Noble prize winner in economics Daniel Kahneman gives insight into how the human mind works. The book emphasizes how easily we can be influenced without even knowing it. He introduces two characters system one our fast and intuitive thought process and contrasts it with system two our slower, more logical reasoning. Recognizing the rationality behind our choices, helps us avoid our natural human biases, crucial in a world where we are constantly making decisions.
Statistically, the same outcomes but conveyed differently.
- A vaccine has a 2% failure rate
- A vaccine has a 98% success rate
Though technically the vaccine is equally as effective, majority of people would respond more positively to option 2.
Availability and Probability
You take the train every day to work, recently a train crashed. Would you be more inclined to take the car to work?
If yes, why? In reality, the chances of the train crashing did not change after the incident. Ironically, you would be more likely to die taking a car than a train probability wise.
People who watch the news often think the events broadcasted are more likely and dangerous than they are in reality. Many people are actively afraid of terrorism, In 2017, 6476 people experienced fatal injuries from terrorism, yet 300,000 people die each year due to obesity. 1.25 million people die each year from road accidents. But many don’t think twice before sitting in their cars or biting into a big mac. 17.9 million from heart disease but these things aren’t on a lot of our minds.
The more we’re exposed to an event, the easier it is to come up with examples, the more examples we can think of the higher we estimate the frequency of the event.
We tend to overestimate the probability of unlikely events occurring, resulting in miscalculations when we make decisions by overweighting these events.
when a given value influences an unknown value through a shift.
- How much would you pay for this ring?
2. Would you pay $7000 for this ring?
Those asked question 2, despite being shown the same ring would purchase the ring for a higher price than those in question 1
Another reason a jacket on sale at $300 down from $1000 looks more attractive than the same jacket for a regular price of $300
Say you’ve just lost $50 gambling but have a 50% chance of winning it back, you would be more inclined to try again. Because you’re already in $50 deep. This is how many people get sunken into gambling loopholes.
Similarly, people who have made bad decisions in the past that have cost them time or money are less likely to take action to get out of those bad decisions because they’re too invested in them. For example, you buy a pair of jeans that you never wear, they’re ugly and taking up space in your closet. But you don’t want to throw them out because you invested money in them.
Another reason we see people in toxic relationships or jobs they don’t like. They fear starting over because it means everything they did in the past was all for nothing; this is fear is usually more destructive than letting go.
The decisions from the past should not influence the level of utility we get from new decisions. They should be analyzed individually.
Tieing into prospect theory; when the chances of winning and losing are similar, we are risk-averse. Whereas if there is a guaranteed loss, we become more inclined to take risks.
We also see the endowment effect here. We often overestimate the actual value of the things we own simply because they’re in our possession.
“What you see is all that there is.”
The human mind loves stories. When we get limited information, our brains try to fill in gaps and jump to conclusions to perfect a story, often disregarding the lack of evidence to support our story.
This can lead to Confirmation bias, resulting in us looking for information that supports our current beliefs and perspectives, while we tend to overlook information that contradicts them.
For example, let’s say we don’t believe in climate change. We’re told that the Amazon rainforest is on fire due to increases in temperature. We are quick to disregard this fact and look for any evidence that can reinforce our view that climate change does not cause forest fires. We think up any alternative causes for the fire. (*climate change is a serious problem)
When an event occurs in the past, people recall they “knew it all along.” But if the event doesn’t play out, they often brush it off. We often exaggerate the time’s things went wrong and never acknowledge all the times they didn’t because we never see the cause and effect of them.
We are quick to blame decision-makers when the outcomes of an event are negative. Questioning how they didn’t see the “obvious” signs an event was going to play out. In foresight, these signs might have been extremely subtle, but in hindsight, they are exaggerated.
We overweight our evidence and underweight the base rate of an event.
We tend to use system 1 in single evaluations but use system 2 in comparison evaluations. Comparison evaluations are more accurate and used in Bayes law.
The baseline is the average, or the predicted average when nothing about the case is explicitly given. The given information about the case should be assigned weight factors when calculating. If that’s too much work typically aiming for the in-between of the base rate and our intuitive response works well.
Visualizing large statistics is difficult for a lot of us; we react better to smaller data sets say a study with 100 people. The problem is these small data sets are often not representative of the whole population.
Though this seems obvious, we often are guilty of jumping to conclusions when we experience something and conclude that it must hold true for the broader population.
Say you hear four people talking badly about a movie. You conclude that it must be a bad movie. However, the problem here is you come to a conclusion disregarding absence of the thousands of other people who watched that movie. The same can be said for experiences, people, places, ideas etc.
Small numbers don’t account for the random chance of sampling.
Humans like individual stories or experiances> facts or statists because we can relate and visualize them better. Marketers and influencers have long been using this tactic. You would respond better to a dying person in an anti-smoking ad than a big number.
Correlation ≠ Causation
Because we love a coherent story and anything that supports our beliefs, we look for correlations even when they aren’t tied to causes.
For example, many old people move to Florida, and many old people get Alzheimer’s, therefore, moving to Florida causes Alzheimer’s.
This is stupid, but it’s not always so obvious.
We respond to hard questions by answering easier ones:
What do I think about this? → How do I feel about this?
This is system one at work. It loves to make automatic connections and coming to an answer immediately
“Who is the best candidate?” this is a complicated question, and numerous factors must be weighed in. However, we often substitute this for “Who is the most likeable?”
Substituting isn’t always accurate because of its dependence on the next question your asking. For example
If I asked you how you feel about your love life, then ask you how happy you are with life. The previous answer would influence if not substitute for the second question. If I am unhappy about my love life, then I would say I am unhappy about my life. Whereas if the question was asked on its own, this factor would not be weighted as much.
Here are some others you should check out :)
Mshindi wa tuzo ya Nobel ya uchumi Daniel Kahneman anatoa ufahamu juu ya jinsi akili ya binadamu inafanya kazi. Kitabu hicho kinakazia jinsi tunavyoweza kuathiriwa kwa urahisi bila hata kujua. Mfumo wa kwanza ni mchakato wetu wa kufikiri wa haraka na wa intuitive na unatofautiana na mfumo wa pili ni sababu yetu ya polepole na ya kimantiki. Kutambua sababu za maamuzi yetu hutusaidia kuepuka ubaguzi wetu wa asili, muhimu katika ulimwengu ambapo tunapiga maamuzi mara kwa mara. Kwa takwimu, matokeo ni yaleyale lakini yanaonyeshwa kwa njia tofauti. "Hata hivyo, ""kiwango cha mafanikio cha chanjo ni 98%"" na ""kiwango cha ufanisi ni sawa na kiwango cha ufanisi wa chanjo, lakini watu wengi watakubaliana zaidi na chaguo la pili.""" Upatikanaji na Uwezekano Wewe kuchukua treni kila siku kwenda kazi, hivi karibuni treni alishindwa. Je, ungependa zaidi kutumia gari kwenda kazini? Ikiwa ndiyo, kwa nini? Kwa kweli, uwezekano wa gari-moshi hilo kugonga haukubadilika baada ya tukio hilo. Kwa kushangaza, kuna uwezekano mkubwa zaidi wa kufa ukiendesha gari kuliko gari-moshi. Watu wanaotazama habari mara nyingi hufikiri kwamba matukio yanayoonyeshwa ni ya uwezekano mkubwa na hatari kuliko yale yaliyo kweli. "Watu wengi wanaogopa ugaidi, ""Katika mwaka 2017, watu 6476 walipata majeraha ya kifo kutokana na ugaidi, lakini watu 300,000 hufa kila mwaka kutokana na unene wa kupita kiasi." Watu milioni 1.25 hufa kila mwaka kutokana na ajali za barabarani. Lakini wengi hawafikiri mara mbili kabla ya kukaa katika magari yao au kula mac kubwa. Watu milioni 17.9 wanaugua ugonjwa wa moyo, lakini hawa sio watu wengi wanaofikiria. Kadiri tunavyojikuta katika tukio, ndivyo inavyokuwa rahisi zaidi kupata mifano, ni mifano mingapi tunaweza kufikiria, ni zaidi tunakadiria mzunguko wa tukio. Sisi huwa na overestimate uwezekano wa matukio ya uwezekano, na kusababisha miscalculations wakati sisi kufanya maamuzi kwa overweighting matukio haya. Wakati thamani fulani huathiri thamani isiyojulikana kupitia mabadiliko. - Ungelipa kiasi gani kwa ajili ya pete hii? 2. Uwezekano wa kupata Je, utalipa dola 7,000 kwa pete hii? "Wale waliojibu swali la pili, ""Je, hata ikiwa utaonyeshwa pete hiyo, utainunua kwa bei ya juu kuliko wale waliouliza swali la kwanza?"" sababu nyingine ya koti la kuuza kwa $ 300 chini ya $ 1000 inaonekana kuvutia zaidi kuliko koti moja kwa bei ya kawaida ya $ 300." Kwa sababu tayari uko katika $ 50 kina. Hii ni jinsi watu wengi kupata kuzama katika loopholes kamari. Vivyo hivyo, watu ambao wamefanya maamuzi mabaya katika siku za nyuma ambayo yamewagharimu wakati au pesa ni chini ya uwezekano wa kuchukua hatua ya kupata nje ya maamuzi hayo mabaya kwa sababu wao ni kuwekeza sana katika yao. Kwa mfano, unanunua suruali ya suruali ambayo huvaa kamwe, ni mbaya na inachukua nafasi katika kabati lako. Lakini hutaki kuitupa nje kwa sababu uliwekeza pesa ndani yake. Sababu nyingine tunaona watu katika mahusiano yenye sumu au kazi ambazo hawapendi. Wanaogopa kuanza tena kwa sababu inamaanisha kila kitu walichofanya zamani kilikuwa bure; hii ni hofu ambayo kwa kawaida ni mbaya zaidi kuliko kuacha. Maamuzi ya zamani hayapaswi kuathiri kiwango cha manufaa tunayopata kutoka kwa maamuzi mapya. Zinapaswa kuchunguzwa kwa mtu mmoja-mmoja. Katika nadharia ya matarajio, wakati nafasi za kushinda na kupoteza ni sawa, sisi ni hatari-averse. Lakini ikiwa upotevu umehakikishwa, tunakuwa na mwelekeo zaidi wa kuchukua hatari. Pia tunaona athari ya utajiri hapa. Mara nyingi sisi hukadiria kupita kiasi thamani halisi ya vitu tunavyomiliki kwa sababu tu vinapatikana kwetu. "Kile unachoona ndicho kilichopo". Akili ya binadamu hupenda hadithi. Wakati tunapopata habari ndogo, ubongo wetu hujaribu kujaza mapungufu na kuruka kwa hitimisho ili kukamilisha hadithi, mara nyingi ukipuuza ukosefu wa ushahidi wa kuunga mkono hadithi yetu. Hii inaweza kusababisha upendeleo wa uthibitisho, na kusababisha sisi kutafuta habari inayosaidia imani na mitazamo yetu ya sasa, wakati tuna mwelekeo wa kupuuza habari inayopingana nayo. Kwa mfano, hebu tuseme kwamba hatuamini katika mabadiliko ya hali ya hewa. Tunasikia kwamba msitu wa mvua wa Amazon unawaka moto kwa sababu ya ongezeko la joto. Sisi ni haraka kupuuza ukweli huu na kuangalia kwa ushahidi wowote ambao unaweza kuimarisha maoni yetu kwamba mabadiliko ya hali ya hewa si kusababisha moto misitu. Tunafikiria sababu nyingine za moto huo. Wakati tukio hutokea katika siku za nyuma, watu kukumbuka wao "walijua ni pamoja na". Lakini kama tukio si kucheza nje, wao mara nyingi brush ni mbali. Mara nyingi tunazidisha wakati mambo yalienda vibaya na kamwe hatukubali nyakati zote hazikufanya hivyo kwa sababu hatuwezi kuona sababu na athari zao. Sisi ni haraka kulaumu watunga maamuzi wakati matokeo ya tukio ni hasi. Kuuliza jinsi hawakuona ishara "wazi" tukio ilikuwa kwenda kucheza nje. Kwa kutazama mbele, huenda ishara hizo zilikuwa zisizoeleweka sana, lakini kwa kutazama nyuma, zimezidiwa kupita kiasi. Sisi overweight ushahidi wetu na underweight kiwango cha msingi wa tukio. Sisi hutumia mfumo wa 1 katika tathmini ya moja, lakini kutumia mfumo wa 2 katika tathmini ya kulinganisha. Tathmini za kulinganisha ni sahihi zaidi na hutumiwa katika sheria ya Bayes. Mstari wa msingi ni wastani, au wastani uliokadiriwa wakati hakuna chochote kuhusu kesi hiyo kinachotolewa wazi. Taarifa iliyotolewa kuhusu kesi inapaswa kupewa uzito sababu wakati wa kuhesabu. Kama kwamba ni kazi sana kawaida lengo kwa katika kati ya kiwango cha msingi na majibu yetu intuitive kazi vizuri. Kuangalia takwimu kubwa ni vigumu kwa wengi wetu; tunaitikia vizuri zaidi kwa seti ndogo za data, anasema utafiti na watu 100. Tatizo ni kwamba seti hizi ndogo za data mara nyingi hazionyeshi idadi nzima ya watu. Ingawa hii inaonekana dhahiri, mara nyingi sisi ni hatia ya kuruka kwa hitimisho wakati sisi uzoefu kitu na kuhitimisha kwamba ni lazima kuwa kweli kwa idadi kubwa ya watu. Sema unasikia watu wanne wakizungumza vibaya juu ya filamu. Unafikia mkataa kwamba ni lazima iwe ni filamu mbaya. Hata hivyo, tatizo hapa ni wewe kuja kwa hitimisho kupuuza kutokuwepo kwa maelfu ya watu wengine ambao watazama filamu hiyo. Vivyo hivyo inaweza kusemwa kwa ajili ya uzoefu, watu, maeneo, mawazo nk. Idadi ndogo hazizingatiwi kwa nafasi ya nasibu ya sampuli. Wanadamu kama hadithi binafsi au uzoefu> ukweli au statists kwa sababu tunaweza kuhusiana na visualize yao bora. Wafanyabiashara na washawishi wamekuwa wakitumia mbinu hii kwa muda mrefu. "Hata hivyo, ""mtu anayekufa"" katika tangazo la kupinga uvutaji sigara ni bora kuliko ""nambari kubwa.""" Uhusiano <unk> Usababishi Kwa sababu tunapenda hadithi thabiti na chochote kinachounga mkono imani zetu, tunatafuta uhusiano hata wakati haufanyi uhusiano na sababu. Kwa mfano, watu wengi wazee kuhamia Florida, na watu wengi wazee kupata Alzheimer <unk> s, kwa hiyo, kuhamia Florida husababisha Alzheimer <unk> s. Hii ni kipumbavu, lakini si mara zote ni dhahiri hivyo. "Kuna maswali magumu tunayoyatikia kwa kujibu maswali rahisi kama vile ""Nina maoni gani kuhusu hili?""" → Ninahisije kuhusu jambo hili? Hii ni mfumo mmoja kazini. "Kama unavyojua, ""kufanya uhusiano wa moja kwa moja na kuja kwa jibu mara moja"" ni swali gumu na mambo mengi yanapaswa kupimwa." Hata hivyo, mara nyingi sisi kubadilisha hii kwa <unk>Ni nani kama zaidi?<unk> Kubadilisha si mara zote sahihi kwa sababu ya utegemezi wake juu ya swali la pili kuuliza yako. Kwa mfano, kama wewe ni kuuliza jinsi wewe kujisikia kuhusu maisha yako ya upendo, basi kuuliza jinsi wewe ni furaha na maisha. Jibu la awali lingekuwa na uvutano ikiwa si badala ya swali la pili. Ikiwa sifurahii maisha yangu ya mapenzi, basi ningesema sifurahii maisha yangu. Wakati ikiwa swali liliulizwa peke yake, jambo hili halingekuwa na uzito kama huo. Hapa ni baadhi ya wengine unapaswa kuangalia nje :)