Human social sensing is an untapped resource for computational social science.

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

Strategic identity signaling in heterogeneous networks.

SignificanceMuch of online conversation today consists of signaling one's political identity. Although many signals are obvious to everyone, others are covert, recognizable to one's ingroup while obscured from the outgroup. This type of covert identity signaling is critical for collaborations in a diverse society, but measuring covert signals has been difficult, slowing down theoretical development. We develop a method to detect covert and overt signals in tweets posted before the 2020 US presidential election and use a behavioral experiment to test predictions of a mathematical theory of covert signaling. Our results show that covert political signaling is more common when the perceived audience is politically diverse and open doors to a better understanding of communication in politically polarized societies.

Dynamical system model predicts when social learners impair collective performance.

A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that, when the proportion of social learners exceeds a critical threshold, a bistable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics.

Stewardship of global collective behavior.

Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a "crisis discipline" just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

Sampling complex social and behavioral phenomena.

We comment on the limits of relying on prior literature when constructing the design space for an integrative experiment; the adaptive nature of social and behavioral phenomena and the implications for the use of theory and modeling when constructing the design space; and on the challenges of measuring random errors and lab-related biases in measurement without replication.

We need more precise, quantitative models of sentiments.

The constructs of attitudes, emotions, and sentiments are often only verbally defined and therefore somewhat vague. The sentiment construct might be fruitfully modeled as a result of sampling processes, complementing the Attitude-Scenario-Emotion model in explaining similarities and differences in sentiments across different cultures.

On defensive decision making: how doctors make decisions for their patients.

BACKGROUND: In the past decade, the number of lawsuits for medical malpractice has risen significantly. This could affect the way doctors make decisions for their patients. OBJECTIVE: To investigate whether and why doctors practice defensive medicine with their patients. DESIGN: A questionnaire study was conducted in general practice departments of eight metropolitan hospitals in Spain, between January and February 2010. SETTING AND PARTICIPANTS: Eighty general practitioners (48% men; mean age 52 years) with an average of 15.3 years of experience and their 80 adult patients (42% men; mean age 56 years) participated in the study. MAIN OUTCOME MEASUREMENTS: Participants completed a self-administered questionnaire involving choices between a risky and a conservative treatment. One group of doctors made decisions for their patients. Another group of doctors predicted what their patients would decide for themselves. Finally, all doctors and patients made decisions for themselves and described the factors they thought influenced their decisions. RESULTS: Doctors selected much more conservative medical treatments for their patients than for themselves. Most notably, they did so even when they accurately predicted that the patients would select riskier treatments. When asked about the reasons for their decisions, most doctors (93%) reported fear of legal consequences. DISCUSSION AND CONCLUSIONS: Doctors' decisions for their patients are strongly influenced by concerns of possible legal consequences. Patients therefore cannot blindly follow their doctor's advice. Our study, however, suggests a plausible method that patients could use to get around this problem: They could simply ask their doctor what he or she would do in the patient's situation.

Analogies for modeling belief dynamics.

Belief dynamics has an important role in shaping our responses to natural and societal phenomena, ranging from climate change and pandemics to immigration and conflicts. Researchers often base their models of belief dynamics on analogies to other systems and processes, such as epidemics or ferromagnetism. Similar to other analogies, analogies for belief dynamics can help scientists notice and study properties of belief systems that they would not have noticed otherwise (conceptual mileage). However, forgetting the origins of an analogy may lead to some less appropriate inferences about belief dynamics (conceptual baggage). Here, we review various analogies for modeling belief dynamics, discuss their mileage and baggage, and offer recommendations for using analogies in model development.

Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers.

BACKGROUND: Making evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients' risk perception and leads to better informed decision making. This paper summarises current "best practices" in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools. METHOD: An expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a "state of the art" summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results. RESULTS: The eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid "1 in x" formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience. CONCLUSION: A substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.

Does young adults' preferred role in decision making about health, money, and career depend on their advisors' leadership skills?

Few empirical data exist on how decision making about health differs from that in other crucial life domains with less threatening consequences. To shed light on this issue we conducted a study with 175 young adults (average age 19 years). We presented the participants with scenarios involving advisors who provided assistance in making decisions about health, money, and career. For each scenario, participants were asked to what extent they wanted the advisor to exhibit several leadership styles and competencies and what role (active, collaborative, or passive) they preferred to play when making decisions. Results show that decision making about health is distinct from that in the other domains in three ways. First, most of the participants preferred to delegate decision making about their health to their physician, whereas they were willing to collaborate or play an active role in decision making about their career or money. Second, the competencies and leadership style preferred for the physician differed substantially from those desired for advisors in the other two domains: Participants expected physicians to show more transformational leadership--the style that is most effective in a wide range of environments--than those who provide advice about financial investments or career. Finally, participants' willingness to share medical decision making with their physician was tied to how strongly they preferred that the physician shows an effective leadership style. In contrast, motivation to participate in decision making in the other domains was not related to preferences regarding advisors' leadership style or competencies. Our results have implications for medical practice as they suggest that physicians are expected to have superior leadership skills compared to those who provide assistance in other important areas of life.

Group threat, political extremity, and collective dynamics in online discussions.

Collectives adapt their network structure to the challenges they face. It has been hypothesized that collectives experiencing a real or imagined threat from an outgroup tend to consolidate behind a few group members, and that network structures in which a few members attract most of the attention are more likely in politically extreme groups. These hypotheses have not been tested in large-scale real-world settings. We reconstruct networks of tens of thousands of commenters participating in comment sections of high-profile U.S. political news websites spanning the political spectrum from left to right, including Mother Jones, The Atlantic, The Hill, and Breitbart. We investigate the relationship between different indices of inequality of attention in commenters' networks and perceived group threat associated with significant societal events, from elections and political rallies to mass shootings. Our findings support the hypotheses that groups facing a real or imagined outgroup threat and groups that are more politically extreme are more likely to attend to a few high-profile members. These results provide an extensive real-world test of theoretical accounts of collective adaptation to outgroup threats.

Gender and retention patterns among U.S. faculty.

Women remain underrepresented among faculty in nearly all academic fields. Using a census of 245,270 tenure-track and tenured professors at United States-based PhD-granting departments, we show that women leave academia overall at higher rates than men at every career age, in large part because of strongly gendered attrition at lower-prestige institutions, in non-STEM fields, and among tenured faculty. A large-scale survey of the same faculty indicates that the reasons faculty leave are gendered, even for institutions, fields, and career ages in which retention rates are not. Women are more likely than men to feel pushed from their jobs and less likely to feel pulled toward better opportunities, and women leave or consider leaving because of workplace climate more often than work-life balance. These results quantify the systemic nature of gendered faculty retention; contextualize its relationship with career age, institutional prestige, and field; and highlight the importance of understanding the gendered reasons for attrition rather than focusing on rates alone.

Beyond collective intelligence: Collective adaptation.

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

Social sampling explains apparent biases in judgments of social environments.

How people assess their social environments plays a central role in how they evaluate their life circumstances. Using a large probabilistic national sample, we investigated how accurately people estimate characteristics of the general population. For most characteristics, people seemed to underestimate the quality of others' lives and showed apparent self-enhancement, but for some characteristics, they seemed to overestimate the quality of others' lives and showed apparent self-depreciation. In addition, people who were worse off appeared to enhance their social position more than those who were better off. We demonstrated that these effects can be explained by a simple social-sampling model. According to the model, people infer how others are doing by sampling from their own immediate social environments. Interplay of these sampling processes and the specific structure of social environments leads to the apparent biases. The model predicts the empirical results better than alternative accounts and highlights the importance of considering environmental structure when studying human cognition.

Relationship Between COVID-19 Threat Beliefs and Individual Differences in Demographics, Personality, and Related Beliefs.

Individual differences in demographics, personality, and other related beliefs are associated with coronavirus disease 2019 (COVID-19) threat beliefs. However, the relative contributions of these different types of individual differences to COVID-19 threat beliefs are not known. In this study, a total of 1,700 participants in Croatia (68% female; age 18-86 years) completed a survey that included questions about COVID-19 risks, questions about related beliefs including vaccination beliefs, trust in the health system, trust in scientists, and trust in the political system, the HEXACO 60 personality inventory, as well as demographic questions about gender, age, chronic diseases, and region. We used hierarchical regression analyses to examine the proportion of variance explained by demographics, personality, and other related beliefs. All three types of individual differences explained a part of the variance of COVID-19 threat beliefs, with related beliefs explaining the largest part. Personality facets explained a slightly larger amount of variance than personality factors. These results have implications for communication about COVID-19.

Cognitive Science of Augmented Intelligence.

Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human-machine interaction, group behavior, and community organization, the individual-centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real-world challenges that can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other.

Socioeconomic roots of academic faculty.

Despite the special role of tenure-track faculty in society, training future researchers and producing scholarship that drives scientific and technological innovation, the sociodemographic characteristics of the professoriate have never been representative of the general population. Here we systematically investigate the indicators of faculty childhood socioeconomic status and consider how they may limit efforts to diversify the professoriate. Combining national-level data on education, income and university rankings with a 2017-2020 survey of 7,204 US-based tenure-track faculty across eight disciplines in STEM, social science and the humanities, we show that faculty are up to 25 times more likely to have a parent with a Ph.D. Moreover, this rate nearly doubles at prestigious universities and is stable across the past 50 years. Our results suggest that the professoriate is, and has remained, accessible disproportionately to the socioeconomically privileged, which is likely to deeply shape their scholarship and their reproduction.

Enhancing understanding and recall of quantitative information about medical risks: a cross-cultural comparison between Germany and Spain.

In two experiments, we analyzed cross-cultural differences in understanding and recalling information about medical risks in two countries--Germany and Spain--whose students differ substantially in their quantitative literacy according to the 2003 Programme for International Student Assessment (PISA; OECD, 2003, 2010). We further investigated whether risk understanding can be enhanced by using visual aids (Experiment 1), and whether different ways of describing risks affect recall (Experiment 2). Results showed that Spanish students are more vulnerable to misunderstanding and forgetting the risk information than their German counterparts. Spanish students, however, benefit more than German students from representing the risk information using ecologically rational formats--which exploit the way information is represented in the human mind. We concluded that our results can have important implications for clinical practice.

Integrating social and cognitive aspects of belief dynamics: towards a unifying framework.

Belief change and spread have been studied in many disciplines-from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics-but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.

The unequal impact of parenthood in academia.

Across academia, men and women tend to publish at unequal rates. Existing explanations include the potentially unequal impact of parenthood on scholarship, but a lack of appropriate data has prevented its clear assessment. Here, we quantify the impact of parenthood on scholarship using an extensive survey of the timing of parenthood events, longitudinal publication data, and perceptions of research expectations among 3064 tenure-track faculty at 450 Ph.D.-granting computer science, history, and business departments across the United States and Canada, along with data on institution-specific parental leave policies. Parenthood explains most of the gender productivity gap by lowering the average short-term productivity of mothers, even as parents tend to be slightly more productive on average than nonparents. However, the size of productivity penalty for mothers appears to have shrunk over time. Women report that paid parental leave and adequate childcare are important factors in their recruitment and retention. These results have broad implications for efforts to improve the inclusiveness of scholarship.