Social learning and partisan bias in the interpretation of climate trends

Tracking politically motivated reasoning in the brain: the role of mentalizing, value-encoding, and error detection networks

Susceptibility to misinformation and belief polarization often reflects people's tendency to incorporate information in a biased way. Despite the presence of competing theoretical models, the underlying neurocognitive mechanisms of motivated reasoning remain elusive as previous empirical work did not properly track the belief formation process. To address this problem, we employed a design that identifies motivated reasoning as directional deviations from a Bayesian benchmark of unbiased belief updating. We asked the members of a proimmigration or an anti-immigration group regarding the extent to which they endorse factual messages on foreign criminality, a polarizing political topic. Both groups exhibited a desirability bias by overendorsing attitude-consistent messages and underendorsing attitude-discrepant messages and an identity bias by overendorsing messages from in-group members and underendorsing messages from out-group members. In both groups, neural responses to the messages predicted subsequent expression of desirability and identity biases, suggesting a common neural basis of motivated reasoning across ideologically opposing groups. Specifically, brain regions implicated in encoding value, error detection, and mentalizing tracked the degree of desirability bias. Less extensive activation in the mentalizing network tracked the degree of identity bias. These findings illustrate the distinct neurocognitive architecture of desirability and identity biases and inform existing cognitive models of politically motivated reasoning.

Audience segmentation approach to conservation messaging for transforming the exotic pet trade

Advancing transformative change for sustainability requires population-wide behavior change. Yet, many behavioral interventions tackling environmental problems only examine average effects on the aggregate, overlooking the heterogeneous effects in a population. We developed and preregistered a novel audience segmentation approach to test the diverse impact of conservation messaging on reducing demand for exotic pets (private action - i.e., desire to own exotic pets or visit wildlife entertainment places) and fostering citizen engagement for system-wide change (civic action - e.g., signing a petition or participating in a protest against the exotic pet trade). Through an online survey with US participants (n = 2953), we identified 4 population segments (early adopters, early majority, late majority, and laggards), representing varying levels of commitment to wildlife conservation and then randomly assigned each segment to one of 3 messaging conditions. Messages highlighting negative consequences of the exotic pet trade and the power of collective action for system change effectively promoted private action among all segments except early adopters (ηp 2 = 0.005). Among civic actions, only the collective action message motivated early adopters and the early majority to sign petitions (φC = 0.193 and φC = 0.097, respectively). Furthermore, the 4 segments showed distinct reasoning for action and inaction on wildlife conservation, with certain relational values, such as care, serving as both motivations and barriers to action. These findings highlight the need for targeted behavioral interventions across diverse populations.

What's in a name? Political and economic concepts differ in social media references to harmful algae blooms

Policies and management decisions in the marine environment are driven in part by public sentiment which can grow more intense during hazard events like Harmful Algae Blooms (HABs). The public conversations on social media sites like Twitter (before X) reveal the polarized nature of HABs through nuanced language and sentiment. This article uses mixed methods of machine learned topic modeling and inductive qualitative coding to describe the ways the long-term 2017-2019 Karenia brevis "red tide" bloom were politicized across Florida's South West coast. It finds that there are topical differences in keywords related to place (e.g. beach, Florida, coast), agent (individual or organization), and epistemic values (reliance on scientific and/or media reports). These topical differences demonstrate different levels of politicization and partisanship in qualitative analysis. Conceptually, this research demonstrates the ways different dimensions of a long-duration marine hazard can be polarized. Regarding management, this research provides insights to political and organizational stakeholders and the gaps in the discourse shaping marine hazards which can be used to strategically guide future social media engagement to manage politicization. What if all the careful work that resource and environmental managers do can be undone by simple, seemingly uncontroversial words? In an era of increased environmental and marine distress-coupled with short format communication-the ways environmental managers choose their words is crucial, even between ostensibly inconsequential nouns like "red tide" or "algae bloom." Policies and management decisions in the marine environment are driven in part by public sentiment which can grow more intense during hazard events like Harmful Algae Blooms (HABs). The public conversations on social media sites like Twitter (before X) reveal the polarized nature of HABs through nuanced language and sentiment. This article relies on mining social media posts, and uses mixed methods of machine-learned topic modeling and human-driven inductive qualitative coding to describe the ways the long-term 2017-2019 Karenia brevis "red tide" blooms were politicized across Florida's South West coast. It finds that there are topical differences in keywords related to place (e.g. beach, Florida, coast), agent (individual or organization), and epistemic values (reliance on scientific and/or media reports). These topical differences demonstrate different levels of politicization and partisanship in qualitative analysis. Conceptually, this research demonstrates the ways different dimensions of a long-duration marine hazard can be polarized. Regarding management, this research provides insights to political and organizational stakeholders and the gaps in the discourse shaping marine hazards which can be used to strategically guide future social media engagement to manage politicization.

The distorting effects of producer strategies: Why engagement does not reveal consumer preferences for misinformation

A great deal of empirical research has examined who falls for misinformation and why. Here, we introduce a formal game-theoretic model of engagement with news stories that captures the strategic interplay between (mis)information consumers and producers. A key insight from the model is that observed patterns of engagement do not necessarily reflect the preferences of consumers. This is because producers seeking to promote misinformation can use strategies that lead moderately inattentive readers to engage more with false stories than true ones-even when readers prefer more accurate over less accurate information. We then empirically test people's preferences for accuracy in the news. In three studies, we find that people strongly prefer to click and share news they perceive as more accurate-both in a general population sample, and in a sample of users recruited through Twitter who had actually shared links to misinformation sites online. Despite this preference for accurate news-and consistent with the predictions of our model-we find markedly different engagement patterns for articles from misinformation versus mainstream news sites. Using 1,000 headlines from 20 misinformation and 20 mainstream news sites, we compare Facebook engagement data with 20,000 accuracy ratings collected in a survey experiment. Engagement with a headline is negatively correlated with perceived accuracy for misinformation sites, but positively correlated with perceived accuracy for mainstream sites. Taken together, these theoretical and empirical results suggest that consumer preferences cannot be straightforwardly inferred from empirical patterns of engagement.

Individuals, Collectives, and Individuals in Collectives: The Ineliminable Role of Dependence

Our beliefs are inextricably shaped through communication with others. Furthermore, even conversation we conduct in pairs may itself be taking place across a wider, connected social network. Our communications, and with that our thoughts, are consequently typically those of individuals in collectives. This has fundamental consequences with respect to how our beliefs are shaped. This article examines the role of dependence on our beliefs and seeks to demonstrate its importance with respect to key phenomena involving collectives that have been taken to indicate irrationality. It is argued that (with the benefit of hindsight) these phenomena no longer seem surprising when one considers the multiple dependencies that govern information acquisition and the evaluation of cognitive agents in their normal (i.e., social) context.

Knowledge through social networks: Accuracy, error, and polarisation

This paper examines the fundamental problem of testimony. Much of what we believe to know we know in good part, or even entirely, through the testimony of others. The problem with testimony is that we often have very little on which to base estimates of the accuracy of our sources. Simulations with otherwise optimal agents examine the impact of this for the accuracy of our beliefs about the world. It is demonstrated both where social networks of information dissemination help and where they hinder. Most importantly, it is shown that both social networks and a common strategy for gauging the accuracy of our sources give rise to polarisation even for entirely accuracy motivated agents. Crucially these two factors interact, amplifying one another's negative consequences, and this side effect of communication in a social network increases with network size. This suggests a new causal mechanism by which social media may have fostered the increase in polarisation currently observed in many parts of the world.

Attitudes and Behavior Feedback Loops for Young Women's Premarital Sex

Sociologists have long been puzzled by whether attitudes inform behaviors or vice versa. Accurately assessing both possibilities requires panel data collected at relatively short intervals. In this study, I leverage intensive panel data from the Relationship Dynamics and Social Life Study to assess the case of young women's premarital sexual attitudes and behavior. Through a series of descriptive analyses and cross-lagged panel models, I show that opposition to premarital sex in young adulthood is only sometimes associated with subsequent sexual behavior and that premarital sex is negatively associated with later opposition to premarital sex. Young women are especially likely to reduce their opposition following first sex relative to sex reported at any time. Thus, initial behavioral experiences may result in outsized shocks to attitudes, following an active updating model. That subsequent sex is associated with less attitudinal change suggests that young women initially update their attitudes before settling into them. This study nuances long-standing debates on the malleability of attitudes within a person over time and with respect to behavior and has implications for how people approach behavior according to their attitudes across a wide spectrum of social phenomena.

Resampling reduces bias amplification in experimental social networks

Large-scale social networks are thought to contribute to polarization by amplifying people's biases. However, the complexity of these technologies makes it difficult to identify the mechanisms responsible and evaluate mitigation strategies. Here we show under controlled laboratory conditions that transmission through social networks amplifies motivational biases on a simple artificial decision-making task. Participants in a large behavioural experiment showed increased rates of biased decision-making when part of a social network relative to asocial participants in 40 independently evolving populations. Drawing on ideas from Bayesian statistics, we identify a simple adjustment to content-selection algorithms that is predicted to mitigate bias amplification by generating samples of perspectives from within an individual's network that are more representative of the wider population. In two large experiments, this strategy was effective at reducing bias amplification while maintaining the benefits of information sharing. Simulations show that this algorithm can also be effective in more complex networks.

Toward a General Framework of Biased Reasoning: Coherence-Based Reasoning

A considerable amount of experimental research has been devoted to uncovering biased forms of reasoning. Notwithstanding the richness and overall empirical soundness of the bias research, the field can be described as disjointed, incomplete, and undertheorized. In this article, we seek to address this disconnect by offering "coherence-based reasoning" as a parsimonious theoretical framework that explains a sizable number of important deviations from normative forms of reasoning. Represented in connectionist networks and processed through constraint-satisfaction processing, coherence-based reasoning serves as a ubiquitous, essential, and overwhelmingly adaptive apparatus in people's mental toolbox. This adaptive process, however, can readily be overrun by bias when the network is dominated by nodes or links that are incorrect, overweighted, or otherwise nonnormative. We apply this framework to explain a variety of well-established biased forms of reasoning, including confirmation bias, the halo effect, stereotype spillovers, hindsight bias, motivated reasoning, emotion-driven reasoning, ideological reasoning, and more.

Social Psychological Perspectives on Political Polarization: Insights and Implications for Climate Change

Political polarization is a barrier to enacting policy solutions to global issues. Social psychology has a rich history of studying polarization, and there is an important opportunity to define and refine its contributions to the present political realities. We do so in the context of one of the most pressing modern issues: climate change. We synthesize the literature on political polarization and its applications to climate change, and we propose lines of further research and intervention design. We focus on polarization in the United States, examining other countries when literature was available. The polarization literature emphasizes two types of mechanisms of political polarization: (1) individual-level psychological processes related to political ideology and (2) group-level psychological processes related to partisan identification. Interventions that address group-level processes can be more effective than those that address individual-level processes. Accordingly, we emphasize the promise of interventions leveraging superordinate identities, correcting misperceived norms, and having trusted leaders communicate about climate change. Behavioral interventions like these that are grounded in scientific research are one of our most promising tools to achieve the behavioral wedge that we need to address climate change and to make progress on other policy issues.

Reducing political polarization in the United States with a mobile chat platform

Do anonymous online conversations between people with different political views exacerbate or mitigate partisan polarization? We created a mobile chat platform to study the impact of such discussions. Our study recruited Republicans and Democrats in the United States to complete a survey about their political views. We later randomized them into treatment conditions where they were offered financial incentives to use our platform to discuss a contentious policy issue with an opposing partisan. We found that people who engage in anonymous cross-party conversations about political topics exhibit substantial decreases in polarization compared with a placebo group that wrote an essay using the same conversation prompts. Moreover, these depolarizing effects were correlated with the civility of dialogue between study participants. Our findings demonstrate the potential for well-designed social media platforms to mitigate political polarization and underscore the need for a flexible platform for scientific research on social media.

Experimental evidence for structured information-sharing networks reducing medical errors

Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information-sharing networks among clinicians to improve clinicians' diagnostic accuracy and treatment decisions. We use a pool of 2,941 practicing clinicians recruited from around the United States to conduct 84 independent group-level trials, ranging across seven different clinical vignettes for topics known to exhibit high rates of diagnostic or treatment error (e.g., acute cardiac events, geriatric care, low back pain, and diabetes-related cardiovascular illness prevention). We compare collective performance in structured information-sharing networks to collective performance in independent control groups, and find that networks significantly reduce clinical errors, and improve treatment recommendations, as compared to control groups of independent clinicians engaged in isolated reflection. Our results show that these improvements are not a result of simple regression to the group mean. Instead, we find that within structured information-sharing networks, the worst clinicians improved significantly while the best clinicians did not decrease in quality. These findings offer implications for the use of social network technologies to reduce errors among clinicians.

Does Considering the Consequences of Selective Exposure Help Reduce Selective Exposure Behaviors?

Selective exposure (the tendency to avoid information one disagrees with) is particularly easy to do and leads to problematic outcomes. This study investigated if personally relevant message frames would increase participant engagement and agreement when reading a counterattitudinal message. Participants (N = 180) were randomly assigned into one of three message frames: the idea attack frame asked participants to recall a time their ideas were attacked or summarily dismissed; the unable to defend position frame asked participants to recall a time they were vulnerable due to a lack of knowledge; and an irrelevant-frame control. Participants then read a counterattitudinal message about increasing internet service taxes. Next, participants rated their message agreement, and self-perceived engagement level. Although the messages did not influence agreement, the unable to defend position and the idea attack frames influenced participants to be more engaged with the message. This suggests that people are motivated to engage more with a counterattitudinal message when they are reminded of a time in which they were vulnerable due to a lack of knowledge or when others were summarily dismissive of their own ideas. Overall, both frames showed some promise and should be explored further.

Partisans' receptivity to persuasive messaging is undiminished by countervailing party leader cues

It is widely assumed that party identification and loyalty can distort partisans' information processing, diminishing their receptivity to counter-partisan arguments and evidence. Here we empirically evaluate this assumption. We test whether American partisans' receptivity to arguments and evidence is diminished by countervailing cues from in-party leaders (Donald Trump or Joe Biden), using a survey experiment with 24 contemporary policy issues and 48 persuasive messages containing arguments and evidence (N = 4,531; 22,499 observations). We find that, while in-party leader cues influenced partisans' attitudes, often more strongly than the persuasive messages, there was no evidence that the cues meaningfully diminished partisans' receptivity to the messages-despite them directly contradicting the messages. Rather, persuasive messages and countervailing leader cues were integrated as independent pieces of information. These results generalized across policy issues, demographic subgroups and cue environments, and challenge existing assumptions about the extent to which party identification and loyalty distort partisans' information processing.

Realtime user ratings as a strategy for combatting misinformation: an experimental study

Because fact-checking takes time, verdicts are usually reached after a message has gone viral and interventions can have only limited effect. A new approach recently proposed in scholarship and piloted on online platforms is to harness the wisdom of the crowd by enabling recipients of an online message to attach veracity assessments to it. The intention is to allow poor initial crowd reception to temper belief in and further spread of misinformation. We study this approach by letting 4000 subjects in 80 experimental bipartisan communities sequentially rate the veracity of informational messages. We find that in well-mixed communities, the public display of earlier veracity ratings indeed enhances the correct classification of true and false messages by subsequent users. However, crowd intelligence backfires when false information is sequentially rated in ideologically segregated communities. This happens because early raters' ideological bias, which is aligned with a message, influences later raters' assessments away from the truth. These results suggest that network segregation poses an important problem for community misinformation detection systems that must be accounted for in the design of such systems.

Network segregation and the propagation of misinformation

How does the ideological segregation of online networks impact the spread of misinformation? Past studies have found that homophily generally increases diffusion, suggesting that partisan news, whether true or false, will spread farther in ideologically segregated networks. We argue that network segregation disproportionately aids messages that are otherwise too implausible to diffuse, thus favoring false over true news. To test this argument, we seeded true and false informational messages in experimental networks in which subjects were either ideologically integrated or segregated, yielding 512 controlled propagation histories in 16 independent information systems. Experimental results reveal that the fraction of false information circulating was systematically greater in ideologically segregated networks. Agent-based models show robustness of this finding across different network topologies and sizes. We conclude that partisan sorting undermines the veracity of information circulating on the Internet by increasing exposure to content that would otherwise not manage to diffuse.

What the Neuroscience and Psychology of Magic Reveal about Misinformation

When we believe misinformation, we have succumbed to an illusion: our perception or interpretation of the world does not match reality. We often trust misinformation for reasons that are unrelated to an objective, critical interpretation of the available data: Key facts go unnoticed or unreported. Overwhelming information prevents the formulation of alternative explanations. Statements become more believable every time they are repeated. Events are reframed or given "spin" to mislead audiences. In magic shows, illusionists apply similar techniques to convince spectators that false and even seemingly impossible events have happened. Yet, many magicians are "honest liars," asking audiences to suspend their disbelief only during the performance, for the sole purpose of entertainment. Magic misdirection has been studied in the lab for over a century. Psychological research has sought to understand magic from a scientific perspective and to apply the tools of magic to the understanding of cognitive and perceptual processes. More recently, neuroscientific investigations have also explored the relationship between magic illusions and their underlying brain mechanisms. We propose that the insights gained from such studies can be applied to understanding the prevalence and success of misinformation. Here, we review some of the common factors in how people experience magic during a performance and are subject to misinformation in their daily lives. Considering these factors will be important in reducing misinformation and encouraging critical thinking in society.

An Online experiment during the 2020 US-Iran crisis shows that exposure to common enemies can increase political polarization

A longstanding theory indicates that the threat of a common enemy can mitigate conflict between members of rival groups. We tested this hypothesis in a pre-registered experiment where 1670 Republicans and Democrats in the United States were asked to complete an online social learning task with a bot that was labeled as a member of the opposing party. Prior to this task, we exposed respondents to primes about (a) a common enemy (involving Iran and Russia); (b) a patriotic event; or (c) a neutral, apolitical prime. Though we observed no significant differences in the behavior of Democrats as a result of priming, we found that Republicans-and particularly those with very strong conservative views-were significantly less likely to learn from Democrats when primed about a common enemy. Because our study was in the field during the 2020 Iran Crisis, we were able to further evaluate this finding via a natural experiment-Republicans who participated in our study after the crisis were even less influenced by the beliefs of Democrats than those Republicans who participated before this event. These findings indicate common enemies may not reduce inter-group conflict in highly polarized societies, and contribute to a growing number of studies that find evidence of asymmetric political polarization in the United States. We conclude by discussing the implications of these findings for research in social psychology, political conflict, and the rapidly expanding field of computational social science.

The network science of collective intelligence

In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts.

A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams

Algorithmic agents, popularly known as bots, have been accused of spreading misinformation online and supporting fringe views. Collectives are vulnerable to hidden-profile environments, where task-relevant information is unevenly distributed across individuals. To do well in this task, information aggregation must equally weigh minority and majority views against simple but inefficient majority-based decisions. In an experimental design, human volunteers working in teams of 10 were asked to solve a hidden-profile prediction task. We trained a variational auto-encoder (VAE) to learn people’s hidden information distribution by observing how people’s judgments correlated over time. A bot was designed to sample responses from the VAE latent embedding to selectively support opinions proportionally to their under-representation in the team. We show that the presence of a single bot (representing 10% of team members) can significantly increase the polarization between minority and majority opinions by making minority opinions less prone to social influence. Although the effects on hybrid team performance were small, the bot presence significantly influenced opinion dynamics and individual accuracy. These findings show that self-supervized machine learning techniques can be used to design algorithms that can sway opinion dynamics and group outcomes.

Negacionismo climático e desinformação online: uma revisão de escopo

Esforços de síntese de evidências vêm apontando para o avanço das formas organizadas de desinformação e negação do conhecimento científico sobre a mudança climática global. Em vários países do mundo, há um forte debate sobre a difusão dessas narrativas no ambiente online e seus impactos políticos, sociais e econômicos. Neste trabalho, realizamos uma revisão de escopo aplicada às bases Web of Science e Scopus, a fim de mapear como a literatura acadêmica internacional vem descrevendo as relações entre o negacionismo da ciência sobre mudanças climáticas e o uso de campanhas de desinformação no século XXI, assim como as possíveis lacunas e apontamentos desses estudos para a agenda de pesquisas. Em todos os tipos de mídias estudados nos 31 artigos selecionados, foi identificada uma predominância de discursos contrários ao consenso científico sobre o tema, alavancada por campanhas de desinformação organizadas, inclusive, por atores governamentais. Observamos um crescimento significativo do campo nos últimos anos, assim como transformações estratégicas nas comunicações negacionistas tendendo a uma disputa maior da opinião pública sobre a ciência

Political coherence and certainty as drivers of interpersonal liking over and above similarity

Affective polarization and political segregation have become a serious threat to democratic societies. One standard explanation for these phenomena is that people like and prefer interacting with similar others. However, similarity may not be the only driver of interpersonal liking in the political domain, and other factors, yet to be uncovered, could play an important role. Here, we hypothesized that beyond the effect of similarity, people show greater preference for individuals with politically coherent and confident opinions. To test this idea, we performed two behavioral studies consisting of one-shot face-to-face pairwise interactions. We found that people with ambiguous or ambivalent views were nonreciprocally attracted to confident and coherent ingroups. A third experimental study confirmed that politically coherent and confident profiles are rated as more attractive than targets with ambiguous or ambivalent opinions. Overall, these findings unfold the key drivers of the affability between people who discuss politics.

Investigating Predictors of Public- and Private-Sphere Sustainable Behaviors in the Context of Agritourism

Encouraging sustainable behaviors regarding food choices among the public is crucial to ensure food systems’ sustainability. We expand the understanding of sustainable behavioral change by assessing engagement in local food systems (LFSs) in the context of agritourism experiences. Using theory of planned behavior (TPB) and personal norms, we conducted pre–post-surveys at agritourism farms to measure the impact of changes in the TPB behavioral antecedents as predictors of the following behavioral intentions regarding LFS engagement: (1) purchasing local food (private-sphere behavior), (2) increasing monthly budget to purchase local food (private-sphere behavior) and (3) advocating for local food (public-sphere behavior). Our findings indicate that strategies to encourage LFS engagement should seek to activate moral considerations that can motivate action across private and public behaviors, which applies to various demographic groups. To stimulate collective action, strategies should target subjective norms specifically (e.g., encouraging social interaction around local food), while strategies encouraging private behaviors should focus on easing perceived barriers to buying local food (e.g., promoting local food outlets). As agritourism experiences effectively modify the three above-mentioned behavioral antecedents, we advocate for holistic experiences that provide opportunities for deeper engagement with local food, stimulate the senses, and facilitate social interaction around LFSs.

Interindividual cooperation mediated by partisanship complicates Madison's cure for "mischiefs of faction"

Political theorists have long argued that enlarging the political sphere to include a greater diversity of interests would cure the ills of factions in a pluralistic society. While the scope of politics has expanded dramatically over the past 75 y, polarization is markedly worse. Motivated by this paradox, we take a bottom-up approach to explore how partisan individual-level dynamics in a diverse (multidimensional) issue space can shape collective-level factionalization via an emergent dimensionality reduction. We extend a model of cultural evolution grounded in evolutionary game theory, in which individuals accumulate benefits through pairwise interactions and imitate (or learn) the strategies of successful others. The degree of partisanship determines the likelihood of learning from individuals of the opposite party. This approach captures the coupling between individual behavior, partisan-mediated opinion dynamics, and an interaction network that changes endogenously according to the evolving interests of individuals. We find that while expanding the diversity of interests can indeed improve both individual and collective outcomes, increasingly high partisan bias promotes a reduction in issue dimensionality via party-based assortment that leads to increasing polarization. When party bias becomes extreme, it also boosts interindividual cooperation, thereby further entrenching extreme polarization and creating a tug-of-war between individual cooperation and societal cohesion. These dangers of extreme partisanship are highest when individuals' interests and opinions are heavily shaped by peers and there is little independent exploration. Overall, our findings highlight the urgency to study polarization in a coupled, multilevel context.

Segregation and clustering of preferences erode socially beneficial coordination

Polarization on various issues has increased in many Western democracies over the last decades, leading to divergent beliefs, preferences, and behaviors within societies. We develop a model to investigate the effects of polarization on the likelihood that a society will coordinate on a welfare-improving action in a context in which collective benefits are acquired only if enough individuals take that action. We examine the impacts of different manifestations of polarization: heterogeneity of preferences, segregation of the social network, and the interaction between the two. In this context, heterogeneity captures differential perceived benefits from coordinating, which can lead to different intentions and sensitivity regarding the intentions of others. Segregation of the social network can create a bottleneck in information flows about others' preferences, as individuals may base their decisions only on their close neighbors. Additionally, heterogeneous preferences can be evenly distributed in the population or clustered in the local network, respectively reflecting or systematically departing from the views of the broader society. The model predicts that heterogeneity of preferences alone is innocuous and it can even be beneficial, while segregation can hamper coordination, mainly when local networks distort the distribution of valuations. We base these results on a multimethod approach including an online group experiment with 750 individuals. We randomize the range of valuations associated with different choice options and the information respondents have about others. The experimental results reinforce the idea that, even in a situation in which all could stand to gain from coordination, polarization can impede social progress.

Polarized information ecosystems can reorganize social networks via information cascades

The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively "unimportant" story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others' political identities. Observational follow network data collected on Twitter support this prediction: We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be "unimportant" news at the expense of missing out on subjectively "important" news far more frequently. This suggests that "echo chambers"-to the extent that they exist-may not echo so much as silence.

Co-created Future Scenarios as a Tool to Communicate Sustainable Development in Coastal Communities in Palawan, Philippines

Scenarios can be used to communicate potential future changes and engage and connect different audiences in exploring sustainable solutions. Communicating scenarios using creative visualisation, co-creation and a focus on local contexts are especially promising. This research is conducted on the island of Palawan in the Philippines as part of the GCRF Blue Communities project. With a quasi-experimental design, we investigate the psychological and emotional effects of the engagement with future scenarios as a tool for communicating sustainability. Together with local stakeholders and community members, three distinct, locally relevant scenario narratives (Business as Usual, Best Case, and Worst Case) have been co-created. Subsequently, a sample of N = 109 local high school students was asked to creatively engage with these scenario narratives. Intentions to engage in sustainable behaviour, perceived behavioural control, ascription of responsibility, consideration of future consequences, six basic emotions and connectedness to place were assessed before and after the activity via paper-pencil administrated questionnaires. A mixed-model analysis showed significant increases in intentions to engage in sustainable behaviour, however, this increase disappeared when consideration of future consequences was added as a covariate, suggesting a mediating effect. The level of consideration of future consequences also increased significantly after engaging with any of the future scenarios, which questions the common interpretation of consideration of future consequences as a trait variable. Perceived behavioural control significantly increased following the engagement with each of the scenarios whereas ascription of responsibility and connectedness to place did not show any changes. Overall, the two most emotion-evoking scenarios, Best Case Scenario and Worst Case Scenario, turn out as superior over the Business as Usual Scenario, which points to the relevance of emotional framing for effective messaging in our sample. This is the first systematic, quantitative assessment of the effects of future scenarios as a communication tool.

The reduction of race and gender bias in clinical treatment recommendations using clinician peer networks in an experimental setting

Bias in clinical practice, in particular in relation to race and gender, is a persistent cause of healthcare disparities. We investigated the potential of a peer-network approach to reduce bias in medical treatment decisions within an experimental setting. We created "egalitarian" information exchange networks among practicing clinicians who provided recommendations for the clinical management of patient scenarios, presented via standardized patient videos of actors portraying patients with cardiac chest pain. The videos, which were standardized for relevant clinical factors, presented either a white male actor or Black female actor of similar age, wearing the same attire and in the same clinical setting, portraying a patient with clinically significant chest pain symptoms. We found significant disparities in the treatment recommendations given to the white male patient-actor and Black female patient-actor, which when translated into real clinical scenarios would result in the Black female patient being significantly more likely to receive unsafe undertreatment, rather than the guideline-recommended treatment. In the experimental control group, clinicians who were asked to independently reflect on the standardized patient videos did not show any significant reduction in bias. However, clinicians who exchanged real-time information in structured peer networks significantly improved their clinical accuracy and showed no bias in their final recommendations. The findings indicate that clinician network interventions might be used in healthcare settings to reduce significant disparities in patient treatment.

Challenging the Idea That Humans Are Not Designed to Solve Climate Change

In the face of a slow and inadequate global response to anthropogenic climate change, scholars and journalists frequently claim that human psychology is not designed or evolved to solve the problem, and they highlight a range of "psychological barriers" to climate action. Here, we critically examine this claim and the evidence on which it is based. We identify four key problems with attributing climate inaction to "human nature" or evolved psychological barriers: (a) It minimizes variability within and between populations; (b) it oversimplifies psychological research and its implications for policy; (c) it frames responsibility for climate change in terms of the individual at the expense of the role of other aspects of culture, including institutional actors; and (d) it rationalizes inaction. For these reasons, the message from social scientists must be clear-humans' current collective failure to tackle climate change on the scale required cannot be explained as a product of a universal and fixed human nature because it is a fundamentally cultural phenomenon, reflecting culturally evolved values, norms, institutions, and technologies that can and must change rapidly.

Scaling up fact-checking using the wisdom of crowds

Professional fact-checking, a prominent approach to combating misinformation, does not scale easily. Furthermore, some distrust fact-checkers because of alleged liberal bias. We explore a solution to these problems: using politically balanced groups of laypeople to identify misinformation at scale. Examining 207 news articles flagged for fact-checking by Facebook algorithms, we compare accuracy ratings of three professional fact-checkers who researched each article to those of 1128 Americans from Amazon Mechanical Turk who rated each article’s headline and lede. The average ratings of small, politically balanced crowds of laypeople (i) correlate with the average fact-checker ratings as well as the fact-checkers’ ratings correlate with each other and (ii) predict whether the majority of fact-checkers rated a headline as “true” with high accuracy. Furthermore, cognitive reflection, political knowledge, and Democratic Party preference are positively related to agreement with fact-checkers, and identifying each headline’s publisher leads to a small increase in agreement with fact-checkers.

Humans rely more on algorithms than social influence as a task becomes more difficult

Algorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

Probabilistic social learning improves the public's judgments of news veracity

The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluated the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and maintained polarization. The benefits of probabilistic social learning are robust to participants' education, gender, race, income, religion, and partisanship.

Strategies for integrating disparate social information

Social information use is widespread in the animal kingdom, helping individuals rapidly acquire useful knowledge and adjust to novel circumstances. In humans, the highly interconnected world provides ample opportunities to benefit from social information but also requires navigating complex social environments with people holding disparate or conflicting views. It is, however, still largely unclear how people integrate information from multiple social sources that (dis)agree with them, and among each other. We address this issue in three steps. First, we present a judgement task in which participants could adjust their judgements after observing the judgements of three peers. We experimentally varied the distribution of this social information, systematically manipulating its variance (extent of agreement among peers) and its skewness (peer judgements clustering either near or far from the participant's judgement). As expected, higher variance among peers reduced their impact on behaviour. Importantly, observing a single peer confirming a participant's own judgement markedly decreased the influence of other-more distant-peers. Second, we develop a framework for modelling the cognitive processes underlying the integration of disparate social information, combining Bayesian updating with simple heuristics. Our model accurately accounts for observed adjustment strategies and reveals that people particularly heed social information that confirms personal judgements. Moreover, the model exposes strong inter-individual differences in strategy use. Third, using simulations, we explore the possible implications of the observed strategies for belief updating. These simulations show how confirmation-based weighting can hamper the influence of disparate social information, exacerbate filter bubble effects and deepen group polarization. Overall, our results clarify what aspects of the social environment are, and are not, conducive to changing people's minds.

Social connections with COVID-19-affected areas increase compliance with mobility restrictions

We study the role of social connections in compliance of U.S. households with mobility restrictions imposed in response to the coronavirus disease 2019 (COVID-19) pandemic, using aggregated and anonymized Facebook data on social connections and mobile phone data for measuring social distancing at the county level. Relative to the average restriction efficacy, a county with one-SD more social connections with China and Italy-the first countries with major COVID-19 outbreaks-has a nearly 50% higher compliance with mobility restrictions. By contrast, social connections of counties with less-educated populations, a higher Trump vote share, and a higher fraction of climate change deniers show decreased compliance with mobility restrictions. Our analysis suggests that social connections are conduits of information about the pandemic and an economically important factor affecting compliance with, and impact of, mobility restrictions.

Behavioural frameworks to understand public perceptions of and risk response to carbon dioxide removal

The adoption of carbon dioxide removal (CDR) technologies at a scale sufficient to draw down carbon emissions will require both individual and collective decisions that happen over time in different locations to enable a massive scale-up. Members of the public and other decision-makers have not yet formed strong attitudes, beliefs and preferences about most of the individual CDR technologies or taken positions on policy mechanisms and tax-payer support for CDR. Much of the current discourse among scientists, policy analysts and policy-makers about CDR implicitly assumes that decision-makers will exhibit unbiased, rational behaviour that weighs the costs and benefits of CDR. In this paper, we review behavioural decision theory and discuss how public reactions to CDR will be different from and more complex than that implied by rational choice theory. Given that people do not form attitudes and opinions in a vacuum, we outline how fundamental social normative principles shape important intergroup, intragroup and social network processes that influence support for or opposition to CDR technologies. We also point to key insights that may help stakeholders craft public outreach strategies that anticipate the nuances of how people evaluate the risks and benefits of CDR approaches. Finally, we outline critical research questions to understand the behavioural components of CDR to plan for an emerging public response.

Defeating Merchants of Doubt: Subjective certainty and self-affirmation ameliorate attitude polarization via partisan motivated reasoning

Informed by uncertainty-identity theory, this study tested the polarizing effect of partisan-led politicization of science and ways to combat it. Using a national sample of South Koreans (N = 840), our online experiment found that when partisan elites, as opposed to scientists (or civic activists), spearheaded politicization, attitude polarization emerged via partisan motivated reasoning. Such polarizing effects of party cues did not persist when subjective certainty and self-affirmation enhanced the level of certainty partisans felt about their surroundings and themselves. These patterns proved consistent across multiple scientific issues, including climate change, genetically modified foods, and algae blooms. The implications of the findings are discussed in light of how to attenuate the polarizing effect of partisan-led politicization through the lens of social identity approaches. Given that this study provides one of the first pieces of evidence on the topic outside the Western context, the advantages of using a South Korean sample are noted.

The effects of recursive communication dynamics on belief updating

Many social interactions are characterized by dynamic interplay, such that individuals exert reciprocal influence over each other's behaviours and beliefs. The present study investigated how the dynamics of reciprocal influence affect individual beliefs in a social context, over and above the information communicated in an interaction. To this end, we developed a simple social decision-making paradigm in which two people are asked to make perceptual judgments while receiving information about each other's decisions. In a Static condition, information about the partner only conveyed their initial, independent judgment. However, in a Dynamic condition, each individual saw the evolving belief of their partner as they learnt about and responded to the individual's own judgment. The results indicated that in both conditions, the majority of confidence adjustments were characterized by an abrupt change followed by smaller adjustments around an equilibrium, and that participants' confidence was used to arbitrate conflict (although deviating from Bayesian norm). Crucially, recursive interaction had systematic effects on belief change relative to the static baseline, magnifying confidence change when partners agreed and reducing confidence change when they disagreed. These findings indicate that during dynamic interactions-often a characteristic of real-life and online social contexts-information is collectively transformed rather than acted upon by individuals in isolation. Consequently, the output of social events is not only influenced by what the dyad knows but also by predictable recursive and self-reinforcing dynamics.

Computational Social Science and Sociology

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; (f) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

Networked collective intelligence improves dissemination of scientific information regarding smoking risks

Despite substantial investments in public health campaigns, misunderstanding of health-related scientific information is pervasive. This is especially true in the case of tobacco use, where smokers have been found to systematically misperceive scientific information about the negative health effects of smoking, in some cases leading smokers to increase their pro-smoking bias. Here, we extend recent work on 'networked collective intelligence' by testing the hypothesis that allowing smokers and nonsmokers to collaboratively evaluate anti-smoking advertisements in online social networks can improve their ability to accurately assess the negative health effects of tobacco use. Using Amazon's Mechanical Turk, we conducted an online experiment where smokers and nonsmokers (N = 1600) were exposed to anti-smoking advertisements and asked to estimate the negative health effects of tobacco use, either on their own or in the presence of peer influence in a social network. Contrary to popular predictions, we find that both smokers and nonsmokers were surprisingly inaccurate at interpreting anti-smoking messages, and their errors persisted if they continued to interpret these messages on their own. However, smokers and nonsmokers significantly improved in their ability to accurately interpret anti-smoking messages by sharing their opinions in structured online social networks. Specifically, subjects in social networks reduced the error of their risk estimates by over 10 times more than subjects who revised solely based on individual reflection (p < 0.001, 10 experimental trials in total). These results suggest that social media networks may be used to activate social learning that improves the public's ability to accurately interpret vital public health information.

Perception of mandatory infant vaccines and trust in vaccination among first-year healthcare students: An opportunity window for the training of future healthcare workers

INTRODUCTION

A high level of vaccine hesitancy is observed in France; to maintain a high level of vaccine coverage, a policy of mandatory vaccines has been applied since January 2019. As vaccine hesitancy has been described among healthcare workers, we aimed to assess the adhesion to vaccination in students enrolled in the first common year of healthcare studies (PACES), and their perceptions regarding the mandatory vaccine policy.

METHODS

We elaborated an anonymous online questionnaire and asked the 35 French universities offering PACES studies to diffuse it to their students; 26 accepted.

RESULTS

Overall, 4326 participants completed the questionnaire (age 18.7 ± 1.5 years, female 76.1%). They evaluated vaccine usefulness and harmfulness at a median of 90 [IQT:80-100] and 25 [IQT:10-50], respectively; on the same scale, their median trust in vaccines was 82 [IQT:70-94]. Among the five main age classes (17 to 21 years), the positive perceptions of vaccination declined with age. A majority declared that making vaccines mandatory was morally and scientifically justified (65.7% and 84.7%, respectively); in response to the questionnaire showing a blatant anti-mandatory vaccine billboard, 52.3% and 28.6%, respectively, strongly disagreed or rather disagreed with it. Again, these proportions declined between 17 and 21 years. Participants were asked whether the mandatory vaccine policy would improve their perceptions of vaccination; those with poorer perceptions were less likely to answer this question positively.

CONCLUSION

The perceptions of vaccination and the recent mandatory vaccine policy are positive in first-year students but better in younger ones, thus suggesting that vaccination should be taught early, during this study year. The mandatory vaccine policy may not improve the perceptions of those with a high level of vaccine hesitancy.

Information gerrymandering and undemocratic decisions

People must integrate disparate sources of information when making decisions, especially in social contexts. But information does not always flow freely. It can be constrained by social networks^1-3 and distorted by zealots and automated bots⁴. Here we develop a voter game as a model system to study information flow in collective decisions. Players are assigned to competing groups (parties) and placed on an 'influence network' that determines whose voting intentions each player can observe. Players are incentivized to vote according to partisan interest, but also to coordinate their vote with the entire group. Our mathematical analysis uncovers a phenomenon that we call information gerrymandering: the structure of the influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes. We confirm the predicted effects of information gerrymandering in social network experiments with n = 2,520 human subjects. Furthermore, we identify extensive information gerrymandering in real-world influence networks, including online political discussions leading up to the US federal elections, and in historical patterns of bill co-sponsorship in the US Congress and European legislatures. Our analysis provides an account of the vulnerabilities of collective decision-making to systematic distortion by restricted information flow. Our analysis also highlights a group-level social dilemma: information gerrymandering can enable one party to sway decisions in its favour, but when multiple parties engage in gerrymandering the group loses its ability to reach consensus and remains trapped in deadlock.

Discrepancy in scientific authority and media visibility of climate change scientists and contrarians

We juxtapose 386 prominent contrarians with 386 expert scientists by tracking their digital footprints across ∼200,000 research publications and ∼100,000 English-language digital and print media articles on climate change. Projecting these individuals across the same backdrop facilitates quantifying disparities in media visibility and scientific authority, and identifying organization patterns within their association networks. Here we show via direct comparison that contrarians are featured in 49% more media articles than scientists. Yet when comparing visibility in mainstream media sources only, we observe just a 1% excess visibility, which objectively demonstrates the crowding out of professional mainstream sources by the proliferation of new media sources, many of which contribute to the production and consumption of climate change disinformation at scale. These results demonstrate why climate scientists should increasingly exert their authority in scientific and public discourse, and why professional journalists and editors should adjust the disproportionate attention given to contrarians.

The role of climate change (CC) contrarians is neglected in climate change communication studies. Here the authors used a data-driven approach to identify CC contrarians and CC scientists and found that CC scientists have much higher citation impact than those for contrarians but lower media visibility.

The wisdom of partisan crowds

Theories in favor of deliberative democracy are based on the premise that social information processing can improve group beliefs. While research on the "wisdom of crowds" has found that information exchange can increase belief accuracy on noncontroversial factual matters, theories of political polarization imply that groups will become more extreme-and less accurate-when beliefs are motivated by partisan political bias. A primary concern is that partisan biases are associated not only with more extreme beliefs, but also with a diminished response to social information. While bipartisan networks containing both Democrats and Republicans are expected to promote accurate belief formation, politically homogeneous networks are expected to amplify partisan bias and reduce belief accuracy. To test whether the wisdom of crowds is robust to partisan bias, we conducted two web-based experiments in which individuals answered factual questions known to elicit partisan bias before and after observing the estimates of peers in a politically homogeneous social network. In contrast to polarization theories, we found that social information exchange in homogeneous networks not only increased accuracy but also reduced polarization. Our results help generalize collective intelligence research to political domains.

Directionality of information flow and echoes without chambers

How do echo chambers operate? Why does social propagation of information become trapped within the boundaries of social groups? Previous studies of these questions have identified informational and structural factors which hinder information exchange across group boundaries; these factors constitute “chambers” in which information flows are confined and transformed into “echoes.” However, empirical evidence has indicated that these factors may not sufficiently explain the mechanism of echo chambers. Hence, the present study investigated whether the insular flow of information emerges and endures without the chambers. A randomized controlled experiment was conducted in which participants, who were classified into two political groups, exchanged randomly selected articles with the same number of ingroup and outgroup neighbors. The experiment manipulated the directionality of incoming information flow by varying the number of articles sent from ingroup neighbors across two conditions. Analyses revealed that the ingroup-slanted inflow induced ingroup-slanted outflow, suppressing transmission toward neighbors in a different social group. The biased inflow also promoted positive reactions to information exchanges and reduced negative evaluations on the exchanged information. Furthermore, the ingroup-slanted inflow increased false perceptions of ingroup majority, which is known to encourage information dissemination by a social group. The present study suggests two self-reinforcing mechanisms of ingroup-biased flows that generate echoes even without the chambers. These mechanisms may enable a small group of strategic actors to exacerbate polarization within a large population by manipulating directions of information flow.

Bridge ties bind collective memories

From families to nations, what binds individuals in social groups is, to a large degree, their shared beliefs, norms, and memories. These emergent outcomes are thought to occur because communication among individuals results in community-wide synchronization. Here, we use experimental manipulations in lab-created networks to investigate how the temporal dynamics of conversations shape the formation of collective memories. We show that when individuals that bridge between clusters (i.e., bridge ties) communicate early on in a series of networked interactions, the network reaches higher mnemonic convergence compared to when individuals first interact within clusters (i.e., cluster ties). This effect, we show, is due to the tradeoffs between initial information diversity and accumulated overlap over time. Our approach provides a framework to analyze and design interventions in social networks that optimize information sharing and diminish the likelihood of information bubbles and polarization.

Social groups form collective memories, but the temporal dynamics of this process are unclear. Here, the authors show that when early conversations involve individuals that bridge across clusters of a social network, the network reaches higher mnemonic convergence compared to when early conversations occur within clusters.

Hipsters on networks: How a minority group of individuals can lead to an antiestablishment majority

The spread of opinions, memes, diseases, and “alternative facts” in a population depends both on the details of the spreading process and on the structure of the social and communication networks on which they spread. One feature that can change spreading dynamics substantially is heterogeneous behavior among different types of individuals in a social network. In this paper, we explore how antiestablishment nodes (e.g., hipsters) influence the spreading dynamics of two competing products. We consider a model in which spreading follows a deterministic rule for updating node states (which indicate which product has been adopted) in which an adjustable probability pHip of the nodes in a network are hipsters, who choose to adopt the product that they believe is the less popular of the two. The remaining nodes are conformists, who choose which product to adopt by considering which products their immediate neighbors have adopted. We simulate our model on both synthetic and real networks, and we show that the hipsters have a major effect on the final fraction of people who adopt each product: even when only one of the two products exists at the beginning of the simulations, a small fraction of hipsters in a network can still cause the other product to eventually become the more popular one. To account for this behavior, we construct an approximation for the steady-state adoption fractions of the products on k-regular trees in the limit of few hipsters. Additionally, our simulations demonstrate that a time delay τ in the knowledge of the product distribution in a population, as compared to immediate knowledge of product adoption among nearest neighbors, can have a large effect on the final distribution of product adoptions. Using a local-tree approximation, we derive an analytical estimate of the spreading of products and obtain good agreement if a sufficiently small fraction of the population consists of hipsters. In all networks, we find that either of the two products can become the more popular one at steady state, depending on the fraction of hipsters in the network and on the amount of delay in the knowledge of the product distribution. Our simple model and analysis may help shed light on the road to success for antiestablishment choices in elections, as such success—and qualitative differences in final outcomes between competing products, political candidates, and so on—can arise rather generically in our model from a small number of antiestablishment individuals and ordinary processes of social influence on normal individuals.