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# MIT, Intel unveil new initiatives addressing ‘big data’

MIT has been selected from among 55 institutions that submitted 157 proposals to host a new Intel research center that will concentrate on what’s come to be called “big data” — new techniques for organizing and making sense of the huge amounts of information generated by Web users and new networked sensors.

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# Papers

Recent publications related to complex systems
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## Immigrants Do Not Increase Crime, Research Shows

Across our studies, one finding remains clear: Cities and neighborhoods with greater concentrations of immigrants have lower rates of crime and violence, all else being equal.
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## Seafood prices reveal impacts of a major ecological disturbance

Coastal hypoxia is a growing problem worldwide, but economic consequences for fisheries are largely unknown. We provide evidence that hypoxia causes economic effects on a major fishery that was once the most valuable fishery in America. Our analysis is also a breakthrough in causal inference for coupled human-natural systems. Although establishing causality with observational data is always challenging, feedbacks across the human and natural systems amplify these challenges and explain why linking hypoxia to fishery losses has been elusive. We offer an alternative approach using a market counterfactual that is immune to contamination from feedbacks in the coupled system. Natural resource prices can thus be a means to assess the significance of an ecological disturbance.

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## The Lexicocalorimeter: Gauging public health through caloric input and output on social media

We propose and develop a Lexicocalorimeter: an online, interactive instrument for measuring the “caloric content” of social media and other large-scale texts. We do so by constructing extensive yet improvable tables of food and activity related phrases, and respectively assigning them with sourced estimates of caloric intake and expenditure. We show that for Twitter, our naive measures of “caloric input”, “caloric output”, and the ratio of these measures are all strong correlates with health and well-being measures for the contiguous United States. Our caloric balance measure in many cases outperforms both its constituent quantities; is tunable to specific health and well-being measures such as diabetes rates; has the capability of providing a real-time signal reflecting a population’s health; and has the potential to be used alongside traditional survey data in the development of public policy and collective self-awareness. Because our Lexicocalorimeter is a linear superposition of principled phrase scores, we also show we can move beyond correlations to explore what people talk about in collective detail, and assist in the understanding and explanation of how population-scale conditions vary, a capacity unavailable to black-box type methods.

Alajajian SE, Williams JR, Reagan AJ, Alajajian SC, Frank MR, Mitchell L, et al. (2017) The Lexicocalorimeter: Gauging public health through caloric input and output on social media. PLoS ONE 12(2): e0168893. doi:10.1371/journal.pone.0168893

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## The Anthropocene equation

The dominant external forces influencing the rate of change of the Earth System have been astronomical and geophysical during the planet’s 4.5-billion-year existence. In the last six decades, anthropogenic forcings have driven exceptionally rapid rates of change in the Earth System. This new regime can be represented by an ‘Anthropocene equation’, where other forcings tend to zero, and the rate of change under human influence can be estimated. Reducing the risk of leaving the glacial–interglacial limit cycle of the late Quaternary for an uncertain future will require, in the first instance, the rate of change of the Earth System to become approximately zero.

The Anthropocene equation
Owen Gaffney, Will Steffen

The Anthropocene Review

First Published February 10, 2017

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## Nature Insight: Frontiers in biology

This year’s ‘Frontiers in biology’ Insight features Reviews on how genomics is helping to uncover the peopling of the world, the interplay between morphogens and morphogenesis in determining organismal shape, the factors that influence the immune response to cancer, advances in single-cell genomics, and the effects of base modifications in messenger RNA.

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Complexity is becoming mainstream...

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## A solution to the single-question crowd wisdom problem

Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.

A solution to the single-question crowd wisdom problem

Dražen Prelec, H. Sebastian Seung & John McCoy

Nature 541, 532–535 (26 January 2017) doi:10.1038/nature21054

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## Fundamental limitations of network reconstruction from temporal data

Inferring properties of the interaction matrix that characterizes how nodes in a networked system directly interact with each other is a well-known network reconstruction problem. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations governing which properties of the interaction matrix (e.g. adjacency pattern, sign pattern or degree sequence) can be inferred from given temporal data of individual nodes remain unknown. Here, we rigorously derive the necessary conditions to reconstruct any property of the interaction matrix. Counterintuitively, we find that reconstructing any property of the interaction matrix is generically as difficult as reconstructing the interaction matrix itself, requiring equally informative temporal data. Revealing these fundamental limitations sheds light on the design of better network reconstruction algorithms that offer practical improvements over existing methods.

Fundamental limitations of network reconstruction from temporal data
Marco Tulio Angulo, Jaime A. Moreno, Gabor Lippner, Albert-László Barabási, Yang-Yu Liu

JRS Interface

February 2017
Volume 14, issue 127

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 Suggested by Eduardo Mojica-Nava

## The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

Recently, there has been an increasing interest in the control community in studying large-scale distributed systems. Several techniques have been developed to address the main challenges for these systems, such as the amount of information needed to guarantee the proper operation of the system, the economic costs associated with the required communication structure, and the high computational burden of solving for the control inputs for largescale systems.

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## Data-driven predictions in the science of science

The desire to predict discoveries—to have some idea, in advance, of what will be discovered, by whom, when, and where—pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the “science of science” and what it teaches us about the predictability of scientific discovery. We then discuss future opportunities for improving predictions derived from the science of science and its potential impact, positive and negative, on the scientific community.

Data-driven predictions in the science of science
Aaron Clauset, Daniel B. Larremore, Roberta Sinatra

Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 477-480
DOI: 10.1126/science.aal4217

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## Improving election prediction internationally

This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data. We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds. We also participated in two live forecasting experiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that, with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.

Improving election prediction internationally
Ryan Kennedy, Stefan Wojcik, David Lazer

Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 515-520
DOI: 10.1126/science.aal2887

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 Suggested by Simon D Angus

## The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations

With the large-scale penetration of the internet, for the first time, humanity has become linked by a single, open, communications platform. Harnessing this fact, we report insights arising from a unified internet activity and location dataset of an unparalleled scope and accuracy drawn from over a trillion (1.5$\times 10^{12}$) observations of end-user internet connections, with temporal resolution of just 15min over 2006-2012. We show how these data can be used to provide scientific insights in diverse fields such as technological diffusion, chronobiology and economics.  To our knowledge, our study is the first of its kind to use online/offline activity of the entire internet to infer such insights, demonstrating the potential of the internet as a quantitative social data-science platform.

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## How Life (and Death) Spring From Disorder

Life was long thought to obey its own set of rules. But as simple systems show signs of lifelike behavior, scientists are arguing about whether this apparent complexity is all a consequence of thermodynamics.

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## The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease classes, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

The multiplex network of human diseases
Arda Halu, Manlio De Domenico, Alex Arenas, Amitabh Sharma
doi: https://doi.org/10.1101/100370

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## Control of finite critical behaviour in a small-scale social system

Many adaptive systems sit near a tipping or critical point. For systems near a critical point small changes to component behaviour can induce large-scale changes in aggregate structure and function. Criticality can be adaptive when the environment is changing, but entails reduced robustness through sensitivity. This tradeoff can be resolved when criticality can be tuned. We address the control of finite measures of criticality using data on fight sizes from an animal society model system (Macaca nemestrinan=48). We find that a heterogeneous, socially organized system, like homogeneous, spatial systems (flocks and schools), sits near a critical point; the contributions individuals make to collective phenomena can be quantified; there is heterogeneity in these contributions; and distance from the critical point (DFC) can be controlled through biologically plausible mechanisms exploiting heterogeneity. We propose two alternative hypotheses for why a system decreases the distance from the critical point.

Control of finite critical behaviour in a small-scale social system
Bryan C. Daniels, David C. Krakauer & Jessica C. Flack
Nature Communications 8, Article number: 14301 (2017)
doi:10.1038/ncomms14301

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## Synergy from reproductive division of labor and complexity drive the evolution of sex

Computer experiments, testing features proposed to explain the evolution of sexual recombination, show that this evolution is better described as a network of interactions between possible sexual forms, including diploidy, thelytoky, facultative sex, assortation, bisexuality, and division of labor, rather than a simple transition from parthenogenesis to sexual recombination. Results show that sex is an adaptation to manage genetic complexity in evolution; that bisexual reproduction emerges only among anisogamic diploids with a synergistic division of reproductive labor; and that facultative sex is more likely to evolve among haploids practicing assortative mating. Looking at the evolution of sex as a complex system explains better the diversity of sexual strategies known to exist in nature. The paper shows that Analytical mathematics used in theoretical biology has limitations in tackling complex problems. Switching to algorithmic mathematics, such as ABM, will be important in advancing our understanding of complex issues. More sophisticated models will enlighten more aspects of this complex dynamics with implications for the understanding biological and cultural evolution, intelligence, and complex systems in general.

Synergy from reproductive division of labor and complexity drive the evolution of sex
Klaus Jaffe

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## An artificial immune system approach to automated program verification: Towards a theory of undecidability in biological computing

An immune system inspired Artificial Immune System (AIS) algorithm is presented, and is used for the purposes of automated program verification. Relevant immunological concepts are discussed and the field of AIS is briefly reviewed. It is proposed to use this AIS algorithm for a specific automated program verification task: that of predicting shape of program invariants. It is shown that the algorithm correctly predicts program invariant shape for a variety of benchmarked programs. Program invariants encapsulate the computability of a particular program, e.g. whether it performs a particular function correctly and whether it terminates or not. This work also lays the foundation for applying concepts of theoretical incomputability and undecidability to biological systems like the immune system that perform robust computation to eliminate pathogens.

Banerjee S. (2017) An artificial immune system approach to automated program verification: Towards a theory of undecidability in biological computing. PeerJ Preprints 5:e2690v1 https://doi.org/10.7287/peerj.preprints.2690v1

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## A matter of fact

This is a worrying time for those who believe government policies should be based on the best evidence. Pundits claim we've entered a postfactual era. Viral fake news stories spread alternative facts. On some issues, such as climate change and childhood vaccinations, many scientists worry their hard-won research findings have lost sway with politicians and the public, and feel their veracity is under attack. But just how should evidence shape policy? And why does it sometimes lose out? Those are just some of the questions tackled in this special section on evidence-based policymaking.

A matter of fact
David Malakoff
Science  10 Feb 2017:
Vol. 355, Issue 6325, pp. 562-563
DOI: 10.1126/science.355.6325.562

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## A theoretical foundation for multi-scale regular vegetation patterns

Empirically validated mathematical models show that a combination of intraspecific competition between subterranean social-insect colonies and scale-dependent feedbacks between plants can explain the spatially periodic vegetation patterns observed in many landscapes, such as the Namib Desert ‘fairy circles’.

A theoretical foundation for multi-scale regular vegetation patterns

Corina E. Tarnita, Juan A. Bonachela, Efrat Sheffer, Jennifer A. Guyton, Tyler C. Coverdale, Ryan A. Long & Robert M. Pringle

Nature 541, 398–401 (19 January 2017) doi:10.1038/nature20801

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## Shoppers like what they know

Faced with ever-changing products, consumers can benefit from trying new items. But data collected over almost five years show that, the longer shoppers have been buying a favourite product, the more likely they are to stick with it.

Human behaviour: Shoppers like what they know
Peter M. Todd
Nature 541, 294–295 (19 January 2017) doi:10.1038/nature21114

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## Ecosystem restoration strengthens pollination network resilience and functions.

Land degradation results in declining biodiversity and the disruption of ecosystem functioning worldwide, particularly in the tropics1. Vegetation restoration is a common tool used to mitigate these impacts and increasingly aims to restore ecosystem functions rather than species diversity2. However, evidence from community experiments on the effect of restoration practices on ecosystem functions is scarce3. Pollination is an important ecosystem function and the global decline in pollinators attenuates the resistance of natural areas and agro-environments to disturbances4. Thus, the ability of pollination functions to resist or recover from disturbance (that is, the functional resilience)5, 6 may be critical for ensuring a successful restoration process7. Here we report the use of a community field experiment to investigate the effects of vegetation restoration, specifically the removal of exotic shrubs, on pollination. We analyse 64 plant–pollinator networks and the reproductive performance of the ten most abundant plant species across four restored and four unrestored, disturbed mountaintop communities. Ecosystem restoration resulted in a marked increase in pollinator species, visits to flowers and interaction diversity. Interactions in restored networks were more generalized than in unrestored networks, indicating a higher functional redundancy in restored communities. Shifts in interaction patterns had direct and positive effects on pollination, especially on the relative and total fruit production of native plants. Pollinator limitation was prevalent at unrestored sites only, where the proportion of flowers producing fruit increased with pollinator visitation, approaching the higher levels seen in restored plant communities. Our results show that vegetation restoration can improve pollination, suggesting that the degradation of ecosystem functions is at least partially reversible. The degree of recovery may depend on the state of degradation before restoration intervention and the proximity to pollinator source populations in the surrounding landscape5, 8. We demonstrate that network structure is a suitable indicator for pollination quality, highlighting the usefulness of interaction networks in environmental management6, 9.

Via Samir
Marcelo Errera's curator insight,
Restoration goes beyond species and population. The ecological interactions, although invisible, are the ones which make the whole system function.
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## Prediction and its limits

We have tried to predict the future since ancient times when shamans looked for patterns in smoking entrails. As this special section explores, prediction is now a developing science. Essays probe such questions as how to allocate limited resources, whether a country will descend into conflict, and who will likely win an election or publish a high-impact paper, as well as looking at how standards should develop in this emerging field.

Prediction and its limits
Barbara R. Jasny, Richard Stone
Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 468-469
DOI: 10.1126/science.355.6324.468

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## Prediction and explanation in social systems

Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.

Prediction and explanation in social systems
Jake M. Hofman, Amit Sharma, Duncan J. Watts

Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 486-488
DOI: 10.1126/science.aal3856

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## Sequence Memory Constraints Give Rise to Language-Like Structure through Iterated Learning

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language.

Cornish, H., Dale, R., Kirby, S. & Christiansen, M.H. (2017). Sequence memory constraints give rise to language-like structure through iterated learning. PLoS ONE 12(1): e0168532.

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 Suggested by Christoph Riedl

## Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Collaboration to Crowd-Based Problem Solving Performance

Organizations are increasingly turning to crowdsourcing to solve difficult problems. This is often driven by the desire to find the best subject matter experts, strongly incentivize them, and engage them with as little coordination cost as possible. A growing number of authors, however, are calling for increased collaboration in crowdsourcing settings, hoping to draw upon the advantages of teamwork observed in traditional settings. The question is how to effectively incorporate team-based collaboration in a setting that has traditionally been individual-based. We report on a large field experiment of team collaboration on an online platform, in which incentives and team membership were randomly assigned, to evaluate the influence of exogenous inputs (member skills and incentives) and emergent collaboration processes on performance of crowd-based teams. Building on advances in machine learning and complex systems theory, we leverage new measurement techniques to examine the content and timing of team collaboration. We find that temporal “burstiness” of team activity and the diversity of information exchanged among team members are strong predictors of performance, even when inputs such as incentives and member skills are controlled. We discuss implications for research on crowdsourcing and team collaboration.

Preprint at SSRN.

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## Updating Darwin: Information and entropy drive the evolution of life

The evolution of species, according to Darwin, is driven by struggle – by competition between variant autonomous individuals for survival of the fittest and reproductive advantage; the outcome of this struggle for survival is natural selection. The Neo-Darwinians reframed natural selection in terms of DNA: inherited genotypes directly encode expressed phenotypes; a fit phenotype means a fit genotype – thus the evolution of species is the evolution of selfish, reproducing individual genotypes.

Four general characteristics of advanced forms of life are not easily explained by this Neo-Darwinian paradigm: 1) Dependence on cooperation rather than on struggle, manifested by the microbiome, ecosystems and altruism; 2) The pursuit of diversity rather than optimal fitness, manifested by sexual reproduction; 3) Life’s investment in programmed death, rather then in open-ended survival; and 4) The acceleration of complexity, despite its intrinsic fragility.

Here I discuss two mechanisms that can resolve these paradoxical features; both mechanisms arise from viewing life as the evolution of information. Information has two inevitable outcomes; it increases by autocatalyis and it is destroyed by entropy. On the one hand, the autocalalysis of information inexorably drives the evolution of complexity, irrespective of its fragility. On the other hand, only those strategic arrangements that accommodate the destructive forces of entropy survive – cooperation, diversification, and programmed death result from the entropic selection of evolving species. Physical principles of information and entropy thus fashion the evolution of life.

Updating Darwin: Information and entropy drive the evolution of life
Irun R. Cohen

Version 1. F1000Res. 2016; 5: 2808.
Published online 2016 Dec 1. doi:  10.12688/f1000research.10289.1

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