/collab science the only option.
Call to action.
Can we solve coord problem in other communities? Eg good quantified self research could be done by a small group with little funding, maybe act as a seed.
Most discussion I've seen on the subject of collective intelligence has been fuzzy daydreaming. This book benefits from being very concrete. It's a cautious extrapolation from current trends and past examples.
I'm most interested in the section on collaboration. It's far from being complete, but it introduced some good ideas and triggered more. Now thinking of collaborative work as consisting of:
Constant ongoing process. Have to pay attention to efficiency of each section eg modular software is easier to decompose into human-sized subproblems.
Most of the rest of the book consists of examples and ideas I've seen before, but seeing them all gathered in one place is valuable. Makes me realize that the traditional academic career is not the only path to contributing to science.
So here is an idea – find a narrow field, with low barriers to entry, that is currently under-served, where participants have some intrinsic motivation to get results but no active incentive to hoard them. Would it be possible to seed some interesting collaborative effort by working in the open and making it easy to get involved? Certainly works for open-source software, and there is a lot to learn from looking at successes and failures there. The rationality movement and quantified self seem like two good examples of semi-scientific efforts that have arisen outside of the normal scientific structure.]]>
Author is seemingly well regarded in HCI.
Anecdote about sock puppet testifying in Congress. Puppet makes a statement about violence in video games, senator asks puppeteer if she agrees with the puppet. Takes the room several seconds to realize that this is ridiculous and start laughing.
Company running software evals notices big jump in ratings at some companies. Finds that the only thing that has changed is that questionnaires are now being run on the same machine as the software being reviewed. Same effect is reproduced in controlled experiment. Interpretation is that people subconsciously avoid criticizing the computer to its face. That may sound crazy, but a senator interviewed a sock puppet in congress. Anthropomorphisation is very easy.
Author hired to improve Clippy. If we think of Clippy as being a social actor, the reason for his unpopularity is clear. He's a asshole. Also leaning over your shoulder, interrupting your work and never even remembers your name. Can't fix directly, so author made a scapegoat instead – if Clippys help isn't good enough he offers to help you write hate mail to MS. Very popular, but not adopted by MS for some reason…
German drivers complain about a new gps device that has a female voice. Say they don't trust directions from a woman. Not mollified by the idea that all the programmers and designers were men, because it's still a lady robot.
Author claims many results on social psychology transfer directly to HCI. But doesn't really back it up.
Wanted to study flattery but no experimenters interested – too hard to control. Realized that computers are an ideal confederate – can totally control interaction, personality, appearance. Designs experiment with 3 groups. A) runs driving games, gets +ve feedback, told the feedback is from a highly sophisticated AI. B) runs driving game, gets +ve feedback, told the feedback is just random standin until the AI is ready. C) gets no feedback at all. Groups A+B both rate their driving better than C, even though B was told the feedback was meaningless. Author concludes that flattery is always safe and effective.
Or maybe flattery has no downsides when there is no possible suspicion of manipulation? Seems like a big leap.
Does degree of tech literacy affect the extent to which this effect holds? Who are the test subjects?
Company evaluations suck.
Thalamus – involved in valence/judgment
Cannot avoid valence response to feedback – too deeply wired.
Hedonic asymmetry – negative valencies are stronger and last longer, and negative spirals much easier to start/maintain than positive spirals.
In driving game, random flattery increases self-evaluation of skill, but random criticism has no effect. Explained away.
In news reading experiment, articles which criticized third parties reduced approval of third party, writer and newpapers. Criticism seems to cause all-round negativity.
Don't recall details of praise for long, but details of criticism stick in the memory.
Retrospective interference – negative valence can cause so much load that interferes with committing immediately preceding events to long-term memory. Reason why tech support calls often can't remember exactly what they did to trigger the problem.
Proactice enhancement – events immediately following negative event are more easily recalled.
Warning drivers about instances of poor driving didn't cause improvement, caused negative spiral.
Implies feedback should start with few negative items, followed by lots of positive items. Constructive criticism – focusing on desired outcome rather than current undesirable state. Specific details, so easy to make response and feel like issue is resolved rather than lingering. Leave time to react – no hit and run – but don't pressure for immediate response.
Mindset. Single message about mindset ("you are good enough for this game" vs "anyone can learn to play this with enough effort") followed by crushing default affects willingness to play other games described as being really hard.
Similarly, fixed mindset praise ("you are good at this sort of game, this game will be really easy for you") reduced enjoyment of task and self-evaluation.
Implies framing of feedback is important – "let's come up with a plan to do better at X" vs "you are bad at X" – "you've improved at X" vs "you are good at X".
Computers which criticize others viewed less favorably but judged as more intelligent.
Voice recognition programs which blamed themselves for errors were better rated and sold more items but were viewed as less accurate, vs those that blamed the user or even some third party.
Voice recognition programs which praised themselves were rated lower all round.
Almost all the experiments are the authors own. No effect sizes or sample sizes given. References not given inline (although appendix has list of papers per chapter). Seems like a lot of generalization from small number of experiments, especially given that it's extrapolating from machine interactions to human.
Personality traits – various behaviors measures form correlated clusters – attach labels to these clusters – measurable and have some predictive power.
Large lit review says these traits are largely set in place by 5 years old. TODO read review.
Computer federates can have personality to order – useful for experiment.
Control – dominant vs submissive – desire to influence/control other people.
Affiliation – friendly vs cold – desire to interact with other people
Extrovert – dominant and friendly. Introvert – submissive and cold. Critic – dominant and cold. Sidekick – submissive and friendly.
Comes with horoscopes for each. TODO how strongly predictive/correlated are these trait clusters?
Took strong introverts and extroverts and had them rate item descriptions on ebay. Descriptions written to match either personality, but with same factual content. Items written by similar personalities are rated higher and bought more often. But doesn't even control eg length of description. And extrapolates from this that introverts prefer to interact with other introverts and vice versa.
Can judge personality by voice – reference another book by same author. Similar results – 'introverted' voices preferred by introverts and vice versa.
Ambiguous personalities (eg voice and body language don't match) disliked by everyone. Sign of trying to fake interaction? Apparently a widely reproduced effect.
Gradually switching personality to match subject does lead to approval. Recommends doing this in real life, but can the average human really do that without triggering the ambiguity effect?
Identification and interdependence. (Linked via kin selection.)
Identification – something, anything, as shared identity to form group around.
Record subject giving feedback phrases. In test, subjects who get their own recordings as feedback rate their performance higher than subjects who get recordings of someone else (of the same age, race, gender) and rated feedback as more valid and objective.
Visit swing voters and show them pictures of Bush and Kerry, ask which they will vote for. Unknown to them, one of the candidates has had their face merged with the subject. Causes small but significant shift in voting. Strong implications for diversity in hiring.
Manipulate identity to form teams. Focus on shared attributes.
Inside jokes, jargon etc.
Interdependence – focus on or create shared goals.
Can be completely arbitrary eg matching wristband color with computer color affects approval rates, trust, perceived intelligence, persuasiveness.
Widely established that feeling of team spirit correlates with positive outcomes. Causation in other direction seems entirely plausible.
Typical activities don't work. Trust falls bring focus on lack of trust. Bridge building creates interdependence but focuses attention on mistakes. Whitewater rafting leads to associating teammates with fear. No team-building retreats. Focus instead on continuous reinforcement of identity and interdependence. Just so! Could tell an equally convincing story in the other direction – what activity involves interdependence but has no potential to make mistakes?
T-shirts and swag – make it identifiable to team members but not a loud billboard – think of it as a secret handshake.
Individual incentives (raises, promotions) reduce interdependence, promote competition.
If no obvious shared goal, make cooperation itself the shared goal.
Merges create asymmetric dependencies. Create artificial dependencies in other direction eg make people rely on subteam for infrastructure.
Dealing with defection. Highlight benefits of cooperation. Highlight the fact that the team members were selected for this job. Make membership exclusive. Hazing / initiation.
Or cut losses – single out the defector and unify the group against them. Deviants can useful as bounds on acceptable behavior. Removing deviant tends to lead to another one being singled out. Wait, I thought we were dealing with a defector, how is a new defector made… just starting to sound like an excuse to pick on someone.
Maybe create an artificial deviant instead.
Identity can engender group think. Make diversity of opinion a shared goal, rather than consensus. Focus on individual understanding of all group opinions rather than group decisions.
Commitment to unanimity polarizes opinion. Risky shift – focus on positive outcomes and unwilling to bring up possible negative outcomes, betraying the group – leads to shift towards high risk / high reward choices. Ahem.
Root of emotion is self-evaluation:
Maps to axes of emotion:
All emotions totally explainable as just position on those two axes. Uh…
Ok, what about bittersweet nostalgia? Neutral valence? Same as diffidence?
Other descriptions I can find of these axes don't make nearly such strong claims – just that they capture much of the variety of emotion, not all of it.
Emotions cause physiological response. Negative responses are stronger and last longer. Response can linger and be confused with other emotion eg being forced to smile increases happiness eg riots are as often caused by positive events (eg winning big match) as by negative events – high arousal lingers and next negative stimulus gets treated as if it had that magnitude.
Contradictions between emotion in eg tone and body language cause negative reaction, similar to contradictions in personality traits.
Positive valence increases problem-solving ability / creativity. Brain at Work also covers downsides – suggests that being able to regulate valence/arousal is useful for changing mental state.
Valence is extremely contagious. Not clear from experiments described whether this is contagion or similar people clustering together. Maybe some details are missing.
Happy drivers prefer advice from happy computers. Sad drivers prefer advice from sad computers. Seems tricky setting the subjects emotion at the start of the experiment.
Humorous computers rated more popular, did not distract subject (no reduction in time taken or accuracy on task). Same results even when jokes aren't funny. Again, generalizes from this to humor being safe and non-distracting in all contexts.
Arousal lasts longer than valence. Avoid giving negative stimulus to people who are still aroused.
Hard to fight over-arousal with logic. Emotion is pulling the levers.
Frustrating game with deliberate freezes/hangs. Group A) gets multiple-choice questionnaire B) gets open ended questionnaire C) gets open ended questionnaire + emotional support from computer. C led to longer playtime in next game. What about emotional support alone?
Suppression vs reappraisal. Driving computer that suggests reappraisal when cut off led to improved driving and reduced frustration.
Evidence that reappraisal can be learned/trained.
Expertise / specialism – news programmes on tv labeled "News TV" rated more important and trustworthy than news programmes on tv labeled "News and Entertainment TV". Similarly for comedy on tv labeled "Entertainment TV".
Dark side skills: Grill opposition with questions you know the answer to, makes you look like an expert – audience ignores that you picked the questions. Assign arbitry specialties when interacting with outside world eg designate one person on team as UI expert, refer to them that way and send all comments on UI from their address. You were supposed to bring balance!
Discomfort with uncertainty – want to believe or disbelieve immediately. I am more and more aware of this affecting my decisions – have to fight to remind myself to leave things open. 'Is an expert' is a heuristic that can be grabbed to make a decision and relieve uncertainty.
All cultures create gender identities from early age via clothing, colour, segregation etc. Which is freshly disturbing after reading so many experiments on the power of even small artificial differences.
Products sell better when aligned with gender stereotype of voice – judged as more knowledgeable even though the voice is clearly synthesized and it's gender couldn't possibly influence the text. Interesting way to remove the possibility of rational stereotyping by demonstrating that the stereotype is applied even when it couldn't possibly make sense – seems to indicate that even knowing for certain that someone is knowledgeable/proficient in a non-stereotypical area is not enough to prevent bias.
Stereotype threat – isolated minorities have their performance swayed towards stereotypes eg a single girl in a roomful of boys will do worse on math tests. Given the choice, may be preferable to have some non-diverse groups and some groups with substantial minority presence, rather than distributing minorities evenly.
Online test with virtual avatars for classmates. When told that group performance is measured instead of individual performance, stereotype threat vanishes – performance is the same as with a female majority.
Huge list of halo effects.
Two points of judgment:
Common mistake is to try to convince people only on point 1, forgetting to make them trust you first.
Reciprocity. Computers which appear to be working hard to help subjects receive more effort on later data-gathering tasks. Switching computers removes effect, so not just general good mood. Computers which appear to be lazy get less effort, so not just familiarity.
Same experiment in Japan – no effect. Some explanation about how reciprocity in Japan is aligned with groups, not individuals. Trying experiment with PC vs Mac shows effect. Does this not terrify the author – how many of the experiments here will only reproduce on WASP ivy-league students? Also, gave a glib explanation and only a single additional test – doesn't egender confidence that this was actually the reason and not just luck. Sample size? Effect size? Nada.
Sharing personal info / vulnerability creates reciprocity. Computers that share ridiculous personal info ("My CPU is only 400MHz so sometimes I can't keep up") get more personal info in questionnaires (as judged by blinded readers).
I notice here that I am predicting the results of the experiments before reading them. Where are the surprises? Where are the failures? Are we only seeing a cherry-picked sample?
Following structure of justification gets more help even if content makes no sense. eg at copier machine 'can I go ahead of you?' vs 'can I go ahead of you because I'm in a rush' vs 'can I go ahead of you because I need to make copies'. The last two are equally effective vs first, even though 'because I need to make copies' is not actually a reason.
Similarity attraction for expertise eg US readers prefer US advice about Swedish destinations over Swedish advice about Swedish destinations. This is not necessarily irrational, eg I have learned to be very cautious about Chinese advice on where to eat in China. Advice is not just about the facts but also about understanding of advisees desires/goals/values. An American would be much more likely to accurately judge what kind of food I would enjoy eating.
Trustworthiness is often more persuasive than expertise. For example, politicians competing on who can be the most ordinary – "I'm just like you". I don't want someone like me running the country – I want someone much smarter. But it is rational to prefer someone who appears to be in your tribe – can be trusted to represent your interests.
Elaboration likelihood model – two modes of thought:
ie trustworthiness is a cheap heuristic, acts as default / fallback
High arousal, low energy, time pressure push towards peripheral route. Need for cognition pushes towards central route. Oh, this suddenly brings into focus a typical mind fallacy I have been carrying around for ages – I expect other people to at least attempt to dispassionately examine evidence when making important decisions and am often frustrated when they don't – but if that mode of thinking is largely predicted by need for cognition it makes sense that many people won't choose it at all. I've been trying to persuade people on the central route when I could have predicted that they would be using the peripheral route.
Framing/priming can switch routes eg signs that someone might not be trustworthy (eg ambiguous tells that we saw earlier) can trigger peripheral route.
Argues that Bush was one of the most intelligent presidents ever but had poor presentation / mixed tells that lead to much distrust. This runs head first into my wall of liberal bias, so I went looking for falsification of my existing poor opinion. Stanovich argues that while tests show he has an IQ around 120, reports from various aides indicate that he has a low RQ. I would like to see if someone has cleaned up and re-presented one of his speeches, to see if I would be more persuaded without his various verbal tics.
Pitches from Aus-accented whites vs Aus-accented asians vs Korean-accented asians – pitches with mixed-stereotypes are less successful.
No mention of any experiments which find difference between human and machine interaction – the first thing I would want to do is figure out under which situations this holds and to what degree and what kind of human interactions produce same results as computer interactions. Find it hard to believe that every single experiment succeeded.
Experiments are also predictable from the narrative – can guess the results from the setup. Might just be good storytelling, but seems like everything lines up way too neatly. Given that we've previously encountered strong evidence that people prefer a coherent story over an accurate report, it seems worth being extra suspicious of anything that doesn't exhibit a real-world level of messiness.
The results are neat too, and massively generalized from the single experiment (or for some of the sections, from no experiments). Maybe the author is speaking from experience and has seen enough evidence to generalize, but I want to be convinced directly.
Also, the one experiment tried in a different context fails to generate result. They try a single tweak, got a result that matches a theory and call it a day. If I was the author I would be looking back at a book full of experiments run in a single culture with probably very homogeneous subjects and wondering just how much of it was down to context. This is Fundamental Attribution Error 101 – preferring to attribute behavior to immutable attributes rather than context/environment.
(Could this be an example of the introvert vs extrovert preferences in the ebay experiment? Do I distrust this because of the presentation style? Have I been more harsh on the evidence here than in the more cautiously-toned book? I think I need to come up with a structured process for deciding whether I believe a claim, to avoid potential bias.)
One of the ways I evaluate ideas is in terms of expected predictive power – how confident I am that I could use this idea to usefully predict results in the future. I have low confidence in most of the individual claims in this book – due to the limited samples, lack of replication and the vulnerability to different contexts and cultures. Worse, many of the claims can interact and it's not clear from these experiments that you could predict which effect would be dominant.
Another way I evaluate ideas is in terms of hypothesis generation and attention focus – do they give me a new viewpoint from which to notice patterns. From that point of view, the broad ideas outlined in the book – that interaction with computers can draw on social heuristics and that computers can be used to isolate social heuristics – seem worth adding to my collection of perspectives. I am more likely to be on the lookout for such effects in future, even if I don't think that I can predict them in advance from the evidence here.
Some of the results are more widely known / replicated, and of those there are some that seem very useful to bear in mind:
Lastly, I benefited a lot from the reminder that most people use the peripheral route more than I would expect based on my own internal experience.]]>
Ideas about how to effectively manage your brain, based on modern science.
Author is an executive coach rather than a researcher, but has been highly recommended by several researchers. Still, tread with caution.
Each chapter starts with a story illustrating a poorly handled situation. Explains the mechanisms at play. Then shows how the story could have gone better by following advice. I felt like these stories helped make the ideas stick with me better so they are worth reading, but I won't attempt to summarize them here.
Goal is to work smarter, be more focused and productive, stay cool under pressure, reduce the lengths of meetings and influence other people.
Prefrontal cortex. In evolutionary history, last major brain region to develop. Involved in setting goals, controlling impulses, imagining fictive scenes. "It's where we hold thoughts that are not being generated from external sources or from the senses."
Limited computation power. Prefrontal cortex is to rest of brain as change in your pocket to the US economy. Goldilocks – everything has to be just right for it to function well.
Metaphor: Prefrontal cortex is a stage. Actors are information held in your attention. Sometimes they enter from the side – info from the outside world. Sometimes the audience jumps on stage – ideas, knowledge, thoughts and memories from your own mind.
To understand an idea, you put it on the stage and hold it there long enough to see how it connects to audience members.
To make a decision, you hold multiple actors on stage and compare them. To make a conscious decision? Compare to research on unconscious decision making, which doesn't require attention and can better compare choices with many facets.
To recall information, you call it up from the audience. If it's dimly remembered, it may be in the dark at the back of the crowd and might take time and effort to find. What about when you fail to recall something and hours later it just pops into your mind. Does recall actually require conscious attention?
To memorize information, you need to get if off the stage and into the audience.
Inhibiting a thought – trying to stop an over-excited audience member from getting onto the stage – takes effort but is essential to saving the limited capacity of the stage.
Effort = oxygen, glucose. Prefrontal cortex is a pig – can measure drop in blood sugar when active. Will shut off when out of resources. This is why you are easily distracted when tiredor hungry.
(Basal ganglia – control routine actions. Highly efficient.)
Implication is that attention/focus should be treated as a limited resource, to be deployed where it is most valuable. Don't give attention to things that are unimportant/ineffective. Be disciplined in not thinking about problems that you can't currently solve. Personal habit is to reflexively check whether I can do anything right now, and if not then set a reminder to think about it at some future date. The existence of the reminder stops me worrying about it.
Prioritization requires prefrontal cortex. Don't allow any distractions (eg email) before prioritizing your day. Parallels popular advice to start your day by doing the most important thing.
Recall of chronological events (eg what was for lunch 10 days ago) requires walking backwards in time. Recall difficulty is proportional to time since event.
Imagining something unfamiliar takes more effort than imagining something familiar. Easier to think about current problems than possible solutions.
Have very efficient circuitry for social interactions, images and narratives. If you can convert an abstract problem into a social/visual/narrative problem you get to take advantage of this. Is this why metaphors are useful?.
Practices like writing down thoughts/worries/ideas can aid inhibition, freeing up attention for more useful work.
Schedule tasks that require attention at times when you expect to be fresh.
Stage is small. Short-term memory seems to be around 4 items. Can maybe only remember 1 item without any degradation. Figuring out what constitutes an item is tricky, probably depends on what supporting mental structures you have – could be 4 digits or 4 words or 4 sentences, if the sentences make sense.
Relational complexity – when making decisions, the fewer variables you hold in mind at once, the more effective you are at making decisions.
Competition between circuits. Contradictory circuits inhibit each other. Can only focus on one at a time, but can switch between them at will.
Simplify – reduce ideas to a size that can fit into focus.
Chunking – creating mental structure to aid recall eg grouping phone-numbers into 3-4 digits. Chunks seem to work best when they take about 2s to think or say.
Part of becoming an expert at something seems to be in creating new kinds of chunks, allowing to think more complex thoughts without changing the limits of the prefrontal cortex.
Prioritize what gets focus. Use external storage (eg writing) to offload chunks that are not immediately needed.
Interesting to relate this to previous notes on Fermi equations or cost-benefit analysis. Is the main benefit of these externalizing the structure so that we apply focus to each detail of the problem in turn?
Another limitation – stage can only perform one process at a time. Processes include understanding, making a decision, memorizing, inhibiting.
Can only multitask effectively when all but one of the processes do not require conscious attention. Dual-task interference – context switching between two mental tasks tanks performance.
Constant texting and emailing has same effect.
Allostatic load – measure of stress hormones and other factors related to sense of threat – physiologically damaging. Being 'always on' leads to brain being stuck on alert.
Does this imply that I shouldn't switch too much between note-taking and reading? Had been considering switching to chapter-at-a-time. Will try that on the next book. The effort to remember the contents of a chapter long enough to take notes may be useful in itself.
Solutions to multitasking problems:
More automation (mental or technological). Automatic processes don't steal focus. Eg learn to touch-type, learn keyboard shortcuts. Develop routines for daily tasks so that there are patterns for the basal ganglia to learn. The combination of fullscreen book, zen mode editor and well memorized keyboard shortcuts to switch between them has been very useful for writing these notes.
Handle recurring mental distractions. Solve nagging problems. Find ways to address or delegate worries. Don't let unmade decisions sit in your mental queue.
Explicitly partition attention. Schedule times to check email. If interrupted by a call, abandon current task rather than trying to do both.
TODO There are lots of dramatic physiological signs that conscious effort is being applied. Can I find a way to continuously measure this on myself? Can I detect exhaustion and warn myself to stop?
Distractions, both external and internal, require effort to inhibit.
Most common distraction I notice right now is going from understanding something to thinking about how I would explain it to someone and then drifting away into thoughts of eg how to structure a talk.
Seems likely that mindfulness practice would be effective in reducing internal distractions since it increases awareness of conscious thought.
Anterior cingulated cortex – involved in detecting novelty + errors. Small amounts of novelty are pleasant. Large amounts cause stress/alert/anxiety/fear.
Ventrolateral prefrontal cortex – involved in 'braking' / inhibiting responses – overriding instinctive reactions and replacing them with conscious, reasoned reactions. Requires energy, works less well when you lose your focus. Self-control is a limited resource. I note that the most likely time for me to break down and binge on icecream or chocolate is during a stressful work day. I just lose the ability to override the desire.
For subconscious decisions, there is a short (~0.3ms) interval between activation in brain and conscious awareness of decision. There is another short interval (~0.2ms) between conscious awareness and actual action. This is the window in which you can inhibit your response. Once you start the action and begin getting rewards (eg novelty reward for checking email) it's much harder to control yourself. See also the focus in rationality of teaching 5-second level skills. Knowing principles is not enough, have to be able to reflexively apply them.
Don't have free will (ie no control or introspection on subconscious decisions) but have "free won't" – can inhibit action.
Easier to veto actions/thoughts if you have conscious labels for them. Can improve vetoing with practice. This last point doesn't have any references yet? If true, does it transfer across different situations?
Over-arousal reduces performance. There is a sweet spot between too much pressure/stress and not enough, where performance is maximized. Does this only apply to conscious tasks?
Norepinephrine – brain, feeling of alert/fear. Can increase alertness by visualizing scary consequences of failure. Don't overdo it. In climbing, common advice is not to allow visualizing failure at all after initial preparation. Maybe just visualizing a hard but successful climb is enough to increase alertness? Does alertness even matter for non-conscious tasks?
Dopamine – brain, feeling of interest/novelty. Can increase by change of environment or by visualizing future rewards. Seems to be more effective than using alert/feat.
Bit confused about the relationship between the specific chemicals and the practices. Mentions that reward can generate both dopamine and adrenaline, which presumably increases norepinephrine.
Over-arousal – experienced as stress, panic, over-excitement, inability to think/focus. Reduce arousal by eg taking a walk.
Creative block – can't generate novel ideas, keep circling back to some existing idea. Same actor keeps hogging the stage. Can't think out of the box.
Need to reduce activation of the blocking circuit before others can be activated. If inhibition isn't working, must switch off entirely. Even a very short break to a completely different activity can help.
Right anterior temporal lobe – involved with connecting distantly related information. Shows more activity when successfully solving a problem. During the process of solving the problem, presumably?
Insight is more likely in happy, relaxed states than in alert, focused states.
ARIA model – Awareness, Reflection, Insight, Action – both describes the stages of insight, and advises how to aid insight.
Awareness – viewing the problem we are stuck on. Want to leave space on stage for other ideas, so simplify problem as much as possible – elevator pitch. Don't try to focus too hard.
Relection – reflect on thinking processes. Notice what strategies have been tried and what might be missing. Aiming for relaxed daydream-like state.
Insight – solution is discovered. Visible as burst of gamma waves in brain – lots of communication between different brain areas. Burst of dopamine and adrenaline.
Action – take advantage of short-lived high – extra courage and energy.
Executive function – director is the part of you that observes your mental processes.
This is the part of you can debug and rewrite your mental processes!
Interoception / mindfulness – awareness of your own thoughts and thought processes. Can be improved by training. Measured by Mindful Awareness Attention Scale.
Default network – involved in planning, daydreaming, ruminating. Also narratives. Called default circuit because it tends to kick in when not doing anything else.
Direct experience – focus on immediate sensation and perception, including interoception. Confused by this – my anecdotal experience is that direct perception and introspection are different states and are mutually exclusive.
Default network and direct experience inhibit each other. Mindfulness training increases the ability to notice and control the switch.
Limbic system – track emotional relationship to thoughts, events, people, objects. Executes value judgments.
Toward/away response – limbic system motivates you towards primary rewards (food, money, sex etc) and away from primary threats (predators, hunger, thirst etc).
Not very bright – activates on eg scary pictures or bad memories.
Emotional arousal often measured by activation of amydale.
Walk toward, run away – away responses trigger faster, last longer and more intense. Towards responses are more easily displaced. This is why negative spirals are more common than positive spirals.
Over-arousal of limbic system can impair decision-making. Can also sometimes increase confidence (eg adrenaline rush). Poor combination.
Surprisingly strong effect. Triggers as simple as smiley/frowning faces can impact performance.
Over-aroused limbic system also makes more sensitive to false pattern-recognition. When you are scared, every tree is a monster.
Attentional blink – interval between identify different stimuli. If internal stimuli are competing with external stimuli, get more blinks and can miss info.
Before emotional arousal: Situation selection – avoid situations that cause high emotions. Situation modification – do what you can to make the situation less impactful eg prepare thoroughly. Attention deployment – control thoughts to avoid spiralling, focus on other stimuli.
During emotional arousal: Express – eg cry – often not an option but worth considering when it is. Expressive suppression – hold it in – diverts cognitive resources. Cognitive change – use conscious control to influence limbic system eg via labeling or reappraisal.
Labeling – simply attach a label to the emotion eg "I notice anger". Seems to inhibit limbic system. Use simple label, not lengthy description, to minimise focus given to the emotion.
Higher scores on MAAS correlate with better effectiveness of labeling. Note that common to many mindfulness practices is labelling and then releasing thoughts/emotions.
Prediction is one of the core functions of the brain.
The limbic system is activated towards certainty and away from uncertainty. Strong response – uncertainty is treated as a threat.
Similarly for autonomy/control. Feeling of control, the ability to take action, can reduce the threat from uncertainty.
Reappraisal, reframing, re-contextualising – finding a different perspective on the situation. Cannot directly control limbic system, but can influence it by looking for perspectives which generate the desired level of emotion.
Types of reappraisal. Reinterpreting – new information brings an new understanding – "oh, they're crying because they're happy about getting married". Normalizing – recasting the current situation as expected – "it's normal to feel overwhelmed when starting a new job". Reordering – place focus on different values – "getting fired is actually exciting, it means I now have freedom to choose a new life". Repositioning – deliberately simulate a perspective other than your own – "if I were in their shoes I would be angry too".
People who use reappraisal rather than suppression are better off on a variety of measures of emotional health such as life satisfaction.
Discusses whether reappraisal is tantamount to lying to yourself. Note that Tetlock's foxes made more accurate predictions by synthesizing many perspectives. It's totally plausible that reappraisal could lead to more accurate perceptions on average.
Suggests using reappraisal when frustrated with yourself too – instead of "I screwed up" use "my brain screwed up", leaving yourself more resourcs to actually fix the problem.
Expectations can influence your perceptions and reactions. Perceive patterns faster when expecting to see them. Also more likely to have false positive.
Unmet expectations can cause a threat response, as if you already had the reward and it was taken away. Seems similar to loss aversion – moving the reference point changes the value function. Unexpected rewards are more pleasant than expected rewards (in the moment, presumably. You don't get the pleasure of anticipation with surprise rewards).
Placebo effect can reduce pain. Effect remains, though less strong, even when told it's a placebo. (Can measure brain with brain scans, to avoid problems with self-reporting.)
Advises managing expectations, especially when you notice that they are causing strong arousal. If you need the prospect of a reward, focus on a certain reward rather than one that might not appear. I find it useful to focus on the satisfaction of a good days work as a reward rather than on the results, otherwise I can get demotivated when the results don't match my expectations.
We also experience away/towards response for people. Classification into friend/foe is immediate and subconscious, and usually defaults to foe to be on the safe side.
The quality and quantity of your social connections is the one thing that has been empirically demonstrated to raise your happiness (once base needs are met).
Loneliness or feeling left out generates a threat response.
Speaking out loud while learning increases retention.
The foe response reduces empathy. Makes it harder to see the foes perspective. Makes you more likely to interpret intent as hostile. Contaminates your opinion of ideas that they support.
Defusing the stranger-danger can be as simple as sharing a joke or shaking hands. Banter at the start of a meeting or events that unite distant colleagues are examples of practices that can switch the categorization.
Perceived fairness generates a reward and perceived unfairness generates a threat response. Perceptions of unfairness can be poisonous to any group and need to be addressed urgently.
Fairness and relatedness are linked. When people perceive that they are being treated fairly, they are less likely to perceive the other as a foe.
In game experiments, stronger activation of VLPFC is correlated with likelihood of accepting unfair offers (instead of rage-quitting and getting nothing).
You can generate fairness rewards for yourself by volunteering or helping.
Social status is another primary threat/reward. High social status is correlated with longer life (controlling for education and income) .
Tend to avoid situations that could threaten our status.
Social pain (exclusion, rejection etc) activate same regions of the brain as physical pain. Sticks-and-stones is empirically untrue.
Can game your perception of status by directing focus to an area where you feel better than others.
Giving positive feedback to others increases their feeling of status, leading to fewer destructive threat responses when collaborating. But if can feel like a threat to your own status.
Can also game status by 'playing against yourself' – gain status by being better than your previous selves.
SCARF – status, certainty, autonomy, relatedness, fairness. Social perceptions that are treated as primary rewards/threats, on a par with more direct threats to survival.
Be wary of creating threats to other peoples SCARFs – will lead to irrational responses.
Feedback is tricky. Most popular techniques ignore psychological findings like SCARF. "Let me tell you what I/others think about you" is a sure way to generate a threat response.
When you have a problem with someone, it's natural to want to focus on talking about solutions. This threatens their autonomy and status – feels like telling them what to do. When feedback is often a threat, people learn to avoid giving/receiving feedback. Communication suffers.
Instead, help them come to a solution themselves by putting them into a good mental state. Increase status with encouragement, increase certainty by explicitly stating issues, increase autonomy by stressing that they are the person to figure out the solution. Encourage them to state the problem as simply as possible to reduce load on PFC. Ask questions which focus attention on their thinking processes rather than the problem itself. This is remarkably similar to the teaching methods described in How to solve it. The idea is that math teachers are responsible for teaching not just knowledge, but problem solving strategies, and that they must be careful how they help the student. Eg 'the theorem starts with P' does not demonstrate a useful strategy, whereas 'what theorems can you think of that relate to this problem' does.
In general, when solving problems, focus on the desired outcome rather the problem itself. Creates templates for pattern-matching circuits to look for. Avoids emotional over-arousal from the problem.
Natural to protect your status by defending your own work, missing the opportunity to learn. Managing the perception of status threat is crucial to growth.
Carrot-and-stick doesn't work well with adults – sensitive to manipulation, resentful of attempted control, punishment creates threat response.
Don't try to influence people during away state. Need to reduce threat response first.
Need to direct focus. Carrot-and-stick directs focus to external punishment/reward. Instead try to direct focus to internal motivations.
Instead of explicit rewards, appeal to SCARF. Eg rather than declaring punishments for a problem, tell people about the problem and give them power to fix it – appeals to status and autonomy.
Avoid uncertainty by explicitly bounding discussions beforehand eg 'I only want to talk for 15 minutes and Im not looking for specific outcomes.'
Focus attention on the desired area by asking questions instead of demanding improvement eg 'What is one thing you have done that has made a customer delighted in the past?'. People arrive at their own answers – no threat to status / autonomy. Draws focus away from negativity which could arouse emotional responses.
Be as clear as possible about the desired goal. Head off potential distractions. Eg focus on solution to problem rather than who is at fault.
Need regular attention to maintain new behavior. Set up collaborative processes that involve regularly talking about the behavior eg have a weekly brainstorming session on reducing customer complaints.
Even aside from the content, the book is really interesting in the ways it attempts to make the information actionable. Examples are placed in a familiar narrative rather than an artificial experiment, making it easier for the reader to recall them at the appropriate time. Each chapter ends with a short list of points to remember and actions to try in future (I found that the summaries in eg Mindware were too long to be recalled and mixed information with suggestions – I need chunked information). The book introduces a few core metaphors that everything else hangs off, providing a pre-made mental structure to organize all this new information around.
The content is much less rigorous than some other books and adds a lot of interpretation to the actual evidence. That may actually be a good thing in this context – for research it is important to be very clear about exactly what is known and what is assumed, but for teaching skills it's more important to present a simple, cohesive model. See also 'all models are wrong, but some are useful'. Given the book is aimed at teaching skills it should be judged on how effective it is rather than how accurate it is. So how to evaluate effectiveness…
Things for me to remember:
Why so many predictions fail – but some don't.
Notes are sparse because this is mostly just long narratives.
Invention of the printing press allowed knowledge to accumulate across generations for the first time. Previous methods of transmission (verbal, scribed) were error-prone and degraded over time.
The industrial revolution began largely in countries that had a free press. For the first time in history, per capita wealth begins to increase and living standards rise to match. How strong is the link between printing press and industrial revolution?
Computer revolution produced a temporary decline economic and scientific productivity, as measured by proxies such as research cost per patent filed. Took some time to realize that computers are not magical, and to figure out what they are good for and how to effectively use them.
New fashion for Big Data. Predicts a similar trend – currently hyped but will eventually crash and then finally level out, as we figure out what kind of problems Big Data is good for.
Prediction domains: weather, earthquakes, economy, medicine (2/3 of positive findings fail to replicate). In some of these fields there has been steady progress. In others there has been none. Why?
Availability of more information is not always good. Coupled with confirmation bias, gives more opportunities to fool yourself. Eg the more informed people are about global warming, the less they agree with each other.
New methods of communication spread misinformation just as fast as information – faster if it appeals to bias.
Cannot make objective predictions. Always tainted by perspective. Does not mean nihilism – indeed, pursuit of the objective truth is the vital first step. But the second step is to realize that you perceive the truth imperfectly.
Housing bubble. According to ratings, only 0.12% of CDOs were expected to default. In reality, 28% defaulted. Ratings agencies claimed 'noone could have seen this coming'.
Google searches for 'housing bubble' increased 10x from Jan 2004 to summer 2005. The phrase 'housing bubble' appeared in the new 8 times in 2001, but 3447 times in 2005 ie about 10x per day.
Ratings agencies were paid to rate CDOs, and customers would send more custom to the agencies that gave the best ratings. Clear misalignment of incentives.
A 2005 memo inside one agency simulated a crash of similar magnitude to what later happened, and concluded that there would be no problems.
Fault in their rating model was that it assumed odds of each mortgage defaulting were independent. Roughly true during good times, but not during crashes.
Risk – a danger with known odds eg most gambling games.
Uncertainty – a danger with unknown odds.
Ratings agencies presented systematic uncertainty as risk, which the banks then made presumed safe gambles on.
There have been very similar bubbles in other countries. Using that data for simulation would have predicted the risk.
Crash magnified by huge leverage, ~30x for major banks. Meaning that only need the price to drop by 1/30 to wipe out their safety margin and make lenders nervous.
In March 2007, Lehman Brothers still believed in a housing 'hiccup' and continued to invest.
Federal stimulus justified by a model showing projected unemployment rates with and without stimulus. Reality was far higher than either. If historic accuracy rates for such projection had been taken into account would have been much less convincing, but reality would have just fallen inside the range.
Prediction failures due to using out-of-sample data: * Home owners predicted prices would continue to rise (there had never before been such a large boom in the US) * Ratings agencies and banks failed to predict correlated default rates (they had never rated such novel and complex) * Hardly anyone predicted that a US housing crisis could cause a global financial crisis (the financial system had never been so highly leveraged before) * Economists and policy makers failed to predict the severity of the impact (financial crises causes more severe and long-lasting impacts than over economic crises)
Complex models can exhibit non-linear effects – small mistakes can cause huge mis-predictions.
Large amounts of data can exacerbate over-confidence. Intuitively feels like error should shrink with so much data, but we forget eg model error or sample error.
Political pundits on a talk show make predictions, do no better than random.
Retells Tetlocks research on predicting fall of USSR and other major events.
Foxes and hedgehogs.
Hedgehog pundits making predictions did worse on questions where they had more info. Hedgehogs like to construct stories, and are confident if the story is coherent and fits well. More data gives them more details to cherrypick, making the story fit better and increasing their confidence.
FiveThirtyEight. Only impressive because the bar was set so low. Nate Silvers strategy in general is to pick areas where the bar is so low that improving it is easy. Worth thinking about, in terms of future direction. What areas are sorely lacking in skills that I have?
Principle 1: Think probabilistically. There is noise, and you must account for it.
Principle 2: Todays forecast is the first forecast for the rest of your life. Don't be afraid to change predictions in the face of new information.
Principle 3: Look for consensus. Being the lone dissenter who is proved right is rare. If you prediction is different from that of other similarly informed forecasters, you should worry.
Beware of magic bullets. Reality is complicated. Any model that claims to boil it down to a few variables should be regarded with suspicion.
Quantitative methods are not magical. Don't forget model error and sample error. Unknown unknowns can kill you.
Don't ignore qualitative information. Code it and use it. Some research suggests using a structure approach where possible to enforce discipline eg interviewing candidates is more successful if the interviewer has a short list of attributes and records marks for or against on the spot.
Improving objectivity is hard. Acknowledging that your view is subjective is the first step.
Baseball forecasting. Not just a matter of crunching numbers. Interpreting requires deep knowledge of the game and of player psychology. Scouts are still crucial for adding qualitative information to the pool – demonstrably improving forecasts.
Weather forecasting has steadily improved over time. Non-linear system to increasing computational power has declining returns. Weather Service still uses human input – visual pattern matching for catching and correcting outliers – improves forecasts by 10% for temperature and 25% for precipitation.
How to judge a forecast service. Accuracy – how often is it correct. Honesty – was it the best forecast they could make, or was it tweaked for other reasons. Economic value – does it help people make better decisions.
Some commercial weather services optimize for number of angry customers rather than accuracy eg wet bias – increasing rain prediction from %5 to 20% because people get mad if you say 5% and then they get wet. Can detect this by looking at calibration curve.
Weather forecasts are less and less accurate over time, to the point that by 9 days out they are less accurate than just predicting historical averages. Possibly because of feedback loops in the model itself?
Many local forecast services are significantly worse than the national ones, and they don't care. Claim noone trust them anyway – kind of circular.
Many people refused to evacuate for Katrina. One of the reasons is that many people didn't believe the forecasted severity. Eroding trust in forecasts can cost lives.
L'Aquila – taking scientists to court over failure to predict earthquakes.
No success in earthquake prediction so far. None likely any time soon.
Many examples of prediction systems looking good, but only as a result of over-fitting.
Do know that magnitudes in any given region follow a power law. This would have been sufficient to put small but frightening odds on the Fukushima earthquake happening at some point. Instead they overfit on a few datapoints near the end. Many rationalizations were given for why the model was different from the normal model eg unique rock composition of the area.
This is a common duality. In many system we cannot even theoretically predict individual events, but we can still easily predict stochastic trends.
Grand Forks flood. Predicted water level was just below levees. But uncertainty was not communicated – actual prediction showed 35% chance of topping the levee. Communicating uncertainty is vital.
Typical overconfidence story. Economists asked to make predictions with confidence ranges. Reality is way outside the ranges.
Many well-regarded indicators of economic health did not predict the 2007 recession despite working well for the previous two recessions. Overfitting again?. Also often give false alarms.
Goodhart's law – theory that once an economic measure is proposed it quickly loses it's value, as policy makers begin targeting it and artificially distort the signal. Observer effect.
Economy changes on long scale too. Incorrect models of the 2007 recession were calibrated on recent data, but from 1983 to 2006 the US saw uncharacteristic growth that may not be maintainable by any economy. Also no major recessions during this period. Models were calibrated on an outlier.
Data is not enough. Have to understand what's going on behind the data to know if you are in-sample. No amount of statistical magic would be able to make correct predictions from the 1983-2006 data.
Economic data is noisy too. Frequently gets major corrections years after the fact.
How can we reduce the bias in economic forecasts? Supply-side – create markets for good forecasts eg prediction markets. Demand-side – educate people to be able to judge forecaster accuracy.
H1N1 outbreak in Fort Dix. Predictions that it would be widespread in the next flu season – secretary of health predicted that 1M Americans might die. Mass vaccination program for $180m. That summer, no signs of H1N1 in southern hemisphere. Administration doubles down on vaccination with dramatic PSAs. ~500 vaccinees develop Guillan-Barre syndrome – 10x the rate in usual population. Program shut down. Never any other confirmed cases of H1N1.
Appeared decades later in Mexico with 1900 cases and 150 deaths. Huge reaction followed. But nowhere near as virulent when it hit US – normal numbers of deaths for flu season.
Cannot reliably estimate infection rate / death rate until disease is widespread, at which point it is too late. Forced to extrapolate from early cases. Reported fatality rate in Mexico was likely high because only severe cases were actually diagnosed.
Predictions can be self-fulfilling or self-cancelling when they interact with the system being predicted. Eg pandemic predictions may look alarmist because if they are early enough the pandemic is averted. Eg news reports of swine flu may have increased diagnose rate, exaggerating the real infection rate.
SIR model to simple. Eg serosorting in gay men in SF. Eg Fort Dix has higher R_0 than normal environments for any disease. Eg measle outbreak in Pittsburgh because, while population overall is vaccinated to the point of herd immunity, non-vaccinated people occur in clusters.
SimFlu – agent based simulation. Currently limited to 'modeling for insights' rather than prediction.
All models are wrong, but some models are useful. You have to simplify something to not just end up with a copy of the system.
Anecdotes about a foxy sports bettor.
Bayes theorem.
Sometimes Bayes theorem shows you that the evidence is so huge that the prior barely matters.
False positives. More data = more hypotheses. Possibly correlations between variables increase quadratically with number of variables. But number of true hypotheses is constant. Harder to filter the signal from the noise. Not entirely convinced by this. Big Data also means many more samples of the same variable, which allows teasing out more subtle correlations.
Rise of Frequentism. Fisher contended that the existence of the prior in Bayes rule allowed for bias. Frequentist statistics were supposed to remove that bias, but instead just hit it. Also does not allow accounting for bias in experiment selection.
Bayesian updates from any prior all converge towards truth as samples increase, just at different rates.
Currently starting to see progress away from significance testing as gold standard.
Long discussion of how computers play chess. Key to beating a computer (back when they were beatable) was that they could look further but had worse heuristics, so push them into situations where they choose tactical wins that are also strategic losses.
A/B testing at google. Measure search query quality with survey team and train model on results.
Poker involves approximate Bayesian reasoning about opponents hand. Expert players attempt to exploit systematic mis-predictions in opponents eg confusing 'unlikely' and 'impossible'. Bluffing and aggressive play is on the rise – makes it harder to gain information from your actions.
Psychological challenge of professional poker. Luck has a huge effect, so actual skill is hard to see without many months of data.
Prediction markets and group predictions. Average forecast may be better than individual, but that's not the same as good. Group predictions only work well when predictions are independent (can be feedback loops in predictions markets). Some individuals may be better than the group forecast.
Efficient-market hypothesis. Variants: * Weak. Stock market prices cannot be predicted from statistical analysis alone. * Semistrong. Predictions using only public ally available information cannot consistently beat the market. * Strong. Predictions using even insider information cannot consistently beat the market.
So why does so much trading happen if it's not profitable?
Strong form is empirically false eg members of Congress consistently get returns 5-10% higher than market averages.
Addendums to efficient market – cannot consistently beat the market on risk-adjusted gains after transaction costs.
Bubbles happen. They can maybe be predicted eg by P/E ratios. But making money from them is difficult for technical reasons. "The market can stay irrational longer than you can stay solvent".
Efficient market hypothesis does not hold over the long term, because not enough exploitation of predictable long-term patterns. Traders need to demonstrate short-term returns to keep their jobs/clients.
Fun simulation – assume traders are perfectly rational but overconfident. Results in lots of trading, lots of volatility, strange correlations and below-average returns for active traders.
Trading profits come from "noise traders" – inexperienced, non-professional traders. Like fish in poker. There has to be a sucker at the table.
Of course the sucker doesn't care much in this case, because they don't mind get suckered for 0.5% in exchange for liquidity.
So professionals do make money, but to beat the market you have to be as well educated/trained/resourced as them. You probably aren't.
Similar criticism of MRA as seen in Mindware.
Predictions are much stronger when backed up by understanding of the underlying system, rather than just data.
Huge confidence that greenhouse effect exists.
Expect to see noise in the data. A single datapoint does not outweigh all the other evidence. "It snowed in July, so much for global warming."
Healthy skepticism in climate science is generally directed at computer models. Broad consensus on current state of the climate and on the physical laws, and that the climate will get more energetic. Less agreement on exactly what the consequences are.
Initial condition uncertainty – what state are we in now? Scenario uncertainty – what input variables will change in the future? Structural uncertainty – do we understand the problem correctly? These vary over time and there is a sweetspot around 20 years out where uncertainty is minimized. No idea where this magic number came from.
First IPCC report in 1990 should have sweet spot in 2010. It's predictions are beaten by a model that simply looks at CO_2 levels and temperatures and extrapolates linearly. This suggest that the complexity of current climate models may not be justified.
Problem of how to publicly present these predictions. A scientifically accurate level of uncertainty would not be effective at persuading the public.
Pearl Harbour. Had the data that suggested a possible attack, but were not able to even generate that hypothesis. Similarly for 9/11 attacks. Similar to Superforecasting chapter on how to generate good questions.
Deaths in terrorist attacks also follow power law. Like Fukishima, could have used that to imagine an attack on that scale being plausible. Debate on whether similar analysis suggests that a nuclear or biological terrorist attack should be considered a plausible threat.
Much the same as Superforecasting
While very entertaining, the book is mostly anecdotes. What insights it does contain are a subset of the material in Superforecasting.
The dives into various domains are interesting but not immediately useful. I would have to dedicate time to any one of them to really get a good understanding.
One interesting point is the idea that Pearl Harbour, Fukishima and 9/11 should have been imaginable. This adds to Tetlock and Taleb's debate about predicting black swan events – you may not be able to predict the exact event, but good prediction habits can at least tell you that something like it is possible so you should prepare.
Also interesting to see many domains where the combination of human insight and computer models outperforms either alone. How to effectively combine the two is definitely a subject worth looking into.
Overall, not a lot of insight but maybe worth following up on some of the examples.]]>
Critical tasks are becoming more and more complex, requiring many more subtasks to be completed correctly. A simple checklist can often significantly reduce the error rate of even experienced professionals.
A stab patient in ER almost dies due to massive internal bleeding from his aorta, despite the stab wound being in his stomach. Despite doing almost everything correctly, the ER team almost loses him because nobody remembers to ask what weapon he was stabbed with. A bayonet, not a knife.
A cancer patient's heart stops during surgery. Almost 15 minutes before the anesthesiologist is brought in and recalls that he was given a dose of potassium. High doses can stop the heart, so they fish out the bag and find that the concentration was accidentally 100x too high.
Complexity of medicine has skyrocketed. Doctors and surgeons have many, many more tools and knowledge than before. Often the difficulty is just in correctly applying them. The most experienced, highly-trained experts in the world still make simple errors of omission that cost lives. How can we do better?
Describes the process of reviving a girl who spent half an hour under icy water. Stabilized after two days of work. Went home after two weeks and within a few years made a complete recovery. The entire process is astoundingly complicated and a single mistake, omission or slip of timing would have ruined it all. These cases are rare not because we don't have the technology, but because we don't have the level of perfection it takes to get every single detail right. I am utterly blown away by the complexity described. I had no idea.
Rough numbers – 13000 recognized diseases/syndromes/injuries, 6000 drugs, 4000 medical and surgical procedures. That's the toolkit. Despite intense specialization, in one year an average doctor might see 250 different primary conditions, work around 900 other active problems, prescribe 300 different medications, order 100 different lab tests, perform 40 different office procedures. One of the most common diagnoses is 'Other'.
The average ICU patient requires 178 actions per day. Staff error rate is only 1% – still an average of two per day per patient. 4% of lines are infected within 10 days, with a fatality rate of 5-28% depending on initial condition. 4% of urinary catheters lead to bladder infection. 6% of ventilators lead to pneumonia, with a fatality rate of 40-45%. Overall about half of ICU patients develop a serious complication.
So far the answer has been hyper-specialization eg author specializes in removing cancer from endocrine glands.
Deaths following surgeries at 3x rate of traffic deaths. At least half of deaths and major complications were avoidable in theory.
What do you know when expertise is not enough?
Demo flight of the Flying Fortress crashed and exploded due to pilot error. So much more complicated than previous planes that even the experienced test pilot simply missed step. Declared "too much airplane for one man to fly".
Instead of blaming the pilot or requiring more training, Boeing created the first pre-flight checklist. Single index card with step-by-step checks. FF went on to fly 1.8m miles without a single accident.
Medicine is far too complicated to make step-by-step lists for everything. But do check 4 vital signs.
Reasons for failure. Distracted / tired / forget ie PFC failure. Skip a step that doesn't seem to matter (complication rates may be low, but if lots of people skip stems they multiply). Checklists instill discipline.
Experiment – 5 stage checklist for putting in lines. Trivial stuff, everyone knows it. Nurses observed that at least one step was skipped in more than a third of patients. Nurses now authorized to stop doctors if they skip a step. Line infection rates went from 11% to 0%. Over a year, estimated this prevented 43 infections, 8 deaths and $2m in costs. Adding further checklists for other simple procedures had similarly dramatic effect.
Not just aid to recall – found that often staff didn't know how important some steps were.
Has to have buy-in from every level eg in one case, found that chlorhexidine soap not always available – requires executives to change ordering.
Similar approach to resuscitating drowning victims. Gave checklist to rescue squads and telephone operators and used them as coordinators, so that when the victim reaches the hospital everything is already prepared.
Construction used to have a 'master builder' who coordinated all work. Modern construction is far too complicated for that to work. Common in software forums to hear that software is different engineering. "We know how to build a bridge, they're all the same". In the descriptions here I notice that even building a mall involves an extraordinary amount of complex problem-solving. There are too many permutations on building design for it to be a matter of just plugging numbers into a standard design.
Instead of a master builder, the work is orchestrated by a combination of legal frameworks (building codes) and project-management software. There is a massive dependency graph of tasks which is updated every week and printed out on the wall of the main office. Many of these tasks are actual construction tasks (place flooring, install air ducts etc). Others are communication tasks (eg "contractors, installers and elevator engineers need to meet to review the condition of the cars"). Tools that require code review before merging branches are a simple form of this. I'm not aware of whether large-scale organizations do the same for other tasks eg require that security engineers sign off on design before deployment.
Further specialized tools are used to track blueprints, calculate load, check fire safety. Tool called Clash Detective automatically finds conflicts between designs of different subsystems (eg structural beam passing through light fixture), emails the involved parties and schedules a meeting in the task graph.
Safety checks are not strictly laid out in the legal code – too complex. Instead the code dictates that for each sub-domain a qualified professional must sign-off on a list of checks. Dictates the process but leaves room for expert judgment and innovation.
The US experiences an average of 20 serious building failures per year – a 0.00002% error rate.
Philosophy is to push power of decision making to the periphery and make management (which is part human, part machine) responsible only for enabling coordination and orchestration. __Note that most modern militaries have also gone through this transformation, pioneered by the Wehrmacht.
During Katrina, FEMA was ineffective. Too centralized, field agents had no power to make decision, central office didn't have timely access to information. Resulted in farces like supply trucks being turned back because they weren't mentioned in the plan, and hundreds of buses sitting idle because noone had the authority to decide what to do with them.
WalMart much more effective. Top level concentrated on setting goals and tracking high-level view, as well as encouraging communication between various parties.
Not about private vs government. Plenty of private corps (electrical, phones, oil) fell over while local police and firefighters coped admirably. Difference is about power and communication.
Story of Van Halen and the brown M&Ms – testing the error rate of the concert staff.
Award-winning restaurant that maintains quality by forcing staff to follow printed recipes. Recipes are adjusted and experimented with over time. Staff creativity/expertise is applied to the process, not to the individual actions. Food is tested on the way out to detect mistakes. Scheduled quick meetings to raise concerns, plan reactions to potential problems.
Author worked with WHO to figure out how to improve world-wide surgery rates. 7m per year left disabled and 1m dead after surgical complications.
Huge range of environments from worlds best hospitals to single-doctor operations.
Looked at successful public health interventions. Smallpox vaccination. Tracking cholera to public wells. Common attributes – simple interventions, carefully measured effects, widely transmissible benefits. This is a fantastic list of think about for future research.
Distributing leaflet with instructions on how and when to wash hands in Karachi slums reduced diarrhea by 52%, pneumonia 48%, impetigo 35%. People already washed hands, just not effectively and not as often as needed.
Cleared for takeoff list – simple steps to follow before surgery. Included a little metal tent to be placed over scalpels – removed by nurse after checklist – subtle reminder of the nurses authority to prevent surgery starting. Went from 2/3 of patients receiving antibiotics at correct time to 100%.
Another step added – team briefing. Everyone introduces themselves by name and role, and has a chance to discuss concerns and plans for possible complications. Distributes power/authority from surgeon to whole team. Improves teamwork. Improves reaction times in case of complication.
Lots of iteration with checklist eg first version did not specify it was supposed to be run through out loud with everyone.
Pilots have huge book of checklists for all kinds of situations.
Good checklists are precise and efficient. Cannot teach user, only remind them of what they already learned. This is a crucial point – the checklist is not telling experts what to do, it's helping structure and trigger trained memories.
Can't be too long – rule of thumb is 5-9 items – limit of working memory. Why does working memory matter? Isn't the point of the checklist to offload work from working memory?.
Time also matters – after 60-90s people typically start skipping steps. Focus on the 'killer items' – the mistakes that are most dangerous and most common to skip.
Transcripts of flight accidents show that even under huge pressure, pilot training is sufficient to keep them smoothly running through checklists.
DO-CONFIRM checklist – do the work first, run through the checklist after. READ-DO checklist – carry out tasks as they are checked off.
Checklists are constantly tested and revised using simulators and data from real-life.
FAA is incredibly persistent in investigating failures and finding ways to prevent or react to them in future. 'Mistakes happen' is never accepted as an answer.
Takes on average 17 years for new treatments to be adopted for at least half of US patients. Problem is that knowledge has not been transformed into a simple, usable, systematic form.
By contrast, crash investigators issue huge, dense reports but checklist team spends enormous amount of time distilling and refining this into simple, actionable form.
Simple simulation of WHO checklist in a conference room revealed many errors and rough edges. Followed by several rounds of small-scale testing with specific surgical teams. Gradual rollout.
The checklists were not just imposed from the top down. They were presented as a voluntary measure and there was a constant process of buy-in, feedback and revision. Admins were strongly discouraged from making it mandatory.
Dramatic improvements from adoptees all over the world. Controlled for different times of year, different kinds of operations. Observers present before and after so no Hawthorne effect. Effects were large in both poor and rich hospitals in many different cultures.
If a new medical device could claim the same results as the checklist, it would be a $b invention. But adopting checklists is harder, requires people to check their egos.
Compares to the shift from maverick test pilot culture to modern checklist utopia.
Pabrai Investment Funds. Uses an explicit checklist when evaluating investments. Acts as a check against over-excitement, confirmation bias etc. Built from a list of past mistakes. Lots of basic, painstaking groundwork – very tempting to skip it. This is interesting because it's not even imposed from outside. It's like their past selves imposing discipline on their current selves. Checklist also massively increased efficiency – made it very clear when enough information had been gathered to make a decision. Reminds me of an informal description I gave of my process for getting unstuck. I wonder if I would benefit from formalizing it, to remind me not to skip steps?
Hudson ditching. Media wanted to find a hero, to the point of misrepresenting the facts, but pilot keeps emphasizing the role of the crew and their training. Notes that in the preflight meeting the pilot and copilot can be heard discussing what they would do if there was an accident during takeoff.
Checklist isn't about being told what to do – it's about offloading the routing tasks so that you are free to focus on the parts that humans do best.
Cessna engine failure checklist: #1 FLY THE AIRPLANE. Reminds pilots not to get so focused on restarting engines that they crash into the ground.
Code of conduct for different professions have common core: selflessness, striving for excellence, trustworthiness. But aviation also adds 'discipline', which is rare in other fields.
Systems thinking. It's not enough to optimize only the parts (experts, tools, materials). Have to optimize their interactions, and the system as a whole. Collecting data and studying failure is such a basic step yet happens in hardly any fields.
Author describes lives saved by simple checks in his own practice.
I don't know where to start. This is not a book about checklists. It's a book about building complex systems out of human parts, and about the role of discipline and systems thinking in preventing catastrophic error.
It has many examples of distributed cognition that I wasn't at all aware of. I was especially struck by the construction example, where the traditional top-down organizational structure is replaced by many autonomous groups tied together with a semi-automated coordination system. This is sci-fi level stuff and I've never even heard about it. Now I want to find out more. I'm sure other complex engineering fields (eg aerospace engineering) have similar systems.
A good chunk of the book focuses on the challenges of adoption and the various measures that helped or harmed the introduction of checklists in medicine. It sames like one crucial aspect in all cases is that the external system is not seen as imposing control on the people, but as being a helpful assistant that they can choose to make use of or modify. There are definitely crucial questions to address about who gets to change the system eg in the construction case, who is allowed to say that a certain kind of check is no longer needed in future?
The many uses of checklists remind me of sections in previous books on Fermi equations, cost/benefit analysis, priority lists etc. The common theme is that imposing external structure helps improve cognition by preventing mistakes that happen without conscious awareness, whether through lapses of attention, cognitive biases or bait-and-switch. An even simpler example is the various exercises at Clearer Thinking, which generally just ask a series of questions. It's normally easy to put hard questions out of mind or ignore inconvenient facts without even really being aware of it, but much harder when it manifests as a blank text box on your screen. Since we don't have access/introspection to System 1, these techniques all help by externalizing the thought process into a place where it can be checked, debugged and improved over time.
Many of these systems also help by just moving load out of the conscious mind (Mindware has a lot to say about the limitations of the prefrontal cortex). Some procedures can be trained into System 1 (eg physical movements, simple acronyms) eg I notice safety checks for both shooting and climbing tend to involve physically touching instead of just looking, making the movement easier to build into unthinking habit (when I dream about climbing, I dream the safety checks too). Can think of checklists as solution for procedures that System 1 doesn't do a good job of learning.
Lastly, the subject of discipline and professional responsibility. It's a touchy subject in programming, which still has a hero approach in most places. I notice that the programmers are happy to adopt elements of this that feel like automation rather than external control – continuous integration, code review bots, issue trackers etc – but there is much less discipline on the more human side of things. No checklists for the design process, no standard code of security procedures etc. We're happy to automate, as long as it's done with code and not with a list of rules. Again, questions of power and control probably come into play there, but there is also the problem of ego and identity as experts.
Rereading this book has started me thinking about how to build such systems. It's not enough to just provide the software, you need a systems-level approach to the entire problem, from communication and interfaces to psychology and politics. I guess the place to start is by surveying success and failures – this may be the next step after I finish the next few weeks of cramming.]]>
Why are some experts good at making predictions when others fare no better than chance?
Introduces Tetlocks research. Often misinterpreted as 'experts cant forecast' but actually more an investigation into why some experts can while others cant.
Accurate and well calibrated forecasting is important. Will Iraq deploy WMDs? Will bailing out banks improve unemployment rates? Should I invest money in the stock market? We rarely score experts in this field – when we do, they are often perform no better than chance.
Contrast Laplaces demon vs Lorenzs butterflies – clearly some aspects of some systems are inherently unpredictable over time. Just as clearly, most aspects of daily life are very predictable. We are rarely surprised by minute-to-minute events.
Question is how close to the theoretical limits do we get, and how can we do better.
Equally interesting, how well calibrated is our confidence ie do we know when our predictions are good?
Optimistic skeptic – perfect forecasting is not possible, but we can do much better than we do right now.
Tetlock ran the Good Judgment Project. >20k volunteers making predictions through website. Demolished everyone else in IARPA competition, including the intelligence agencies.
This is the reason I have high confidence in the main ideas of this book – as well as seeming truthy and being based on research, they have been tested on difficult problems with a wide variety of people and produced excellent results.
Point of the book is that foresight is a real, measurable skill. It can be taught, and reasonable improvements can be made with fairly minimal investment. Only real problem is overcoming human psychology – people are overconfident in their own predictions and so averse to being wrong that they will sabotage their own measurements by retroactively re-intrepreting predictions.
Tetlock makes a prediction – the best results will be achieved by blending computer-base forecasting and human insight. Neither will supplant the other.
Focuses on human biases that make discovering knowledge hard.
Until the mid-20th century, the field of medicine was full of confident experts who had years of expertise and anecdotal evidence that there treatments worked. They turned out to be mostly wrong and, on average, more likely to harm then help.
Rise of evidence-based medicine in the 50s, pushed heavily by Archie Cochrane. Heavily opposed by doctors who saw it as a challenge to their expertise. They felt like they were already experts and didn't need any external validation / criticism, even though evidence showed that many of their treatments were useless or worse. Especially resistant to trials, which they saw as unethical when they already 'knew' what the most effective treatment was.
Similar stories in politics even today – vicious debate over effectiveness of various policies but with little validity. Humans tend to feel like they know what they are talking about.
Suffering from the illusion of knowledge.
This is going to be a common theme through all of these notes – the importance of escaping naive realism / what-you-see-is-all-there-is. Feeling right is not the same as actually being right, feeling confident does correlate well with successful predictions, the map is not the territory etc.
Discusses System 1 vs System 2. Covered better in other books, but in short: System 1 is unconscious, fast, effortless, automatic and associative where System 2 is conscious, slow, effortful, sequential and logical. System 1 is the default mode of thinking and runs most of our life but has a number of systematic heuristics/biases/flawes. Since it's below conscious introspection we can't notice these directly, only by observing large numbers of mistakes. Activating System 2 takes conscious effort and is tiring, so is only activated when we realize that we need it.
Similarly, because the decisions made by System 1 are below conscious perception we aren't aware of the reasons for them. Various ingenious experiments demonstrate that, rather than being aware of this lack of introspection, people will generate theories from scratch and believe them to be the original reason ie post-hoc rationalization of System 1 activity.
Main source of naive realism seems to be believing uncritically the data passed up to conscious attention by System 1. Explains why cognitive reflection, mindfulness etc are useful. Similarly, there needs to be some word for the corresponding illusion where people believe that they have full access to their own thought processes when in fact that perception is generated post-hox.
The Cognitive Reflection Test is a series of questions that test how good the testee is at activating System 2 at appropriate times ie noticing when System 1 is likely to make mistakes.
Same section connects the urge to rationalize post-hoc with overconfidence in general, arguing that both are related to confirmation bias. We uncritically believe first theory that fits and stop looking for alternative explanations.
Remember: confidence == coherent story, not confidence == accuracy.
Bait and switch – System 1 often replaces hard questions with easy questions without the thinker noticing. Many biases fit this pattern eg availability bias replaces 'how likely is X' with 'how easily can I imagine X / how many instances of X can I remember' – reasonable heuristic in many environments but not always correct.
Tetlock coins 'tip-of-your-nose perspective' for the subjective reality created by System 1.
Note the message is not 'System 2 is better than System 1'. System 1 is fast, automatic and effortless. Makes it very good at pattern matching. The question is when should we trust it? Experiments show that in some fields the intuitive hunches of experts are eerily prescient, recognizing patterns in data that they aren't even able to process consciously. But in other fields the intuitive hunches of experts are no better than random. In both cases the subjective feeling of confident knowledge is the same. Want to figure out what makes the difference, and whether it is something can train.
Discusses the importance of making concrete, testable predictions and checking the results.
Most predictions are so unspecific that it is hard to judge after the fact whether or not they were correct. In some cases this is deliberate – leaves pundits wiggle room to avoid embarrassment.
Being wrong is unpleasant. Most people, consciously or otherwise, optimize for avoiding appearing wrong. Better to optimize for avoid being wrong. In the latter frame of mind, discovering that you are wrong is a welcome mistake – you get the change to be less wrong.
What makes a good forecast?
It should be completely unambiguous ie it must be easy for observers to agree later on whether or not you were wrong. Ambiguity leaves room to fool yourself. Some prediction sites require each prediction to have a referee, which seems like a useful way to keep yourself honest.
It should have a concrete finish date. Predictions like 'unemployment will go down after stimulus' leave too much room to spend years saying 'any minute now'.
It should be probabilistic. For predictions to be used as the basis for decisions it is important to know what level of confidence is expressed, and to calibrate that confidence it is necessary to practice.
It should use specific numbers for probabilities. Tetlock gives various anecdotes where terrible mistakes were made because different people did not assign the same meaning to phrases like 'There is a serious possibility that…', with interpretations ranging from 20% to 80% chance in one case. Also, non-specific language again creates room to wiggle out of wrong predictions.
You should make lots of predictions. Given partial knowledge and probabilistic events, there is no way to judge if your '70% chance of rain' was misinformed or just unlucky. With large numbers of similar predictions we can begin to judge accuracy and calibration.
Well-calibrated: predicted probability == observed probability, across all ranges from 0% to 100%. Overconfidence: prediction probability > observed probability. Possible to be eg well calibrated between 20% and 80% but poorly calibrated at extremes, or vice versa.
Turns out later that it's sometimes good enough for the shared information to be high. The GJP actually re-calibrated the combined judgments of its participants to achieve better results. If you find your System 1 is systematically overconfident in some domain, System 2 can remember to assign lower confidence to those predictions.
Resolution: how extreme your predicted probabilities are. The reason we value this is that it's easy to be well-calibrated by lumping many classes of events together and taking the average rate. A weather forecaster who makes predictions using the seasonal average will be well-calibrated over time but not as useful as one who manages the same calibration while taking current conditions into account. The latter can achieve better resolution.
Tetlock uses the Brier score to score predictions. This is a scoring rule with the useful property that the maximum expected score is given by predicting the actual probability of the event (rather than eg deliberately extremizing your prediction to score better) so the optimal strategy is to give honest predictions instead of gaming the scoring.
I wonder if a better measure would be shared information between event and prediction. This takes into account calibration and resolution, and also the probabilities of different event classes eg we could look at shared information over forecasts in general, or focus on shared information given that the forecaster predicts tornadoes. Is this the same as logarithmic scoring?
Also have to compare scores to other forecasters on the same problems. Some predictions are easier to make than others, so the only fair comparison is to use the same problems across the board. Also compare scores to simple strategies like 'same weather as yesterday' or 'same weather as this day last year' to see how much value the forecaster is actually adding.
Various experiments run with experts in different fields. Two distinct groups emerge. One group does no better than random chance (and sometimes worse). Other group does slightly better than default strategies like 'predict no change'. Not amazing results, but clearly some foresight exists.
Notable difference between the groups: those with no foresight were dogmatic and convinced of a few Big Ideas whereas those with some foresight were more pragmatic, drew on a range of conceptual tools and weighed many perspectives. Named these groups hedgehogs and foxes after Archilochus "the fox knows many things but the hedgehog knows one big thing". Foxes won on both calibration and resolution ie they weren't just playing it safe, they were better at identifying when they had reason to be confident.
The explanation given is that filtering everything through a strongly believed Big Idea exacerbates confirmation bias, increasing confidence while at the same time filtering out important evidence.
It's not clear in this book how rigorously this was established or how well-replicated it is. TODO find out…
Hedgehogs are much more popular across the board. Media likes bold predictions and simple theories. Politicians are frequently quoted as wanting to hear confidence. People dislike uncertainty.
This theme will be repeated later in unrelated books – uncertainty is known to be a huge stressor. There are suggestions on ways to make it more tolerable. Intended for managing stress, but would also be useful for making oneself less likely to embrace false confidence to escape uncertainty.
Wisdom of the crowds – average group predictions are often more accurate than individual predictions. Tetlock stresses that there is no magic here – it is simply a result of combining the perspective of many people to make an artificial fox. How well this works depends on how much accurate information there is to aggregate. If the crowd has no information then the aggregate result has no information.
Advice is to behave like a crowd yourself. Aggregate information from as many different sources and perspectives as you can.
No guidance on how to weigh different perspectives. Nor is it clear whether the exercise by itself is enough to improve results or whether the habit is itself a symptom of a particular kind of mind. Later shows that training in prediction in general is effective, which is weak evidence that training in foxiness would be effective.
Aims to demonstrate that there is some element of skill to forecasting, not just luck.
Notes a common bait and switch – replace "was this a good prediction" with "did this have a good outcome". Pernicious in politics where even if the speaker doesn't believe, it will still convince the audience and be a useful attack.
IARPA competition was started to improve decision making in US intelligence agencies. Want to know what methods actually produce results, as opposed to just seeming truthy. Tetlock compares this to the rise of evidence-based medicine ie applying the experimental method to intelligence work. What other fields could benefit from similar apporaches.
Competition aimed to focus on problems which were hard but possible. Acknowledged clear theoretical limits to what humans can predict and focused on improving in the areas where we can make predictions in theory but fail in practice.
Good Judgment Project took the combined predictions of thousands of volunteers. Averaged the predictions but weighted successful forecasters more heavily. Extremized the result, because this produced better results in practice – Tetlocks plausible explanation is that combining disparate information should increase confidence, not just produce the average confidence.
The Good Judgment Project beat everyone, including the intelligence agencies who had access to classified info.
Even more amazing, some of the individual members of GJP alone beat the intelligence agencies.
Tetlock studied 58 of the top forecasters, who beat the average Brier score by around 60%. Many of them did not regress to the mean over the later years of competition – in fact, on average their lead over the other contestants increased. To test if this effect was caused by singling out the superforecasters and putting them on special teams, they repeated the experiment with later generations of forecasters and observed the same effect. I'm not clear on how that actually rules out the effect of special teams, since they put the later generations on special teams too.
Overall the correlation between individual scores for all forecasters from year to year was around 0.65, which is sufficiently high to indicate that there is some real skill involved and not just plain luck.
Argues that raw intelligence is not the main contributor to success in forecasting.
Note attributes common to the superforecasters: intelligent, wide range of general knowledge and modern affairs, often highly numerate (even though most used little math in their process). Hypothesis – numeracy is correlated with placing trust in data over intuition, and with repeated exposure to being clearly and inescapably wrong.
Intelligence – on both fluid intelligence and crystallized intelligence (aka knowledge) tests forecasters score ~70% above general population, superforecasters score ~80%. Intelligent, but not unusual genius. Tetlock postulates declining returns on both intelligence and knowledge, explaining why volunteers were able to beat professional intelligence analysts. Would have liked to see the graph of intelligence/knowledge vs score to see if it does indeed level off at the top.
Also common themes to their problem solving process. Fermi estimate –> outside view –> inside view –> synthesis –> loop.
Fermi estimates – rather than just guess, break a problem down into its parts until you reach numbers that are easier to guess accurately. Prevents bait-and-switch by concentrating focus onto the structure of the problem. In probabilistic questions, enforces the correct use of the laws of probability which System 1 systematically misuses.
Gives a long example of using Fermi estimates to predict whether polonium would be found when testing Yasser Arafat's body, showing how it catches several bait-and-switch moments eg replacing "will polonium be found" with "would Israel poison him".
Outside view – aka reference-class forecasting – before focusing on the details of the current problem, first obtain a base estimate by averaging over many similar problems. Thought to counter anchoring – a (poorly replicated) bias where guesses can be heavily influenced by completely unrelated numbers. Effective at reminding oneself to account for priors / base-rate. Also perhaps a counter to story-oriented thinking / inaccurate stereotyping?
Gives an example with a long description of a family followed by asking to predict whether they have a pet. Common responses are led astray by the ethnicity of the family and the number of the children. Superforecasters instead start by finding out what percentage of families in the area have pets and then adjusting from that base estimate if necessary.
There are inherent difficulties in how you pick the reference class eg should our reference class be American families, or Italian-American families, or Italian-American families within this neighborhood, or Italian-American families with this income bracket etc. It's very easy to construct problems where two seemingly valid reference classes for the same event produce wildly different base rates. I currently have no good models/techniques for choosing reference classes.
Inside view. Finally we use the specific details of the problem to adjust the base rate. Have to be sure not to double-count features we already used for our reference class eg if the reference class was families-with-children, can't reuse the children to adjust the base rate.
Both the outside and inside view get applied to the individual components of the Fermi estimate. Can also use outside view of the problem as a whole to sanity-check results.
Synthesis – finally, try to find other perspectives. This is a counter to confirmation bias and what-you-see-is-all-there-is – trying to account for unknown unknowns. Get fresh perspectives from other people if possible, but even deliberately trying to generate alternate perspectives yourself is useful. Research on creativity and out-of-the-box thinking may be useful here for generating fresh ideas.
Loop – keep going until you are done. No mention of how superforecasters judge when they have generated enough perspectives, viewed enough data etc. Typical overconfidence would lead to finishing too early.
No surprise – superforecasters score highly on the Cognitive Reflection Test.
Notes that superforecasters score highly on tests of need for cognition, openness to experience and active open-mindedness. No magic here. Good tools for countering known biases but using them does not guarantee improved predictions. You have to actively want to find the truth more than you want to avoid looking/feeling wrong.
Bumper sticker summary:
For superforecasters, beliefs are hypotheses to be tested, not treasures to be guarded.
Discusses probabilistic reasoning.
Various examples where media and the press portray confidence as heroic and probabilistic estimates as hedging/dissembling.
Some plausible evolutionary psychology just-so stories about why this happens: Probability of risk requires being on alert which is physically draining/damaging. Better to just switch off at low values of confidence. Somewhat aligns with the fact that uncertainty is known to cause stress responses. Alternative just-so story seen elsewhere is that false confidence is useful for convincing other highly-evolved lie detectors – if you don't even know you are lying about your confidence then you won't give off subtle signals for others to catch you out.
Easy to demonstrate that people suck at probability. Bad at updating priors. Bad at conditional probabilities. Confuse probability with scores – if >50% it should happen. A flaw I've noticed in a lot of otherwise numerate people is confusing the map with the territory – believing that probabilities exist in the world itself rather than being a property of ones partial knowledge of the world. This is the underlying flaw exposed by the Monty Hall problem.
A useful model is to assume that people only have a few settings along the range from impossible to guaranteed. Forecasters who use more settings on the dial (going from nearest 10% to nearest 5% or nearest 1%) are more accurate. Rounding up the answers of superforecasters to the nearest 5% reduces their score, whereas regular forecasters don't suffer much from rounding even to the nearest 20%, indicating that the granular answers of the superforecasters does actually reflect real information.
Another explanation of why many people struggle with probability is that it runs counter to their story-oriented view of the universe. People believe in fate and define their identities by the unique events that shaped them. There is a correlation between prediction accuracy and belief in fate / meant-to-happen thinking. Not clear that this result is related to probabilistic thinking. Could be explained instead by the earlier result that confidence == coherent story – perhaps people who don't think in stories don't suffer that bias as much.
Discusses prediction updating over time.
Rebuts the idea that the superforecasters did better simply by putting more time in than the others (consuming more news and updating predictions more often). Already know that superforecasters don't have substantially more general knowledge than other forecasters. Superforecasters initial forecasts were still 50% more accurate than other forecasters, so they still would have won without updating. Not a solid rebuttal – superforecasters clearly do spend lots of time researching their first forecast too.
Discusses 'belief perseverance' – people make beliefs but can be extremely resistant to updating them in the face of fresh evidence. Also under-update ie make less of a change than the evidence demands. Beliefs are more likely to be fixed when they are closer to our identity eg in a domain that we feel expert in. See keep your identity fluid.
Dilution effect – being presented with irrelevant information can weaken our confidence in relevant information. Kinda like the availibility heuristic – replace "what is the confidence" with "how much of the evidence supports it".
Superforecasters make many small updates to their predictions. While many are aware of Bayes theorem, few of them use it explicitly. Would have liked to see experiments on how well they can apply it eg the false positive problem. I suspect that even the forecasters who don't know Bayes theorem explicitly would do well, otherwise how are they making accurate updates?
Discusses how to practice.
Brief interlude about growth mindset.
Practice doesn't always lead to skill gains, otherwise all our experts would be good forecasters. What makes practice effective?
Forecasters who read the booklet prepared by Tetlock and practiced forecasting performed better than forecasters who only read the book and forecasters who only practiced. Not surprisingly, practice is much more effective when you have an idea of what you are aiming for.
Effective practice also needs clear and timely feedback. Ambiguous feedback makes it harder to learn what you are doing right or wrong. Slow feedback gives you fewer opportunities to correct mistakes. This is supported by research on the accuracy of expert intuition in various domains – experts in domains where feedback is poor (eg financial pundits wait a long time to find out if they are right or wrong) are no better than laymen.
Hindsight bias – believing after the fact that the correct decision was obvious – is a major impediment eg experts who made predictions about the fall of the Soviet Union were asked to recall their estimates much later, and on average recalled a 31% higher confidence than they actually gave at the time. If you cannot acknowledge being wrong then you are not actually receiving feedback.
Anecdotally, the superforecasters were more likely to share postmortems and to credit some of their success to luck rather than foresight.
Mentions, without references, that research shows that good calibration doesn't transfer between fields. Practice must be done in the field in which you want to be an expert. TODO track down the reference.
Superforecasters traits:
Notes that not all forecaster has every trait. 'Perpetual beta' is the strongest predictor of success, 3x stronger than intelligence. Defines perpetual beta as 'the degree to which one is committed to belief updating and self-improvement' but does not say how that is measured
It's not clear to what extent all of these are supported by the research vs anecdotal evidence, especially given as I was dubious of some of the earlier lines of reasoning. TODO find the actual research.
Shifts focus to making predictions as a team.
Discusses the Bay of Pigs disaster. Rare consensus on all sides of the political spectrum that the plan was clearly awful. Independent analysis supports that it's not just hindsight bias.
Lead to the coining of groupthink – "members of any small cohesive group tend maintain esprit de corps by unconsciously developing a number of shared illusions and related norms that interfere with critical thinking and reality testing." ie the crowd is not always wise.
Forecasters organised into teams with advice to practice "constructive confrontation" and warned about groupthink. Advised to replace attacking with precise questioning. In effect, asking for the reasoning process behind the claim. Similar to reasons for using Fermi estimates in the first place.
Teams worked cooperatively but made their own predictions. Teams were 23% more accurate than individuals. People who scored highly enough to be placed on superforecaster teams increased their accuracy by 50%. Comparisons like these in the book vary terms – I'm assuming that they all mean Brier score.
Compared teams to commercial prediction markets. Forecaster teams lost by ~20% but superforecaster teams won by 15-30%.
There was a correlation between the AOM of the team and it's accuracy. How do you measure a teams AOM?. The AOM of the team was not just a product of the AOM of it's members, but also of the group dynamics that emerged.
Went through team interactions and categorized people as givers, matchers or takers depending on how much they contributed to the group vs how much they gained. Teams with givers were more successful. Not idea what this is measuring – at a guess improvement in score?.
Declares that "diversity trumps ability". I dont see any mention of evidence.
Overall, this chapter is not hugely convincing. Feels tacked on.
Near-universal agreement that (appearance of) confidence is vital for leadership. But false confidence leads to bad forecasting!
Discusses the shift in military organization begun by the Wehrmacht, from rigid hierarchies to pushing decisions down the chain of command. Communicate information and goals, not orders. Allows correctly responding to new circumstances without needing to wait for new orders. Allow uncertainty about situation but not about decisions.
Distinguishes intellectual humility (maintaining healthy doubt of your own ideas) from self-doubt (doubting your own abilities / attributes). Effectively, recognizing that uncertainty is unavoidable, not a sign of personal flaws.
Following the theme of the book, Tetlock includes criticism from other researchers.
Debate with Kahneman. Kahneman believes that bias is essentially unavoidable. Tetlock believes that it specific contexts humans can be trained to reduce bias. They worked together to test superforecasters for various biases.
Scope insensitivity – basically forgetting to account for variables. In this experiment, ask forecasters to make same prediction but give them each different time frames. Scope insensitivity predicts that the predictions will not vary enough between the different time frames. Superforecasters showed fair resistance to this bias (giving 15% odds if asked about a three month period vs 24% odds if asked about a six month period).
Postulates that superforecasters run thought experiments, varying variables such as time frame, and that this makes them consciously aware that they have to account for those variables. No evidence, but this seems like a useful habit anyway.
Also postulates that superforecasters are so practiced in engaging System 2 that it comes naturally. Doesn't explain why calibration doesn't transfer across fields. Perhaps a finer-grained theory is that they are know which factors are likely to trip them up in their specific field?
Nassim Taleb contends that forecasting is not as useful as it seems because noone can even imagine, let along predict, the Black Swan events that make the largest impact. Tetlock contends that many Black Swan events were imagined in advance, and that there are also plenty of predictable but important changes that shape the world just as much. More importantly, while the occurrence of some events are not predictable, the GJP shows that the consequences often are and this can be important for preparing for them.
A more compelling rebuttal from a different book – knowing the distribution of earthquake magnitudes allows predicting a 1/300 chance per year of the Fukushima earthquake. That doesn't tell you whether it will happen, or when, but it does tell you that it is likely enough to be worth preparing for.
Biggest impediment to accurate forecasting is that making predictions is more often about advancing the interests of the predictor than it is about reaching for the truth.
Sees some promise in the fact that some fields have transformed entirely despite huge opposition. Was not inevitable, but was achievable through persistence. Evidence-based medicine is well established. Evidence-based policy is being introduced in some countries. Sabermetrics is well established, and more generally sport science is now well-regarded.
Discuses elements of good judgment other than good forecasting. Primary is good questioning – how do you decide which prediction problems to think about in the first place. This relates back to the Black Swan criticism – how do you avoid getting blindsided?
Wonders whether good forecasters also make good questioners, or whether generating questions comes more naturally to hedgehogs with Big Ideas.
Holds out hope for resolving partisan disputes by forcing both sides to work within a prediction framework so that over time their claims can be evaluated. Compares this to the scientific world, where progress is eventually made because both sides of a debate are forced to play by rules that allow evidence to accumulate.
The ten commandments of forecasting.
It's not surprising that a book which praises foxes ends with a list of tools and suggestions rather than a list of Big Ideas. Notice the number of commandments which are about balancing different ideas.
The appendix and the list of traits do a good job of summarizing the core lessons of the book. The methods seem well worth adopting, between the empirical evidence and the convincing alignment with other cogsci results. I'm not convinced by all of the explanations as to why the methods work, and there are a number of places where I want to follow up the references to see if they are more convincing.
The later chapters on teams and leadership appear much weaker. They are delivered with just as much confidence but, unless I missed some, far fewer references.
I'm particularly interested in the debate with Kahneman about whether or not these results reflect real changes in superforecasters abilities to overcome cognitive flaws, since this bears directly on the question of whether training/education can increase effective intelligence. The fact that calibration doesn't transfer across fields is a poor sign.
Worth contrasting with The Signal and the Noise which is more reliant on anecdotal evidence but goes much further into exploring what kinds of events are predictable.]]>
Proposing 'libertarian paternalism' – designing choice architectures to influence people to make better decisions for themselves.
Libertarian paternalism. Paternalism – want people to make decisions which lead to best outcomes, as judged by them. Libertarian – don't want to restrict freedom to choose for self. The libertarian part is interesting as an escape hatch – if a particular policy is really bad it can be averted by informing individuals rather than waiting for slow moving government to catch up.
Choice architecture – context in which choices are made.
Nudge – any aspect of choice architecture which would influence a human but not an Econ ie does not significantly change incentives but does change behavior through irrationality or bias.
There is generally no neutral context, no way to not influence choices. If any action is a nudge, you have a responsibility to consider the effects. For example, a common nudge is the choice of default decision if the person in question abstains, forgets or can't be bothered. Should set a default that benefits the person.
Much focus on nudges in government, because there are many important nudges that the government is in the best position to influence eg healthcare.
Not for bigger government, for better governance. Already bipartisan support for nudges in savings.
People are willing to pay other people to help them make better decisions (eg support groups for quitting smoking) – evidence that they do not have full free will over all of their actions. Nudges interact with the portion that they don't control, so they don't reduce freedom to act.
Popular libertarian strategy is to maximize choices. This only works when the chooser is well informed, rational and attentive. Can also cause choice overload, resulting in worse decisions.
Alternate names for System 1 / System 2 – automatic system and reflective system. Not nearly as good as Albert and Rex.
Typical list of biases and experimental evidence. I notice that some of the examples given are effectively using nudges for Evil.
If snacks are in reach you are likely to eat them. You might be grateful if someone helpfully took them away. An Econ would never benefit from having a choice removed.
Dynamically inconsistent – preferences which do not agree over time. Right now I want to wake up early. Tomorrow I will likely want to sleep in. Behavior like this cannot be understood in terms of a single set of preferences.
Another alternate set of names – Planner vs Doer. Albert and Rex! Memorable characters stick in peoples heads. Planner and Doer do not get along. Planner sets you alarm clock for 6am. Doer turns it off and goes back to sleep. Doer is the main victim of nudges. Also Doer is sneaky. Can often convince Planner that it was Planners idea all along.
Reasons why Doer often wins. Temptation – call a choice tempting if we are more likely to make it when in the heat of the moment (hot) than when thinking ahead (cold). Mindlessness – much of the time Planner is asleep at the wheel and the irrational Doer is making the choices. Generally not aware of mindlessness either.
Many strategies for Planner to influence Doer. Pre-commitment – remove choices in advance. Social pressure, incentives – speak Doers language.
Mental accounting – fungible assets often partitioned into multiple accounts eg rent money, food money, vacation money. Doesn't make sense to an Econ, but acts as an aid to self-control. Understanding this is helpful for many nudges.
Social pressure is powerful eg obesity is contagious. Can divide into conflating popular choice with safe/optimal choice vs worrying about negative opinions if not conforming. Can use for nudging eg "Don't mess with Texas" campaign significantly reduced littering.
Conformity experiments. Actual perceptions can be overridden by majority opinion. Strongest when opinion is unanimous – even single dissenter can weaken the effect.
Herd behavior, winner-takes-all effects, fashion effects – rerunning popularity contests (eg choose favorite song under peer influence) can produce random winners. A single confident confederate can sway the entire group. Arbitrary preferences are internalized and can persist even years later. Appears in voting where noone wants to waste a vote by voting for someone that will not win – if you can create a perception of one candidate winning you may increase their votes.
Has interesting implications for public debate. Lacking concrete evidence, simply repeatedly and confidently stating your position could have huge effects.
Pluralistic ignorance – often widespread ignorance of other peoples thoughts/opinions. Explains how unpopular regimes can be stable for a long time, and then suddenly crumble if some stimulus causes everyone to realize that everyone else also hates the regime.
Heavily influenced by peers – implies you can make major changes to your own life by carefully choosing your peers. This is a good part of why I'm considering a postgrad course in cogsci – I don't think the lectures and material add much value over reading alone but being surrounded by other students and staff may well be valuable.
Strategies: information, peer pressure, priming.
Information – people influenced by group norms. If their view of group norms is inaccurate, can influence their behavior with correct information eg heavy drinkers hang out with heavy drinkers so have a biased view of normal drinking levels, giving them correct stats reduces their drinking. Both extremes move towards the norm, so may want to selectively target people if you only want movement in one direction.
Peer pressure – concerned about other peoples opinion. Spotlight effect – believe that people pay more attention to than they really do. Seen elsewhere – choosing popular students to promote new behavior is much more effective than choosing well behaved students.
Priming – subconscious influence by spreading activation eg posters with eyes make people behave better, as if they were being watched. Mere measurement effect – simply asking someone whether they will perform an action makes them more likely to act in accordance with their answer eg asking people if they are going to buy a car increases buying rates. From the description, presumably the people who answered no were then less likely to buy a car?
Nudges are most important for decisions that people get wrong. What attributes make decisions hard?
Requires self-control – Planner struggles.
Difficulty – requires a large investment of time/effort to make the correct decision.
Infrequent – practice makes perfect, but many important decisions happen rarely in a lifetime eg buying a house.
Slow feedback – long time between decisions and feedback makes people less likely to make the connection.
Unclear feedback – not clear whether or not a good decision was made eg you got fat – which of the thousands of things you ate in the last few years contributed?
Complex – can be hard to map choices to futures.
Lack of self-knowledge – even knowing how choices map to futures, humans are poor at predicting which future will make them happy.
Free markets are not a panacea. Ways they can fail people:
Insufficient competition – monopolies or collusion tend to lead to abuse.
Lemon markets – if people cannot sufficiently judge quality they tend to get ripped off.
Irrationality – there is an incentive to exploit any bias, irrationality or lapse in self-control.
Phishing for Phools takes this further and argues for a corollary of the Efficient-market hypothesis – that where human flaws exist, they will be exploited.
Stimulus-response compatibility – if stimulus/affordance does not lead Rex to correct response, Albert has to intervene, reducing performance and accuracy. Eg big red go sign, push door with pull handles.
NUDGES – iNcentives, Understand mappings, Defaults, Give feedback, Expect errors, Structure complex choices. TODO come up with an acronym that I will actually remember.
Incentives. Who uses? Who chooses? Who pays? Who profits? When incentives are misaligned, people get screwed. Incentives might also be out of sight – salience can be improved by putting information on display eg increasing price of electricity will be more effective if accompanied by regular reports of how much each appliance is costing the owner.
Understand mappings. Map choices to things the user values eg medical choices can be mapped to costs, outcome stats, inconvenience. Use units that are familiar in the context eg ml of alcohol vs pints of beer. RECAP – record, evaluate, compare alternative prices – proposed regulation that would require companies to export pricing and usage info in a form that comparison websites can use to make recommendations. This is by far the most interesting suggestion so far – it doesn't clash with ideals on either side and it would be politically difficult for companies to object to.
Defaults. Often not changed, so provide powerful nudges. Required choice – avoid default, insist uses chooses something. In absence of knowledge, choice is unhelpful – do I want my monitor to run at 60hz, 70hz or 80hz?
Give feedback. Acknowledge actions. Warn about potential errors – but be careful of false positive exhaustion. Make things easy for Rex to learn eg provide affordances, don't move buttons around, prefer regular schedules (eg birth control packs include placebo pills for days when no hormones should be taken). Make sure that feedback is actionable eg if input is wrong, explain what needs to be corrected and how.
Expect errors. Try to make incorrect actions impossible, or handle them correctly anyway. Warn about common errors, and notice if they occur. Reduce the impact of errors. Post-completion error – once main goal is accomplished remaining tasks more likely to be forgotten – if necessary delay completion eg ATMs wait till card is taken before handing over cash. I have occasionally taken the card and walked off without the cash.
Structure complex choices. Provide a choice process. Elimination by aspects – pick most important attribute, pick cutoff values, remove all options that fall outside cutoff, repeat until number of choices is manageable. ML recommendations are good example, but beware of filter bubbles.
US personal savings rate at lowest since Great Depression. Most people do not save enough to retire safely.
Should we interfere with their choices? Surveyed employees mostly responded that they 'should be saving more'. In many cases they are literally giving up free money. They spend little time on the decision of how much to save. Typical savings amounts are often round numbers or equal to the maximum employer matching rate, indicating that little thought/planning went into the decision. Interventions that increase savings rate see no increase in dropout rate. So, on the whole, probably people could do with a nudge.
Automatic enrollment. Required choice (at hiring time). Required choice with a recommended option. Enrollment online. All increase participation. Also suggests raising matching rate (while keeping maximum matched amount constant) to a sensibly high round number.
Expanding number of options decreases participation. Financial education does not increase scores on financial literacy tests and has only a small effect on enrollment.
Save more tomorrow – allow employees to precommit to raising their savings rate on every pay increase. Sidesteps loss aversion, takes advantage of future virtue being easier that virtue now. Substantially increases savings.
Pension Protection Act. Incentivizes employers to provide automatic enrollment, increase contribution rates over time and match employee contributions.
Deciding when to cash out social security is tricky and online calculator is useless.
Pension funds have to be allocated to different investments. It's complicated, there is a risk-EV tradeoff and the allocation needs periodic adjustment.
Loss aversion means that risk tolerance depends on how often one looks at the results.
Most people are heavily risk averse (which is likely a mistake) and tend towards buying during bubbles and selling during crashes.
Many people, including some named economists, simply split the money evenly between all the offered funds. Means that the choice of funds directly influences the risk-EV tradeoff.
Many people invest heavily in their employer, believe that it is no risker than a diversified fund and base their valuation on previous performance of the stock. Regulation around the running of pension funds don't apply to company stock (thanks to lobbying) so there is an incentive to push them on employees. Suggests 'Sell More Tomorrow' – give employees option to automatically sell off company stock over time – avoids crashing the price or sending negative signals while still divesting employees of unnecessary risk.
Defaults. Now that the DoL has issued official guidelines on how much risk is advisable, companies can safely default to that level rather than the most conservative option, which tends not to produce enough return to actually retire. Can also offer multiple defaults eg conservative, moderate, aggressive.
Structuring complex choices. Provide a tiered choice – a single default –> a few reasonable plans –> full control.
Expect error. Automatic enrollment, defaulting to Save More Tomorrow. Offer an automatic rebalancing to those who took manual control.
Mappings and feedback. Map plans to expected lifestyle, plus estimate of risk.
Incentives. Enforce laws that require pensions to be managed in best interest of employees. Remove exemption on company stock.
Mortgages used to be simple. Legislation required standard format for reporting interest rates. Much more complicated choice now, opening the door to both better and worse choices. Study finds that higher costs are predicted by race and education, and that brokered mortgages are more expensive. Typical result – complexity results in less sophisticated buyers being taken advantage of by advisors.
Subprime mortgages – high interest rates for risk loans. Provides chances to people who need them, but also opens avenue for exploitation through confusing and manipulative sells. Complicated paperwork often hides fees and legally-required warnings.
Lauren Willis proposes limiting types of mortgages to reduce complexity and adding compulsory delays between receiving estimates of total cost and signing. Author proposes RECAP instead, plus simplified report that reduces costs and fees to single number. Side benefit is that it makes shopping online easier, which reduces discrimination against minorities. (Also notes that RECAP strategies could succumb to collusion between comparison sites and sellers.)
Student loans. Government (need-based) or private. Need-based forms are complicated and difficult. Private loans are easier and have less restrictions – appealing to unsophisticated teens. Some college loan officers are in collusion with private companies. Combination of loan guarantee and government subsidy makes student loans very profitable, but competition has focused on rent-seeking instead of prices. Phishing for phools! Government created a market with unsophisticated buyers and zero risk of default. Blood in the water.
RECAP. Simplify government forms and require same questions for private loans. Make standard report format for all loans. Combine government form with tax return to save on duplicated effort.
Nudge families into college savings plans.
Credit card debt is growing. Estimate average family spends $2k on interest per year. Credit cards break mental accounting – bottomless jars. Rewards encourage Rex to spend. Minimum payment anchors actual payment – encourages higher interest fees. Often no way to default automatic payments to pay the whole bill.
RECAP report should prominently include total cost of using the card (interest + hidden fees), in addition to making it easier to shop around for a better deal.
Swedish government privatized Social Security. Went full pro-choice – 783 funds. Adverts (both government and private) encouraged ditching the default and picking your own – 2/3 did.
Default portfolio is well-designed. Average citizens chosen portfolio (does averaging over the portfolios make any sense?) is higher-risk (mostly stocks), under-diversified (mostly Swedish), has higher fees and requires more active management. Default fund has consistently outperformed the average choice. Very few people have modified their portfolio since creation.
Fund adverts contain little actual financial information, implying rent-seeking and exploits.
Ideally author would have had them offer tiered choices, along with advice that the default option was carefully constructed by experts.
Part D – add choices to medicare. Well intentioned. Offered structured choice and filtered options. Mostly opt-in, but low-income seniors and disabled were required to enroll, and defaulted to a random plan if no action was taken. Growing penalty for late enrollment. 50+ plans. Massive public encouragement to pick a plan. Donut hole – coverage turns off after a certain amount of spending and turns back on after another threshold.
Medicare and hospitals swamped with support needs. Much confusion. Some advice bodies also added their own plans – conflict of interest. Many people still unenrolled, including a large subset for which subsidies add up to make it practically free.
Random defaults = sometimes making choices we know are suboptimal – there is no neutral default. Also removes competition – participation nets an equal share.
Poor UX on website. Takes hours to fill out questions. Four experimenters, given same data to fill in, all ended up with different answers.
Recommends removing the law that forces random assignment.
RECAP. Just before enrollment opens, send patients a standardized list of their medication use over the last year.
Estimates that correct plan choice would save an average of $500/year. Study showed that sending a personalized price comparison is a somewhat effective nudge to pick the cheapest plan.
Choice between opt-in, opt-out, or not option at all (some states allow taking corneas without consent). Switching from opt-in to opt-out increases donation rate 16% (according to what sounds like a complicated analysis controlling for many factors).
Author prefers opt-out but with easiest possible interface. Another alternative is mandated choice eg to renew drivers license, must check yes/no for organ donation. Advertising with mild social/moral pressure is also effective.
Environmental concerns typically addresses by strict regulation. Avoiding tragedy of commons usually requires some form of Leviathan.
Author prefers pricing externalities directly. How are carbon credits working out? Approach is generally not popular because it makes the high costs of fixing the problem very explicit. Suggests using mental accounting eg using taxes on carbon to fund universal health-care. Such a program for reducing acid rain was a huge success. How might I judge this for myself? The author has a clear bias.
Feedback. Mandatory labeling has worked for many areas (eg pesticides, cigarretes). Disclosure requirements for industrial toxins have also been very effective in reducing toxic releases – environmental groups used the reports to target PR campaigns against the worst offenders. Again, little detail. Is it worth following up the reference to judge for myself? New labels for cars show annual fuel cost as well as mileage – suggests adding social pressure by putting the labels on the bumper for all to see.
Ambient Orb – little ball that glows red when owner is using lots of energy. Lead to 40% decrease. Could add social pressure by making the results public.
EPA has engaged companies in voluntary programs to reduce energy costs – setting out standards and allowing any company that meets them to use the corresponding logo. Provides leverage – it's much easier to pick the machine with the sticker than to research the energy usage of each.
School choice is tricky. Allow private schools and rich kids get advantages. Disallow them and parents lose freedom over their children are educated. Income-adjusted subsidies might undercut public schools.
Study finds that public schools forced to compete produce higher student achievement.
Attempt in Worcester to do something RECAP like is foiled by schools sowing doubt and obstacles in the path of transfers.
Most parents send their children to the default school. Choice booklets don't contain much standardized information. Condensing the stats to an easily comparable form on a single sheet led to low-income families making similar choices to high-income families, where before they were less likely to apply to highly performing schools.
Complex selection systems, designed to aid de-segragation, are gamed more often by higher-income, more educated, more connected families. In Boston a group of economists designed a replacement system that incentivizes honesty.
Simple nudges like helping students fill out forms and bringing admissions tests to schools can have large effects.
Litigation is a significant cost to hospitals and that cost must be passed down to customers somehow. Estimated to account for 5-9% of hospital expenditures. Also indirect costs in the way it alters behavior to avoid litigation risk. You can also sue the NHS, but it only accounts for 1% of their costs and those costs are already half that of the US. The NHS directly insures doctors rather than requiring a 3rd party insurer. Are the two cost estimates not comparable? Do US patients sue more often? Do the malpractice insurers add to the cost (via profits, interaction time, cash flow)?
Argues that patients might want to give up their right to sue, in exchange for not bearing the costs.
Doctors pay more or less the same insurance premium regardless of how many times they are sued, so suing does not incentivize their behavior. Does discipline board not penalize doctors at all? Why is insurance sold like that anyway? There is very little correlation between whether review determines that negligence exists and whether the patient sues and whether the claim pays out. Huh? Who is running this system?
If we regard the right to sue as insurance for the patient, it looks like very expensive insurance. Suggests that health insurance plans should come with the choice of whether to be allowed to sue, to make the costs explicit. Also proposes that patients should only be allowed to sue only for reckless or intentional wrongdoing, not for negligence. Why is the doctor being sued anyway? Generally sue companies, not employees. Are doctors not normal employees?
Some countries have adopted a no-fault system where you get compensation for medical injury regardless of the cause, saving the high costs of lawyers and insurance companies. Injured parties get compensation. Hospitals are incentivized to lower injury rates. Might lead to not wanting to take on cases where injury is likely eg by refusing to specialize in such fields and directing patients elsewhere.
If we disentangle the religious and cultural aspects from the governments involvement, what we are left with is an array of legal and financial rules that apply only to pairs of people who enter into a government contract together. Different tax rules, entitlement to certain employment benefits, shared ownership of property and funds, surrogate decision making and additional privileges in court. Not to mention visas – a guarantee of being able to live together regardless of future employment situation.
Aside from the cultural significance, not substantially different to any other government system eg tax avoidance programs, attorney-client relation. Like a government incentive to commit to stable child-rearing, which then accreted other tangentially unrelated laws. Not even a particularly effective way to protect children vs eg mandatory child support, high-quality free childcare. Question is not 'should we protect children' but whether this is the most effective way to do it or whether we need to tie it into so many other systems.
I think a much stronger argument is from the baseball-bat-to-the-head principle – if marriage didn't already exist, would we invent it?
…oh, this gets mentioned one the next page :)
People who are about to marry generally don't believe there is any chance of getting divorced, so don't opt-in to pre-nups, leaving them with the messy default divorce law. Would be much less messy if the default set of outcomes was much clearer eg fixed formula for child support rather than court decision.
A footnote ducks the issue of polygamy, which would have made for a much more interesting chapter (eg http://qntm.org/gay – is marriage transitive?). Dealing with those kinds of issues would be much easier if marriage were carved apart at the seams as the author suggest, allowing each group to consider exactly what commitments they want to make to each other.
Give More Tomorrow – like Save More Tomorrow but for charitable donations. This is exactly how I reached the popular 10% mark – never triggered loss aversion.
Charity Debit Card – tracks charitable donations for easy tax returns.
Automatic Tax Return – anyone without itemized deductions or unreported income receives a pre-filled tax return which they can sign and return. In the UK you don't even have to fill in a tax return if you don't want to make any claims. There is also a tiered architecture, where the official online form expands sections in response to questions. A similar system just launched for small companies. It now only takes me around an hour per year to do both personal and company taxes.
Stikk.com – public pre-commitment with financial or social penalties for failure.
CARES – smokers deposit every week what they would have spent on cigarettes. After six months, if a urine test confirms they have not smoked, they get the money back. More effective than nicotine patches.
Motorcycle helmets – instead of mandating, require a stricter driving test to be allowed to ride without helmet.
Gambling self-bans – online gambling sites in the UK are legally obliged to implement and enforce self-bans and self-limits.
Destiny Health Plan – health insurance with reward scheme for activities that reduce health risks.
Dollar a day – teenage girls with a baby receive a dollar for every day that they are not pregnant.
Change lights for aircon filters
Nobite – nail polish that tastes bad, preventing nail biting.
Disulfiram – drug that cause alcholics to throw up immediately if they drink – breaks reward cycle.
Civility Check – "Warning: this appears to be an uncivil email. Are you sure you don't want to wait a while and think about it?"
Slippery slope. If only implement each individual policy if its widely beneficial, is there a slippery slope at all? Libertarian – low-cost opt-out. Some nudge is inevitable anyway, might as well choose a good one.
Evil nudgers. Public policy especially might be hijacked by partisan interests. Author views this as an incentive problem. RECAP and similar could be reflectively applied to policy design.
Right to be wrong. Doesn't prevent people being wrong, just makes it easier for them to be right.
Anti-redistribution. Some believe that all charity should be left to private entities. I don't really understand this view, and the author doesn't do much in the way of devils advocate, so basically just beating on a straw-man. Would like to hear arguments from someone who actually holds this position – suspect they would be less nonsensical from the horses mouth.
Imposes costs on Econs. Might actually remove costs from Econs eg reducing load on healthcare and welfare by helping people make better decisions.
Also, sometimes people prefer not to choose. Give them the freedom to give up their freedom!
Publicity principle – don't allow policies that could not be defended in public, even if they benefit the public. Quick test of whether something is a nudge or manipulation is whether or not you would be happy to explain it to the subject. Also needs to be monitorable eg subliminal messaging might benefit people but is hard to notice if it is crossing the line if you can't see it.
Neutrality. Tricky to define. Certainly couldn't nudge people towards some religion, even if it empirically increases health and happiness. Nudging makes less sense in domains where judging what a fully-informed version of the individual would want is harder eg religion, relationships. So limit it to cases where it is clear what people want (at least in abstract) and where there is no conflict of interest (eg no nudges on who to vote for). Transparency and reporting can reduce the number of cases where conflicts of interest can hide.
Generalises to asymmetric paternalism – weighing the benefit of helping the least sophisticated against the costs on the most sophisticated. Libertarian is a special case where costs are near zero.
Mandatory cooloff periods (eg for divorce).
Two claims:
A potential Third Way – something that both Left and Right can agree on.
'I double' – establish group norm of avoiding long, drawn out arguments and replacing them with the challenge 'I double', at which point the other party can either commit to a standard bet or back down. Works only when there is an unambiguous answer. Still, I'm interested in ideas like this for reducing pervasive and contagious overconfidence.
The entire paper seems to be an extend motte-and-bailey around what exactly is meant by preserving freedom to choose. If a subconscious nudge causes 50% of nudgees to choose the desired option, did they still have free choice? What if it was 70%? 90%?
Freedom itself is such a slippery concept that its barely possible to use as a means of weighing decisions. Many restrictions of freedom open up other freedoms – very few libertarians would object to restricting the freedom to rape and pillage. Many freedoms enable deleterious downstream effects eg if people are free to sell their kidneys, they are free to be economically coerced into doing so. Similarly, if people are free to not purchase health insurance then there is less political pressure to ensure that everyone can afford it.
However, that motte-and-bailey might be worth quietly ignoring if it turns out to be swallowable by both sides of the US political spectrum. Bipartisan cooperation would enable fixing a lot of pressing problems.
Let's instead ignore the problematic concept of freedom of choice and instead view the book through this lens: how can we influence peoples decisions for their own benefit, without their objection and without the system of influence being subverted by other interests. If people don't believe that nudges can influence them so strongly then they won't object to their use. Commercial advertising is far less restrained, both morally and technically, so I don't see an argument that would allow adverts but frown on nudges.
There are a couple of different kinds of problems covered: Planner vs Doer conflict, lack of information, lack of expertise, bias / lack of self-knowledge. It's only really on the last problem that the moral questions really come into play – in all the others it's quite clear what people would want if fully informed.
For Planner vs Doer conflict we have pre-commitment, cooloff periods and speaking the Doers language. For lack of information we have RECAP and feedback, and for lack of expertise we have guidance on systems of defaults and transparency against collusion. I would expect all of these to be largely acceptable on both sides of the spectrum eg the majority of gun owners support waiting periods. RECAP and transparency measures are also hard for companies to publicly oppose (what have you got to hide?), although they would undoubtedly lobby the shit out of them behind the scenes.
For bias / lack of self-knowledge, things are much murkier. If someone doesn't want to wear a motorcycle helmet is that a reasoned assessment of the risks or the result of inability to recognize their own fallibility. Do we have the right to overrule their decision in either case? I don't see a knockout argument in either direction.
I don't have much to take away from this – the actual concrete techniques are all things I've seen elsewhere, mostly in HCI texts – except for the idea that you can take advantage of the fact that people don't believe in their own irrationality. Advertisers and politicians have abused this for decades, so we might as well try to use it for good too.]]>
Scientific and philosophical concepts can change the way we solve problems by helping us to think more effectively about our behavior and our world. Surprisingly, despite their utility, many of these tools remain unknown to most of us.
Can rationality be taught?
Previously believed that any mental exercise increased intelligence. Common justification for teach Latin. No evidence whatsoever.
Thorndike demonstrated that training in one cognitive task does not transfer to another, but was focused on fairly mechanical tasks. Herbet Simon and Allan Newell demonstrated same for logical tasks like Towers of Hanoi.
Piaget believed that people built schemas for understanding and that those schemas could be applied to many tasks but also believed that schemas could only learned, not taught.
Piaget was correct in that people have different sets of mental tools, but wrong in that they can be taught. Also, while mental exercise in general does not transfer, there are tools that can be learned that increase ability over a wide range of problems.
Teases about research showing that courses studied in university drastically affect thinking years later, and that even brief lessons in statistical concepts lead to better thinking in different contexts weeks later.
Goal of this book is to collect tools that are:
Three major insights into how the world works.
Everyone is familiar with visual illusions. Note that knowing that it is an illusion does not allow you to stop seeing it.
Also subject to mental illusions. Similarly to visual illusions, knowing that the illusion is present does not allow you to stop experiencing it. It still feels like direct perception of reality. Requires conscious effort to work around.
First example is schemas/stereotypes. These are frameworks/templates/rule-systems that we use to structure our mental model of the world. Apply to virtually everything we encounter and inform us about likely properties and behavior eg if you see a restaurant with bright primary colors and plastic seating you would reasonably assume that it is a fast-food restaurant and that it will be cheap and low quality food.
Stereotype is generally a negative word, but couldn't function without them. Only cause problems when they are poorly constructed / inaccurate, or when we mistake them for reality instead of heuristics. Can think of schemas as providing base rates. Compare to reference-class forecasting, which is kind of a way of finding an external schema.
Schemas can cause subconscious influences via priming – an effect where activating one part of a schema can spread activation to the rest without the subjects awareness eg students given word problems that include words associated with old people walked slower after the test. Similarly, using words associated with hospitals makes people faster when later answering yes/no questions about hospitals. Followed by a huge list of examples of priming effects causing poor decisions eg hurricanes with female names cause less deaths, apparently because they sound less dangerous. Priming effects seemed to suffer somewhat in the reproducibility crisis. TODO find out which priming experiments have survived.
Framing – presentation of information matters. Order of information eg is it ok to smoke while praying vs is it ok to pray while smoking. Labels eg undocumented worker vs illegal alien. Focus eg 75% lean vs 25% fat. Simple framing can dramatically effect important decisions like whether to go for surgery vs radiation. Useful habit is to deliberately consider different framings of the same problem to see if the intuitive answer changes.
Representativeness heuristic – events are judged as more likely the closer they are to the prototypical example of the reference class eg homicide is a more representative cause of death than suicide is, so homicide is incorrectly judged to be more likely than suicide. Problematic because it ignores basic rules of probability eg Linda. One special case of this is that our prototype for randomness is inaccurate, so we often assign meaning to things because they don't seem random. Another example is conspiracy theories – it doesn't seem like huge events should be caused by single, unremarkable individuals so we look for big causes to match big effects. Some of these seem to be a bit of a stretch to fit the representativeness heuristic.
Big lesson – the best predictor of future behavior is past behavior. Schemas are a fallback for when you don't know the past behavior.
Availability heuristic – judge the frequency or probability of an event by how easy it is to recall or imagine examples. Distorted by how memorable events are – this is why parents worry about child abduction but not child obesity.
Fundamental attribution error / context blindness – we tend to dramatically overestimate the influence of personal attributes and underestimate the influence of context/environment.
Favorite example – on the way to an experiment students pass a collaborator who appears to be ill and in need of help. 2/3 of students who were told they were on time stopped to help, but only 1/10 of students who told they were running late. But an observer would not know this, and would see one group as charitable and the other group as heartless.
Correction to the big lesson before. The best predictor of future behavior is past behavior across a wide range of situations. Otherwise you are seeing mostly the effect of the situation, not of the person. Seems to imply interesting corollary – that best predictor of behavior in a situation is the past behavior of others in the same situation. This squares with results in Stumbling on happiness – that the best predictor of future happiness is not your own internal simulation but the outside view of others in similar situations.
Special case of environmental influence is peer pressure. Again, effects are heavily underestimated eg a significant influence on the eventual grade of male students was whether or not their (randomly assigned) roommate drank heavily in high school. Not likely to be the explanation given by the students in question.
Can use environmental effects deliberately eg giving students accurate statistics on drinking rates at their school reduces drinking overall.
Fundamental attribution error is less strong for ourselves because we experience our own situation somewhat directly. Result is that people are more likely to attribute their own actions to environment than they are for others. I'm having a bad day, they're an angry person.
In a number of experiments, American students are more susceptible to fundamental attribution error and less likely to focus on context than Japanese students.
The single most useful thing I've gotten from this book is the habit of watching out for fundamental attribution error, especially when I am meeting someone for the first time or have only met them in one environment.
Looking at close friends, I can remember my initial impressions of them being completely different. Many of them only became friends because chance lead to repeated meetings and corrected the first impression. Suggest I may have missed out on many other potential friendships where first impressions where not corrected.
Similarly, I've learned to weight the judgment of people who have known the subject for a long time much more than my own judgment from a short period.
Rationalization. Run priming experiments and ask participants about reasons for the their actions. Typically find strong denial that the priming stimulus had any influence, despite the results. In some, the participants claim the opposite causality. So basically no awareness of priming effects.
Familiarity effect – with repeated exposure a neutral stimulus can become a positive stimulus. Doesn't work for negative stimulus, naturally.
Subliminal perception – can cause priming effects with perceptions that are not consciously experienced eg 0.1s flash of a word.
Verbalizing reasons for preferences can lead to less satisfaction later. Interpretation is that System 1 is much better at weighing choices with many facets, and System 2 just gets in the way. Also limits consideration to facets that can be verbally described.
This is all similar to the ideas in Blink. TODO does there exist a good breakdown of which decisions are better made this way? Does the research on expertise in eg Superforecasting bear on this?
Experiments showing that people can learn complex patterns (learning determined by better performance over time, until the pattern is changed) without awareness. Again, participants rationalized their suddenly worse performance.
Common phenomenon where solution to a problem comes out of the blue when thinking about something else. Experiment where subconscious hints to participants led to sudden revelations. Again, participants could not accurately identify the stimulus that inspired them.
But 'sleeping on the problem' won't help you factorize a large prime number. Don't have any good characterization of which problems the subconscious is able to get solve. Humbug.
Uses chess as an example where experts are unable to access the processes involved. Reading accounts by chess grandmasters contradicts this somewhat. Certainly they describe some conscious operations. Could be double-checking the unconscious processes?
Interesting point – we believe we have access to processes governing judgment and behavior but not perception or recall. Why is that? Time for an evo-psych just-so story! We need to be able to generate reasons for actions to convince other people. We don't ever need to convince other people of perception.
Main point – can't access System 1 directly and so we can't know the reasons for our actions/beliefs/perceptions. Given reasonably accurate models can we consciously recreate the processes? In most cases probably don't know enough of the inputs.
Would be interested to apply these ideas to programming. How much of peoples work process are they actually aware of? Some easily observable examples done before eg people claim they don't use the mouse at all but recording shows they use it 50% of the time.
Behavioral economics is a thing.
Cognitive dissonance theory – if our beliefs and our behavior do not agree, one must change. We can't easily control our beliefs but we can control our behavior. Change behavior and beliefs will follow, because dissonance is unpleasant. Really? Many people seem to manage dissonance just fine eg I believe that pigs are sufficiently intelligent that eating them is immoral, but…uh… I still do it. Also complicated by eg aliefs.
Frameworks for making decisions.
Cost-benefit theory. Maximize expected value. Requires finite number of options and perfect information. Get stuck forever trying to acquire more information before choosing.
Bounded rationality. Satisficing. Move up a level – optimize the expected value of the decision process. Handy rule of thumb for financial decisions is to work out your earning rate per hour. Don't spend longer on a decision than it could possibly save you.
Good advice, but does not describe how people actually behave.
Advises doing cost-benefit analysis even when many of the variables are unknown, because laying out the structure gives your subconscious something to work with. Using you conscious mind to find and feed info to subconscious. Very similar to Tetlocks advice to use Fermi equations. Seems like a broad lesson.
Hard to use this for decisions where costs and benefits are not in the same units eg how much money is a human life worth? Many institutions actually generate numbers for this eg here. See also QALY.
Worth doing these analyses even if they are very rough/uncertain, because when you get to sensitivity analysis you now find out how large/small certain variables would have to be to change the decision. Might find out that some unknown variable would have to be unreasonably extreme to change the result. Again, seems like similar reasoning to using Fermi equations. The process is valuable even if the exact results are not.
Revealed preferences. Can eg place a value on life by looking at how much people are willing to pay to avert various risks to life.
Sunk costs – only future costs/benefits should affect your decisions. Beware of exceptions (eg relationships) where costs are not sunk but invested into future returns.
Opportunity cost – cost/benefit analysis also has to include the value of the time used – could you do something more valuable with that time?
Should use cost/benefit analysis in general?
Find that people self-report using cost/benefit analysis in proportion to how much time they have spent studying it, but they may have chosen to study it because it appeals to them. Find that teaching random people also increases their likelihood to agree with cost/benefit analysis.
Mild correlation between SAT scores and self-reported use of cost/benefit analysis. Self-reported use of cost/benefit analysis correlates with making more money in uni staff. Self-reported use correlates with better grades in students, even when accounting for SAT scores.
Overall, weak evidence, mostly self-reported. Do people who self-report use do better on decision problems?
Humans fail to follow cost/benefit analysis in systematic ways.
Loss aversion – lack of symmetry – attach more weight to losses than to equal gains. Can use this to influence behavior by reframing gains as losses. Effectively, can alter peoples value functions if you can change where they place their reference point.
Status quo bias – hard to switch away from the default. Can alter behavior by changing the perceived default. Does status quo bias make sense as opportunity cost of flip-flopping? Need to stabilize some systems.
Choice architecture – the way decisions are framed/structured, especially choice of default and ease of making each choice (eg which choice corresponds to no-action). Libertarian paternalism – setting defaults to benefit the common good. See Nudge.
Choice overload – more choices can lead to taking no choice at all, or at least not properly evaluating decision. Example of people recognizing opportunity cost?
Social priming again – can alter behavior by setting reference point for 'normal' behavior.
Soft-area (social psych, dev psych) students are more able to apply stats principles to their own lives than hard-area (bio psych, cogsci, neurosci) students. Both take the same stats courses, but soft-area students get practice in applying stats to more relatable areas.
Key skills
Theorems alone are not sufficient – for daily application need heuristics that can be trained into System 1.
Law of large numbers – sample stats converge to population stats as sample size grows. Independent of population size.
Sample bias – when sample chance is not independent of measured variables.
People better at intuitively understanding sampling in domains they are familiar, weighting individual samples less. Is this an example of experience trumping the fundamental attribution error?.
Predictions based on interviews are barely correlated (~0.1) with reality, across many different domains and experiments. An interview is a single sample! Given that it's hard to avoid weighting interviews too strongly, better not to interview at all! This is an interesting point. Kahneman contends that we can't train away bias. That doesn't mean we can't predict and avoid situations that cause bias.
Intuitive grasp of LLN can help counter FAE.
Regression to mean – extreme values are likely to be followed by less extreme values, solely because extreme values are by definition less likely in the first place. Most patients get better, so most treatments appear to work. Also related to problems around significance eg Jellybeans cause cancer. Has anyone tried applying bandit algorithms to this problem? Also vaguely recall some recent argument that regression to mean explains away much of the placebo effect.
Rough SD percentiles for normal dist (one side): 34% 14% 2% 0.1%
Need some sort of intuitive Bayesian update. If a meal from a restaurant is heavenly, your posterior estimate of the mean meal from that restaurant should go up, but not all the way up to the meal itself, and the difference between meal value and size of update is larger as the meal gets more extreme. Declining returns from deliciousness on beliefs :)
Surprising stats. Odds of an American aged 25-60 being in top 1% annual income at least once in their life > 0.110. Odds of same for 6 consecutive years are 0.006. Income is much more variable than we think, especially at the high end. Similarly, while many people are on welfare at any one time, few people are on welfare for many consecutive years.
Given a table with symptom yes/no and disease yes/no and asked whether the symptom can be used to diagnose the disease. People fall prey to confirmation bias and only look at the yes/no row. You always need all four cells to answer the question. Even most doctors and nurse fail to get this right.
Looking at NAAL, half of US adults fail problems like 'Determine whether a car has enough gasoline to get to the next gas station, based on a graphic of the car's fuel gauge, a sign stating the miles to the next gas station, and information given in the question about the car's fuel use.' So I have to wonder, in many of these experiments, are we seeing a specific cognitive bias or just a general lack of quantitative literacy. Can the average person even properly understand the question? I haven't seen any experiments establish a baseline ability to solve similar problems when the bias does not come into play.
Statistical significance – odds of such an extreme result occurring by chance under the default model. Abused as a declaration of whether or not the test model is correct. Does not account for other models, poor default model, number of other tests run but not reported ,prior beliefs in test model etc.
Correlation. Measure of association between two variables. Pearson correlation only measures linear relationships. Easily distorted by transformation of variables. Prefer shared information?
Correlations which are hard to detect visibly can still provide significant information. Example given is correlation of 0.3 between income and IQ. Given IQ in 84th percentile (mean + 1SD), expect income in 63rd percentile (mean + 0.3SD).
Example correlations (useful for mental scale):
0.3 = income <–> IQ, grad school grades <–> college grades, cardiovascular illness <–> weight
0.5 = IQ <–> job performance (for average job)
0.7 = height <–> weight
0.8 = SAT score this year <–> SAT score last year
Correlation != causality. Well known but very hard to resist in practice, especially when causality is plausible. Does this make sense from a Bayesian perspective? How much should correlation increase belief in causation? Especially prior to modern distribution of knowledge and experimental data.
Illusory correlation due to confirmation bias – sample is distorted by prior belief in positive or negative correlation. Similarly, if implausible we are unlikely to even notice evidence of correlation.
In neutral condition ie no prior belief, how high does correlation have to be before we notice. Given all the data at once and when actively looking, still needs to be around 0.6. When presented in pairs, detection rate decreases sharply as the time interval between pairs increases. Does this apply to earlier results of subconscious pattern recognition? Are there some types of correlations which our subconscious can detect at lower levels?
Key point – your belief in whether or not a correlation exists is highly unreliable. Have to rely on systematic sampling + analysis.
Reliability – correlation between repeated test results. Validity – correlation between test results and reality. Eg astrological sign is a reliable but not valid test of personality. A test cannot be valid but unreliable. If two tests for the same trait do not agree, at least one of them must not be valid.
In examples given, trait samples are much more variable than ability samples but people make predictions as if they are less variable. One of the causes of FAE. Claims that coding is important ie people would better understand variability of traits if they had good numerical/categorical measurements.
Rule of thumb – if you can't even figure out how to code a variable, you are likely underestimating the variability because you can't see it.
Training in statistical heuristics like this was shown to transfer between tasks in different domains for the authors students.
Multiple regression – find correlation between two variables that is not explained by listed confounders. Only works if you catch all of the confounders. Often find that randomized control experiments give different answer to the multiple regression, showing that some confounding factor was missed.
Measurements > self-reports.
A/B testing – continuous live experiments with similar alternatives to move towards local optimum.
Within designs vs between designs. Between designs vary the condition across different people/places/whatever. Within designs vary the condition at for each person/place/whatever, which controls for differences between person/place/whatever. Before/after designs vary the condition over time – remember to vary order across sites.
Try to vary only one thing, keeping everything else constant (otherwise we are back to the problems with multiple regression).
In tests of significance, cases only count when they are statistically independent (follows from deriving the significance test).
Natural experiment – data in the wild that contains presumably independent cases of the variable.
Correlational evidence – not actually clear on the definition given, think it is correlation found between cases that are not presumed to be independent, thus requiring multiple regression
Randomized control experiments – variable chosen at random so guaranteed not have any confounding factor.
Natural experiments are useful for identifying hypotheses. Randomized experiments are more reliable but usually costlier. Some experiments are too expensive, impractical or unethical to perform, so have to rely on natural experiments.
We often undertake huge programs without any experiments. Many examples were actual effects of a program are the opposite of expected. Experiments are cheap relative to the cost of being wrong.
Place low confidence in studies that rely on correlation alone. Multiple regression is useful and sometimes the only tool available, but it is very error-prone.
MRA can provide weak evidence for causality via lagged correlations.
Dose-dependence – correlation increases for higher dose – somewhat stronger evidence than correlation alone.
Some effects cannot be proven by MRA eg is there bias against hiring people who have been unemployed for a long time – lack of hiring could be caused by bias, or unsuitability for job could cause long-term unemployment.
Experiments which may withhold intervention from the control group often viewed as unethical. Probably more unethical to apply interventions when you don't know if they help or harm. Similar story in evidence-based medicine – doctors in the 50s were opposed to randomized control experiments of new medicines. History is not on their side.
Lack of correlation doesn't prove lack of causation. Confounding variables may hide the correlation eg suppose some new diet is not correlated with weight loss – it may be because the people who are struggling most with weight gain are more likely to try the diet – the diet is bringing their high numbers down to the average numbers so no obvious effect appears.
Self-reports on internal state are entirely unreliable. Very heavily influenced by priming/framing. Lack of introspection into mental processes means that in practice model of own internal state is often only about as accurate as model of other peoples internal state.
Stumbling on happiness has a lot of good examples of this. In particular, makes a strong case that the outside view is better ie 'would I be happy in situation X' is better answered by 'are other people happy in situation X' than by introspection/simulation.
Reference group effect – when asked to rate some trait/attribute/effect, people rate it relative to what they believe to be the norm. Causes problems if different subjects have different reference groups. Often vanishes when the reference group is made explicit.
Self-enhancement bias – people believe themselves to be better than average in most areas. Varies across cultures. In some cultures effect is negative – a modesty bias.
Agreement response bias – more likely to agree to question that disagree. Varies by culture. Controlled by counter-balancing questions – same quantity of normal and reversed questions for each category.
Behavioral measures more accurate eg better to measure conscientiousness by grades, timeliness etc. How is this established?.
Rule of thumb – behavioral measures > self-reports for concrete scenarios > self-reports for abstract properties.
Self-experiments. Try to only change one thing. Simple measures often good enough eg three-point scale for mood. Record data immediately, don't try to recall.
In some sense, self-experiments are easier because there are many confounders you just don't care about. Maybe coffee only works for you because of your genetics. Don't care – still works. Similar to A/B testing in that you can improve the goal variable without necessarily understanding the causal structure.
Deductive logic – given premises and rules of valid inference can determine the truth of a statement. Assumes closed world, reliable knowledge.
Inductive logic – given samples, reach (probablistic) conclusions about distribution. Open world. Assumes independent samples.
Deductive and inductive logic both give frameworks on what inferences are valid.
Dialectical reasoning – less about refereeing inference and more about ways to structure search for truth.
Deductive/inductive logic are concerned with validity. Dialectical reasoning is concerned with truth.
Propositional logic etc.
Pragmatic reasoning schemas – rules-sets for commonly encountered situations. Examples where people fail a logical puzzle but solve it easily when it is encoded in a familiar setting. Some schemas are valid (in terms of formal logic). Some are more like heuristics.
Deontic reasoning – not concerned with validity or truth but with whether a persons conduct is proper. Often signaled by use of the word 'should'.
Argues that purely logical schema are not useful, because Confucian China was successful without developing them.
Confucian China did not develop formal logic. Instead relied on dialectical reasoning.
Three rules of thumb described as underling Easter dialecticism:
Experimental evidence that people raised in this tradition are more focused on context than individuals, less adept at applying formal logic to real-world examples, more comfortable with uncertainty.
Speaking of the weaknesses of natural experiments, I find much of this chapter unconvincing. Superforecasting covers what seems to me to be similar material (eg practical value of synthesizing multiple viewpoints) without pulling in the huge confounders of Eastern vs Western culture.
Separately, while I didn't find much practical value in Trying not to try, it's a very accessible and enjoyable introduction to Confucian-era philosophy.
Epistemics – discipline which fuses theory of knowledge, cognitive science, philosophy of science – study of knowledge and how it is obtained
Occams razor. Simpler theories preferred because they are easier to test. Define simple.
Compares Occams razor to 'release early, release often'. Investigating simple hypotheses first provides faster feedback. Because they are easier to falsify? Why not skip the middle man and recommend picking the most easily falsified hypothesis?
Reductionism – reductionist theories are those that assert that the whole is nothing more than the sum of the parts. My preferred definition – reductionism is a strategy that assumes that the properties of the whole follow more from the properties of the parts than from their organization. A reductionist approach to understanding a computer might be able to model one perfectly, but would not notice the high-level patterns in the bits in memory, would not realise that there is a simpler way to understand the behavior than a full simulation.
NIMH refuses to support research in behavioral science, preferring to focus on neuroscience and genetics. This reductionist approach has produced no new treatments. There may be high-level structure in the brain that can be understood without understanding the parts, and understanding the parts may not cast any light on this high-level structure. To continue the computer analogy, even a full working model of a computer will not tell you what the program is supposed to do or how it behaves in general. It can only tell you how it will behave under the specific conditions of each simulation run. Entanglement with the world provides meaning to the bits in memory and we need to understand that entanglement.
We are too good at generating truthy theories. What makes a good theory?
Falsifiability. Putting the focus on falsification counters confirmation bias. Interesting observation from Rationality: AI to Zombies is that Bayes rule requires that if outcome X raises your confidence in a theory then outcome ¬X must lower your confidence. If you believe that both outcomes confirm your theory then you can raise your confidence without even looking.
Is evolution falsifiable? No way to run direct experiments. This is why the above formulation of falsifiability is interesting – there are observations that would lower our confidence in evolution, even if we can't test the theory directly.
Induction and black swans. Maybe the sun won't rise tomorrow.
Significance alone is not enough. Have to consider prior beliefs. Some experiments confirm ESP and some deny it, but the prior is so low that we would need significantly more evidence to be convinced, even if many experiments reach the magical 0.05 significance.
Ad-hoc amendments to theories – seemingly unfounded additions to a theory that enable explaining some additional, inconvenient fact. Post-hoc amendments – rendered seemingly reasonable by hindsight bias – prevents noticing falsification.
Rationality: From AI to Zombies has a thorough, novel and convincing treatment of this whole topic that heavily influenced my thinking. I'm increasingly struck by how many concepts can be usefully replaced by Bayesian reasoning and information theory.
Theories often generated and pursued because of intuition.
Paradigm shifts. Exciting new theory comes along. Scientists jump on board and start experimenting, eventually finding evidence that supplants the old theory.It's like a new hammer is discovered and for a while everything looks like a nail. Eventually it settles down and only the useful applications remain.
Dominant theories are often heavily influenced by culture.
On the whole, the scientific process in reality is not nearly as disinterested and impartial as it sounds on paper.
Some very useful ideas, mixed in with lots of interesting stories and experiments.
East vs West digressions were unconvincing, especially since they came immediately after the section explaining all the possible problems with the methodology. Frequently made the leap from 'Eastern students display behavior X' to 'Eastern display behavior X because of cultural trait Y' with little more than truthy feels.
For the later chapters on the scientific process, I much prefer the treatment in Rationality: From AI to Zombies.
The sections on statistics and behavioral economics were mostly not new to me, but I did learn from the warnings about MRA.
Main things I want to remember from this:
Lastly, there were also some minor mentions of some of these skills transferring across different domains, which is something I'm on the lookout for.]]>