Social Mobilization | Annual Review of Psychology

This article reviews research from several behavioral disciplines to derive strategies for prompting people to perform behaviors that are individually costly and provide negligible individual or social benefits but are meaningful when performed by a large number of individuals. Whereas the term social influence encompasses all the ways in which people influence other people, social mobilization refers specifically to principles that can be used to influence a large number of individuals to participate in such activities. The motivational force of social mobilization is amplified by the fact that others benefit from the encouraged behaviors, and its overall impact is enhanced by the fact that people are embedded within social networks. This article may be useful to those interested in the provision of public goods, collective action, and prosocial behavior, and we give special attention to field experiments on election participation, environmentally sustainable behaviors, and charitable giving.

collective_action  political_economy  public_goods  social_behavior  intervention  review  social_networks  networks  dmce  teaching  via:nyhan 
february 2018
The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers
The growth of the "gig" economy generates worker flexibility that, some have speculated, will favor women. We explore one facet of the gig economy by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. Perhaps most surprisingly, we find that there is a roughly 7% gender earnings gap amongst drivers. The uniqueness of our data—knowing exactly the production and compensation functions—permits us to completely unpack the underlying determinants of the gender earnings gap. We find that the entire gender gap is caused by three factors: experience on the platform (learning-by-doing), preferences over where/when to work, and preferences for driving speed. This suggests that, as the gig economy grows and brings more flexibility in employment, women’s relatively high opportunity cost of non-paid-work time and gender-based preference differences can perpetuate a gender earnings gap even in the absence of discrimination
income  economics  gender  platform_economics  labor  econometrics 
february 2018
Non-equilibrium transitions in multiscale systems with a bifurcating slow manifold - IOPscience
Noise-induced transitions between metastable fixed points in systems evolving on multiple time scales are analyzed in situations where the time scale separation gives rise to a slow manifold with bifurcation. This analysis is performed within the realm of large deviation theory. It is shown that these non-equilibrium transitions make use of a reaction channel created by the bifurcation structure of the slow manifold, leading to vastly increased transition rates. Several examples are used to illustrate these findings, including an insect outbreak model, a system modeling phase separation in the presence of evaporation, and a system modeling transitions in active matter self-assembly. The last example involves a spatially extended system modeled by a stochastic partial differential equation.

multiscale_model  dynamical_system  non-equilibrium  large_deviation  active_matter 
february 2018
Phys. Rev. Lett. 119, 188003 (2017) - Spatiotemporal Self-Organization of Fluctuating Bacterial Colonies
We model an enclosed system of bacteria, whose motility-induced phase separation is coupled to slow population dynamics. Without noise, the system shows both static phase separation and a limit cycle, in which a rising global population causes a dense bacterial colony to form, which then declines by local cell death, before dispersing to reinitiate the cycle. Adding fluctuations, we find that static colonies are now metastable, moving between spatial locations via rare and strongly nonequilibrium pathways, whereas the limit cycle becomes almost periodic such that after each redispersion event the next colony forms in a random location. These results, which hint at some aspects of the biofilm-planktonic life cycle, can be explained by combining tools from large deviation theory with a bifurcation analysis in which the global population density plays the role of control parameter.

multiscale_model  self_organization  non-equilibrium  dynamical_system  collective_dynamics  large_deviation  emergence 
february 2018
[1802.00048] Deceptive Games
Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy. While many games are already deceptive to some extent, we designed a series of games in the Video Game Description Language (VGDL) implementing specific types of deception, classified by the cognitive biases they exploit. VGDL games can be run in the General Video Game Artificial Intelligence (GVGAI) Framework, making it possible to test a variety of existing AI agents that have been submitted to the GVGAI Competition on these deceptive games. Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses. This suggests that we can use deception to understand the capabilities of a game-playing algorithm, and game-playing algorithms to characterize the deception displayed by a game.
artificial_intelligence  adversarial_examples  ?  via:zeynep 
february 2018
Are Toxic Political Conversations Changing How We Feel about Objective Truth? - Scientific American
-- Bad headline writing, overly simplistic essay. Surely, a longer essay could have done better at conveying what the authors couldn't...
political_psychology  cultural_cognition  dmce  teaching  via:nyhan 
february 2018
Why Women’s Voices Are Scarce in Economics - The New York Times
-- But to claim that there is a gendered version of a _file drawer problem_ skewing knowledge construction in economics is a strong statement. Most academics chase grants, fads and prestige, not their passions. This more than anything else explains choice of research topics.
gender  economics  sociology_of_science  social_construction_of_knowledge  justin.wolfers 
february 2018
Coffee, Caffeine, and Health Outcomes: An Umbrella Review | Annual Review of Nutrition
To evaluate the associations between coffee and caffeine consumption and various health outcomes, we performed an umbrella review of the evidence from meta-analyses of observational studies and randomized controlled trials (RCTs). Of the 59 unique outcomes examined in the selected 112 meta-analyses of observational studies, coffee was associated with a probable decreased risk of breast, colorectal, colon, endometrial, and prostate cancers; cardiovascular disease and mortality; Parkinson's disease; and type-2 diabetes. Of the 14 unique outcomes examined in the 20 selected meta-analyses of observational studies, caffeine was associated with a probable decreased risk of Parkinson's disease and type-2 diabetes and an increased risk of pregnancy loss. Of the 12 unique acute outcomes examined in the selected 9 meta-analyses of RCTs, coffee was associated with a rise in serum lipids, but this result was affected by significant heterogeneity, and caffeine was associated with a rise in blood pressure. Given the spectrum of conditions studied and the robustness of many of the results, these findings indicate that coffee can be part of a healthful diet.

-- good news for coffee lovers :)
meta-analysis  health  for_friends 
february 2018
Frictions or Mental Gaps: What’s Behind The Information We (Don't) Use and When Do We Care?
Consumers suffer significant losses from not acting on available information. These losses stem from frictions such as search costs, switching costs, and rational inattention, as well as what we call mental gaps resulting from wrong priors/worldviews, or relevant features of a problem not being top of mind. Most research studying such losses does not empirically distinguish between these mechanisms. Instead, we show that most highly cited papers in this area presume one mechanism underlies consumer choices and assume away other potential explanations, or collapse many mechanisms together. We discuss the empirical difficulties that arise in distinguishing between different mechanisms, and some promising approaches for making progress in doing so. We also assess when it is more or less important for researchers to distinguish between these mechanisms. Approaches that seek to identify true value from demand, without specifying mechanisms behind this wedge, are most useful when researchers are interested in evaluating allocation policies that strongly steer consumers towards better options with regulation, traditional policy instruments, and defaults. On the other hand, understanding the precise mechanisms underlying consumer losses is essential to predicting the impact of mechanism policies aimed primarily at reducing specific frictions or mental gaps without otherwise steering consumers. We make the case that papers engaging with these questions empirically should be clear about whether their analyses distinguish between mechanisms behind poorly informed choices, and what that implies for the questions they can answer. We present examples from several empirical contexts to highlight these distinctions.
behavioral_economics  information  heuristics  dmce  teaching  via:sunstein 
february 2018
Political Structures and Political Mores: Varieties of Politics in Comparative Perspective | Sociological Science
We offer an integrated study of political participation, bridging the gap between the literatures on civic engagement and social movements. Historically evolved institutions and culture generate different configurations of the political domain, shaping the meaning and forms of political activity in different societies. The structuration of the polity along the dimensions of “stateness” and “corporateness” accounts for cross-national differences in the way individuals make sense of and engage in the political sphere. Forms of political participation that are usually treated as istinct are actually interlinked and co-vary across national configurations. In societies where interests are represented in a formalized manner through corporatist arrangements, political participation revolves primarily around membership in pre-established groups and concerted negotiation, rather than extra-institutional types of action. By contrast, in “statist” societies the centralization and concentration of sovereignty in the state makes it the focal point of claim-making, driving social actors to engage in “public” activities and marginalizing private and, especially, market-based political forms. We test these and other hypotheses using cross-national data on political participation from the World Values Survey.
political_science  institutions  protests  revolutions  social_movements  collective_action  comparative  civic_engagement  civil_disobidience  political_sociology 
february 2018
Ordinalization: Lewis A. Coser Memorial Award for Theoretical Agenda Setting 2014 - Marion Fourcade, 2016
We can think of three basic principles of classificatory judgment for comparing things and people. I call these judgments nominal (oriented to essence), cardinal (oriented to quantities), and ordinal (oriented to relative positions). Most social orders throughout history are organized around the intersection of these different types. In line with the ideals of political liberalism, however, democratic societies have developed an arsenal of institutions to untangle nominal and ordinal judgments in various domains of social life. In doing so, I suggest, they have contributed to the parallel amplification of both. In this article, I specifically discuss the socio-technical channels through which ordinal judgments are now elaborated, a process I call ordinalization. I conclude by exploring the political and economic possibilities of a society in which ordinal processes are ubiquitous.
economic_sociology  inequality  social_theory  institutions  historical_sociology  comparative 
february 2018
Elements of Causal Inference: Foundations and Learning Algorithms | The MIT Press
"The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.
"After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.
"The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts."
book  causal_inference  graphical_models  causal_discovery  machine_learning  statistics  via:cshalizi 
january 2018
[1708.02890] Asymptotic equivalence of probability measures and stochastic processes
Let Pn and Qn be two probability measures representing two different probabilistic models of some system (e.g., an n-particle equilibrium system, a set of random graphs with n vertices, or a stochastic process evolving over a time n) and let Mn be a random variable representing a 'macrostate' or 'global observable' of that system. We provide sufficient conditions, based on the Radon-Nikodym derivative of Pn and Qn, for the set of typical values of Mn obtained relative to Pn to be the same as the set of typical values obtained relative to Qn in the limit n→∞. This extends to general probability measures and stochastic processes the well-known thermodynamic-limit equivalence of the microcanonical and canonical ensembles, related mathematically to the asymptotic equivalence of conditional and exponentially-tilted measures. In this more general sense, two probability measures that are asymptotically equivalent predict the same typical or macroscopic properties of the system they are meant to model.
non-equilibrium  statistical_mechanics  probability  large_deviation  stochastic_process 
january 2018
[1705.06492] Introduction to dynamical large deviations of Markov processes
These notes give a summary of techniques used in large deviation theory to study the fluctuations of time-additive quantities, called dynamical observables, defined in the context of Langevin-type equations, which model equilibrium and nonequilibrium processes driven by external forces and noise sources. These fluctuations are described by large deviation functions, obtained by solving a dominant eigenvalue problem similar to the problem of finding the ground state energy of quantum systems. This analogy is used to explain the differences that exist between the fluctuations of equilibrium and nonequilibrium processes. An example involving the Ornstein-Uhlenbeck process is worked out in detail to illustrate these methods. Exercises, at the end of the notes, also complement the theory.
non-equilibrium  statistical_mechanics  large_deviation  tutorial 
january 2018
The Captured Economy - Brink Lindsey; Steven Teles - Oxford University Press
For years, America has been plagued by slow economic growth and increasing inequality. Yet economists have long taught that there is a tradeoff between equity and efficiency-that is, between making a bigger pie and dividing it more fairly. That is why our current predicament is so puzzling: today, we are faced with both a stagnating economy and sky-high inequality.

In The Captured Economy , Brink Lindsey and Steven M. Teles identify a common factor behind these twin ills: breakdowns in democratic governance that allow wealthy special interests to capture the policymaking process for their own benefit. They document the proliferation of regressive regulations that redistribute wealth and income up the economic scale while stifling entrepreneurship and innovation. When the state entrenches privilege by subverting market competition, the tradeoff between equity and efficiency no longer holds.

Over the past four decades, new regulatory barriers have worked to shield the powerful from the rigors of competition, thereby inflating their incomes-sometimes to an extravagant degree. Lindsey and Teles detail four of the most important cases: subsidies for the financial sector's excessive risk taking, overprotection of copyrights and patents, favoritism toward incumbent businesses through occupational licensing schemes, and the NIMBY-led escalation of land use controls that drive up rents for everyone else.

Freeing the economy from regressive regulatory capture will be difficult. Lindsey and Teles are realistic about the chances for reform, but they offer a set of promising strategies to improve democratic deliberation and open pathways for meaningful policy change. An original and counterintuitive interpretation of the forces driving inequality and stagnation, The Captured Economy will be necessary reading for anyone concerned about America's mounting economic problems and the social tensions they are sparking.

book  bureaucracy  governance  regulation  administrative_state  critique 
january 2018
Artificial Intelligence’s ‘Black Box’ Is Nothing to Fear - The New York Times
--overly simplifying article. Yes, experts do have their _black box_ heuristics but they are benchmarked by institutions. What we need is a trade school for robots;) and a certification exam for those systems after they get through their school.
risk_assessment  artificial_intelligence  machine_learning  robots  phobia  expert_judgment  prediction  NYTimes 
january 2018
Homeward | RSF
In the era of mass incarceration, over 600,000 people are released from federal or state prison each year, with many returning to chaotic living environments rife with violence. In these circumstances, how do former prisoners navigate reentering society? In Homeward, sociologist Bruce Western examines the tumultuous first year after release from prison. Drawing from in-depth interviews with over one hundred individuals, he describes the lives of the formerly incarcerated and demonstrates how poverty, racial inequality, and failures of social support trap many in a cycle of vulnerability despite their efforts to rejoin society.

Western and his research team conducted comprehensive interviews with men and women released from the Massachusetts state prison system who returned to neighborhoods around Boston. Western finds that for most, leaving prison is associated with acute material hardship. In the first year after prison, most respondents could not afford their own housing and relied on family support and government programs, with half living in deep poverty. Many struggled with chronic pain, mental illnesses, or addiction—the most important predictor of recidivism. Most respondents were also unemployed. Some older white men found union jobs in the construction industry through their social networks, but many others, particularly those who were black or Latino, were unable to obtain full-time work due to few social connections to good jobs, discrimination, and lack of credentials. Violence was common in their lives, and often preceded their incarceration. In contrast to the stereotype of tough criminals preying upon helpless citizens, Western shows that many former prisoners were themselves subject to lifetimes of violence and abuse and encountered more violence after leaving prison, blurring the line between victims and perpetrators.

Western concludes that boosting the social integration of former prisoners is key to both ameliorating deep disadvantage and strengthening public safety. He advocates policies that increase assistance to those in their first year after prison, including guaranteed housing and health care, drug treatment, and transitional employment. By foregrounding the stories of people struggling against the odds to exit the criminal justice system, Homeward shows how overhauling the process of prisoner reentry and rethinking the foundations of justice policy could address the harms of mass incarceration.
book  ethnography  economic_sociology  poverty  development_economics  united_states_of_america 
january 2018
Phys. Rev. E 97, 012306 (2018) - Nonparametric weighted stochastic block models
We present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization. Our method is nonparametric, and thus does not require the prior knowledge of the number of groups or other dimensions of the model, which are instead inferred from data. We give a comprehensive treatment of different kinds of edge weights (i.e., continuous or discrete, signed or unsigned, bounded or unbounded), as well as arbitrary weight transformations, and describe an unsupervised model selection approach to choose the best network description. We illustrate the application of our method to a variety of empirical weighted networks, such as global migrations, voting patterns in congress, and neural connections in the human brain.
networks  teaching 
january 2018
Diversity of meso-scale architecture in human and non-human connectomes | Nature Communications
Brain function is reflected in connectome community structure. The dominant view is that communities are assortative and segregated from one another, supporting specialized information processing. However, this view precludes the possibility of non-assortative communities whose complex inter-community interactions could engender a richer functional repertoire. We use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila, mouse, rat, macaque, and human connectomes. We find that most communities are assortative, though others form core-periphery and disassortative structures, which better recapitulate observed patterns of functional connectivity and gene co-expression in human and mouse connectomes compared to standard community detection techniques. We define measures for quantifying the diversity of communities in which brain regions participate, showing that this measure is peaked in control and subcortical systems in humans, and that inter-individual differences are correlated with cognitive performance. Our report paints a more diverse portrait of connectome communities and demonstrates their cognitive relevance.

connectome  neuroscience  networks  teaching  neural_connectivity  complexity  via:clauset 
january 2018
[1706.06690] The Rise and Fall of Network Stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time
Trends change rapidly in today's world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symbolized by vertices that "shine." That is, at a specific time interval in a network's life, certain vertices become increasingly connected to other vertices. This process creates new high-degree vertices, i.e., network stars. Thus, to study trends, we must look at how networks evolve over time and determine how the stars behave. In our research, we constructed the largest publicly available network evolution dataset to date, which contains 38,000 real-world networks and 2.5 million graphs. Then, we performed the first precise wide-scale analysis of the evolution of networks with various scales. Three primary observations resulted: (a) links are most prevalent among vertices that join a network at a similar time; (b) the rate that new vertices join a network is a central factor in molding a network's topology; and (c) the emergence of network stars (high-degree vertices) is correlated with fast-growing networks. We applied our learnings to develop a flexible network-generation model based on large-scale, real-world data. This model gives a better understanding of how stars rise and fall within networks, and is applicable to dynamic systems both in nature and society.

networks  dynamics  time_series  teaching  code 
january 2018
Forming Beliefs: Why Valence Matters - ScienceDirect
One of the most salient attributes of information is valence: whether a piece of news is good or bad. Contrary to classic learning theories, which implicitly assume beliefs are adjusted similarly regardless of valence, we review evidence suggesting that different rules and mechanisms underlie learning from desirable and undesirable information. For self-relevant beliefs this asymmetry generates a positive bias, with significant implications for individuals and society. We discuss the boundaries of this asymmetry, characterize the neural system supporting it, and describe how changes in this circuit are related to individual differences in behavior.
cognitive_science  neuroscience  judgment_decision-making  dmce  teaching 
january 2018
Critical Thinking - Statistical Reasoning and Intuitive Judgment | Columbia University Press
Life is fundamentally uncertain. We do not know whether it will rain, whether the market will go up or down, whether our unhealthy eating choices will have serious consequences, or whether terrorists will strike our city. To make matters worse, we also lack a tried and true procedure for evaluating the likelihood of such events. Yet we are required to make decisions great and small that depend on these events. In the absence of certainty or an objective procedure for estimating probabilities, we must rely on our own reasoning, which a great deal of research has shown to be less rational than we would like to believe.

In Critical Thinking, Varda Liberman and Amos Tversky examine how we make judgments under uncertainty and explain how various biases can distort our consideration of evidence. Using everyday examples, they detail how to examine data and their implications with the goal of helping readers improve their intuitive reasoning and judgment. From the courtroom to the basketball court, cholesterol count to the existence of the supernatural, Liberman and Tversky explore the fundamental insights of probability, causal relationships, and making inferences from samples. They delve into the psychology of judgment, explaining why first impressions are often wrong and correct answers go against our intuitions. Originally written in Hebrew and published by the Open University in 1996, Critical Thinking is an essential guide for students and interested readers alike that teaches us to become more critical readers and consumers of information.
history_of_ideas  statistics  judgment_decision-making  heuristics  dmce  teaching  book 
january 2018
Lessons from the Lobster | The MIT Press
"Neuroscientist Eve Marder has spent forty years studying thirty neurons in the stomach of a lobster. Her focus on this tiny network of cells has yielded valuable insights into the much more complex workings of the human brain; she has become a leading voice in neuroscience. In Lessons from the Lobster, Charlotte Nassim describes Marder’s work and its significance accessibly and engagingly, tracing the evolution of a supremely gifted scientist’s ideas.
"From the lobster's digestion to human thought is very big leap indeed. Our brains selectively recruit networks from about ninety billion available neurons; the connections are extremely complex. Nevertheless, as Nassim explains, Marder’s study of a microscopic knot of stomatogastric neurons in lobsters and crabs, a small network with a countable number of neurons, has laid vital foundations for current brain research projects.
"Marder’s approach is as intuitive as it is analytic, but always firmly anchored to data. Every scrap of information is a pointer for Marder; her discoveries depend on her own creative thinking as much as her laboratory’s findings. Nassim describes Marder’s important findings on neuromodulation, the secrets of neuronal networks, and homeostasis. Her recognition of the importance of animal-to-animal variability has influenced research methods everywhere.
"Marder has run her laboratory at Brandeis University since 1978. She was President of the Society for Neuroscience in 2008 and she is the recipient of numerous awards, including the 2016 Kavli Award in Neuroscience and the 2013 Gruber Prize in Neuroscience. Research that reaches the headlines often depends on technical fireworks, and especially on spectacular images. Marder's work seldom fits that pattern, but this book demonstrates that a brilliant scientist working carefully and thoughtfully can produce groundbreaking results."
book  neuroscience  comparative  biography  people  ethology  via:cshalizi 
january 2018
The Role of Anger in the Biased Assimilation of Political Information - Suhay - 2018 - Political Psychology - Wiley Online Library
Political psychologists have established that politically motivated reasoning is a common phenomenon; however, the field knows comparatively less about the psychological mechanisms that drive it. Drawing on advances in the understanding of the relevance of emotion to political reasoning and behavior, we argue that anger likely plays a major role in motivating individuals to engage in the biased assimilation of political information—an evaluative bias in favor of information that bolsters one's views and against information that undercuts them. We test this proposition with two online studies, the second of which includes a quasi-representative sample of Americans. The studies support our expectations. Individuals felt more negative emotions toward arguments that undermined their attitudes and positive emotions toward arguments that confirmed them; however, anger was nearly alone in fueling biased reactions to issue arguments.
political_psychology  cultural_cognition  dmce  teaching  via:nyhan 
january 2018
Simpler Math Tames the Complexity of Microbe Networks | Quanta Magazine
-- They should never have published this piece and not keeping up with their usually high standards. The profiled study has serious limitations.
network_inference  network_data_analysis  microbiome  methods  dynamics  networks  teaching  quanta_mag 
january 2018
The Impact of Health on Labor Market Outcomes: Experimental Evidence from MRFIT
While economists have posited that health investments increase earnings, isolating the causal effect of health is challenging due both to reverse causality and unobserved heterogeneity. We examine the labor market effects of a randomized controlled trial, the Multiple Risk Factor Intervention Trial (MRFIT), which monitored nearly 13,000 men for over six years. We find that this intervention, which provided a bundle of treatments to reduce coronary heart disease mortality, increased earnings and family income. We find few differences in estimated gains by baseline health and occupation characteristics. Reductions in serious illnesses and work-limiting disabilities likely contributed to the observed gains.
health  labor  productivity  wealth  inequality  economics  experiments  microeconomics  public_goods  for_friends  via:noa 
january 2018
Fighting over burrows: the emergence of dominance hierarchies in the Norway lobster (Nephrops norvegicus) | Journal of Experimental Biology
Animals fight over resources such as mating partners, territory, food or shelter and repeated contests lead to stable social hierarchies in different phyla. The group dynamics of hierarchy formation are not characterized in the Norway lobster (Nephrops norvegicus). Lobsters spend most of the day in burrows and forage outside of them according to a diel (i.e. 24 h-based) activity rhythm. Here, we use a linear and generalized mixed model approach to analyse, in seven groups of four male lobsters, the formation of dominance hierarchies and rank-related changes in burrowing behaviour. We show that hierarchies emerge within 1–3 days and increase in steepness over a period of 5 days, while rank changes and number of fights gradually decrease over a 5-day period. The rank position determined by open area fights predicts the outcome of fights over burrows, the time spent in burrows, and the locomotor activity levels. Dominant lobsters are more likely to evict subordinate lobsters from their burrows and are more successful in defending their own burrows. They spend more time in burrows and display lower levels of locomotor activity outside the burrow. Lobsters do not change their diel activity rhythms as a result of a change in rank, and all tested individuals showed higher activity at night and dusk compared with dawn and daytime. We discuss how behavioural changes in burrowing behaviour could lead to rank-related benefits such as reduced exposure to predators and energy savings.
social_behavior  comparative  animal_behavior  model_organism  neuroscience 
january 2018
Statistical Recovery of Discrete, Geometric and Invariant Structures
The main objective of the workshop was to bring together researchers in mathematical statistics and related areas in order to discuss recent advances and problems associated with statistical recovery of geometric and invariant structures. Topics include adaptive estimation, confidence sets and testing techniques, as well as statistical algorithms for geometrical structure recovery and data analysis.
statistics  geometry  foundations  workshop  report 
january 2018
[1610.09051] The Geometry of Synchronization Problems and Learning Group Actions
We develop a geometric framework that characterizes the synchronization problem --- the problem of consistently registering or aligning a collection of objects. The theory we formulate characterizes the cohomological nature of synchronization based on the classical theory of fibre bundles. We first establish the correspondence between synchronization problems in a topological group G over a connected graph Γ and the moduli space of flat principal G-bundles over Γ, and develop a discrete analogy of the renowned theorem of classifying flat principal bundles with fix base and structural group using the representation variety. In particular, we show that prescribing an edge potential on a graph is equivalent to specifying an equivalence class of flat principal bundles, of which the triviality of holonomy dictates the synchronizability of the edge potential. We then develop a twisted cohomology theory for associated vector bundles of the flat principal bundle arising from an edge potential, which is a discrete version of the twisted cohomology in differential geometry. This theory realizes the obstruction to synchronizability as a cohomology group of the twisted de Rham cochain complex. We then build a discrete twisted Hodge theory --- a fibre bundle analog of the discrete Hodge theory on graphs --- which geometrically realizes the graph connection Laplacian as a Hodge Laplacian of degree zero. Motivated by our geometric framework, we study the problem of learning group actions --- partitioning a collection of objects based on the local synchronizability of pairwise correspondence relations. A dual interpretation is to learn finitely generated subgroups of an ambient transformation group from noisy observed group elements. A synchronization-based algorithm is also provided, and we demonstrate its efficacy using simulations and real data.
statistics  dynamical_system  synchronization  differential_geometry 
january 2018
[1204.6265] Statistical inference for dynamical systems: a review
The topic of statistical inference for dynamical systems has been studied extensively across several fields. In this survey we focus on the problem of parameter estimation for non-linear dynamical systems. Our objective is to place results across distinct disciplines in a common setting and highlight opportunities for further research.
statistics  dynamical_system  differential_geometry  topological_data_analysis  review 
january 2018
[1801.03400] Scale-free networks are rare
A central claim in modern network science is that real-world networks are typically "scale free," meaning that the fraction of nodes with degree k follows a power law, decaying like k−α, often with 2<α<3. However, empirical evidence for this belief derives from a relatively small number of real-world networks. We test the universality of scale-free structure by applying state-of-the-art statistical tools to a large corpus of nearly 1000 network data sets drawn from social, biological, technological, and informational sources. We fit the power-law model to each degree distribution, test its statistical plausibility, and compare it via a likelihood ratio test to alternative, non-scale-free models, e.g., the log-normal. Across domains, we find that scale-free networks are rare, with only 4% exhibiting the strongest-possible evidence of scale-free structure and 52% exhibiting the weakest-possible evidence. Furthermore, evidence of scale-free structure is not uniformly distributed across sources: social networks are at best weakly scale free, while a handful of technological and biological networks can be called strongly scale free. These results undermine the universality of scale-free networks and reveal that real-world networks exhibit a rich structural diversity that will likely require new ideas and mechanisms to explain.
aaron.clauset  networks  teaching 
january 2018
Do Political Protests Matter? Evidence from the Tea Party Movement* | The Quarterly Journal of Economics | Oxford Academic
Can protests cause political change, or are they merely symptoms of underlying shifts in policy preferences? We address this question by studying the Tea Party movement in the United States, which rose to prominence through coordinated rallies across the country on Tax Day, April 15, 2009. We exploit variation in rainfall on the day of these rallies as an exogenous source of variation in attendance. We show that good weather at this initial, coordinating event had significant consequences for the subsequent local strength of the movement, increased public support for Tea Party positions, and led to more Republican votes in the 2010 midterm elections. Policy making was also affected, as incumbents responded to large protests in their district by voting more conservatively in Congress. Our estimates suggest significant multiplier effects: an additional protester increased the number of Republican votes by a factor well above 1. Together our results show that protests can build political movements that ultimately affect policy making and that they do so by influencing political views rather than solely through the revelation of existing political preferences
protests  collective_action  political_science  social_movements  us_politics 
january 2018
The New Conspiracists | Dissent Magazine
--misleading title, the article is more about use of disinformation, conspiratorial factoids with the dismantling of democratic institutions and administrative state in mind.
dissent_mag  administrative_state  institutions  conspiracy_theories  epidemiology_of_representations  political_psychology  cultural_cognition  dmce  networks  teaching  ? 
january 2018
[1712.09665] Adversarial Patch
"We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class."
deep_learning  adversarial_examples  via:cshalizi 
january 2018
Comparing the axiomatic and ecological approaches to rationality: fundamental agreement theorems in SCOP | SpringerLink
"There are two prominent viewpoints regarding the nature of rationality and how it should be evaluated in situations of interest: the traditional axiomatic approach and the newer ecological rationality. An obstacle to comparing and evaluating these seemingly opposite approaches is that they employ different language and formalisms, ask different questions, and are at different stages of development. I adapt a formal framework known as SCOP to address this problem by providing a comprehensive common framework in which both approaches may be defined and compared. The main result is that the axiomatic and ecological approaches are in far greater agreement on fundamental issues than has been appreciated; this is supported by a pair of theorems to the effect that they will make accordant rationality judgements when forced to evaluate the same situation. I conclude that ecological rationality has some subtle advantages, but that we should move past the issues currently dominating the discussion of rationality."
rationality  debates  foundations  models_of_behavior  via:cshalizi 
january 2018
Understanding and Misunderstanding Randomized Controlled Trials
RCTs would be more useful if there were more realistic expectations of them and if their pitfalls were better recognized. For example, and contrary to many claims in the applied literature, randomization does not equalize everything but the treatment across treatments and controls, it does not automatically deliver a precise estimate of the average treatment effect (ATE), and it does not relieve us of the need to think about (observed or unobserved) confounders. Estimates apply to the trial sample only, sometimes a convenience sample, and usually selected; justification is required to extend them to other groups, including any population to which the trial sample belongs. Demanding “external validity” is unhelpful because it expects too much of an RCT while undervaluing its contribution. Statistical inference on ATEs involves hazards that are not always recognized. RCTs do indeed require minimal assumptions and can operate with little prior knowledge. This is an advantage when persuading distrustful audiences, but it is a disadvantage for cumulative scientific progress, where prior knowledge should be built upon and not discarded. RCTs can play a role in building scientific knowledge and useful predictions but they can only do so as part of a cumulative program, combining with other methods, including conceptual and theoretical development, to discover not “what works,” but “why things work”.


experiments  intervention  public_policy  social_science  methods  critique  angus.deaton 
january 2018
How Newspapers Reveal Political Power* | Political Science Research and Methods | Cambridge Core
Political science is in large part the study of power, but power itself is difficult to measure. We argue that we can use newspaper coverage—in particular, the relative amount of space devoted to particular subjects in newspapers—to measure the relative power of an important set of political actors and offices. We use a new dataset containing nearly 50 million historical newspaper pages from 2,700 local US newspapers over the years 1877–1977. We define and discuss a measure of power we develop based on observed word frequencies, and we validate it through a series of analyses. Overall, we find that the relative coverage of political actors and of political offices is a strong indicator of political power for the cases we study. To illustrate its usefulness, we then apply the measure to understand when (and where) state party committees lost their power. Taken together, the paper sheds light on the nature of political news coverage and offers both a new dataset and a new measure for studying political power in a wide set of contexts.
news_media  political_science  natural_language_processing  text_mining  journalism  via:nyhan 
january 2018
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