Information gerrymandering and undemocratic decisions

Stewart, Alexander J.; Mosleh, Mohsen; Diakonova, Marina; Arechar, Antonio A.; Rand, David G.; Plotkin, Joshua B.

doi:10.1038/s41586-019-1507-6

Cited by 154 publications

(167 citation statements)

References 26 publications

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“…The way in which individuals integrate personal and social information shapes a wide range of collective phenomena, including the spread of knowledge across social networks (1), the development of financial markets (2), political mobilization (3) and voting outcomes (4). By interacting with others and observing their behavior, individuals can often glean useful information helping them to, for example, rapidly adjust to new environments (5).…”

Section: Introductionmentioning

confidence: 99%

Strategies for integrating disparate social information

Molleman¹,

Tump²,

Gradassi³

et al. 2020

Preprint

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Our increasingly interconnected world provides virtually unlimited opportunities to observe the behavior of others. This affords abundant useful 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 judgment task in which participants could adjust their judgments after observing the judgments 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 judgments clustering either near or far from the participant’s). As expected, higher variance among peers reduced their impact on behavior. Importantly, observing a single peer confirming an individual’s judgment 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 judgments. Moreover, the model exposes strong inter-individual differences in strategy use. Third, using simulations, we explore the possible implications of identified strategies for belief updating more broadly. They show how confirmation effects can hamper the influence of disparate social information, exacerbate filter bubble effects and worsen group polarization. Overall, our results clarify what aspects of the social environment are, and are not, conducive to changing people’s minds.

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Section: Introductionmentioning

confidence: 99%

Strategies for integrating disparate social information

Molleman¹,

Tump²,

Gradassi³

et al. 2020

Preprint

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“…From an applied perspective, our results highlight an often overlooked avenue by which social media fosters the spread of misinformation. Rather than (or in addition to) the phenomenon of echo chambers and filter-bubbles 62,63 , social media platforms may actually discourage people from reflecting on accuracy 64 . These platforms are designed to encourage users to rapidly scroll and spontaneously engage with streams of content that mix serious news media with emotionally engaging content where accuracy is not a relevant feature (e.g., photos of babies, videos of cats knocking objects off tables for no good reason).…”

mentioning

confidence: 99%

Shifting attention to accuracy can reduce misinformation online

Pennycook¹,

Epstein²,

Mosleh³

et al. 2019

Preprint

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152

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The spread of false and misleading news content on social media is of great societal concern. Why do people share such content, and what can be done about it? In a first survey experiment (N=1,015), we demonstrate a disconnect between accuracy judgments and sharing intentions: even though true headlines are rated as much more accurate than false headlines, headline veracity has little impact on sharing. Although this may seem to indicate that people share inaccurate content because they care more about furthering their political agenda than they care about truth, we propose an alternative attentional account: Most people do not want to spread misinformation, but the social media context focuses their attention on factors other than truth and accuracy. Indeed, when directly asked, most participants say it is important to only share news that is accurate. Accordingly, across four survey experiments (total N=3,485) and a digital field experiment on Twitter in which we messaged users who had previously shared news from websites known for publishing misleading content (N=5,379), we find that inducing people to think about the concept of accuracy increases the quality of the news they subsequently share. Together, these results challenge the narrative that people no longer care about accuracy. Instead, the results support our inattention-based account wherein people fail to implement their preference for accuracy due to attentional constraints. Furthermore, our research provides evidence for scalable anti-misinformation interventions that are easily implementable by social media platforms.

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“…Much like colonies of the clonal raider ant, these systems exhibit patterns that can be interpreted as behavioral convergence (60–64), divergence (65), and non-linear effects of mixing on group-level phenotypes (66–68). In turn, these patterns affect important processes such as collective decision-making (5), the transmission and evolution of disease (69, 70), and the evolution of cooperative behavior (71, 72). While different variants of threshold-based models have been employed to study several of these systems (73–76), we still lack a unified theoretical framework to understand the consequences of individual differences on collective dynamics (77).…”

Section: Discussionmentioning

confidence: 99%

“…The study of collective behavior and self-organization is an active area of research across a diversity of fields, from animal movement (1) to robotics (2), from tissue engineering (3) to public health (4), and from voting (5) to conservation (6). The colonies of social insects in particular are striking examples of highly integrated, complex biological systems that can self-regulate without centralized control (7).…”

Section: Introductionmentioning

confidence: 99%

Emergent behavioral organization in heterogeneous groups of a social insect

Ulrich

Kawakatsu²,

Tokita

et al. 2020

Preprint

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The composition of social groups has profound effects on their function, from collective decision-making to foraging efficiency. But few social systems afford sufficient control over group composition to precisely quantify its effects on individual and collective behavior. Here we combine experimental and theoretical approaches to study the effect of group composition on individual behavior and division of labor (DOL) in a social insect. Experimentally, we use automated behavioral tracking to monitor 120 colonies of the clonal raider ant, Ooceraea biroi, with controlled variation in three key correlates of social insect behavior: genotype, age, and morphology. We find that each of these sources of heterogeneity generates a distinct pattern of behavioral organization, including the amplification or dampening of inherent behavioral differences in colonies with mixed types. Theoretically, we use a well-studied model of DOL to explore potential mechanisms underlying the experimental findings. We find that the simplest implementation of this model, which assumes that heterogeneous individuals differ only in response thresholds, could only partially recapitulate the empirically observed patterns of behavior. However, the full spectrum of observed phenomena was recapitulated by extending the model to incorporate two factors that are biologically meaningful but theoretically rarely considered: variation among workers in task performance efficiency and among larvae in task demand. Our results thus show that different sources of heterogeneity within social groups can generate different, sometimes non-intuitive, behavioral effects, but that relatively simple models can capture these dynamics and thereby begin to elucidate the basic organizational principles of DOL in social insects.Significance StatementWhen individuals interact in an aggregate, many factors that are not known a priori affect group dynamics. A social group will therefore show emergent properties that cannot easily be predicted from how its members behave in isolation. This problem is exacerbated in mixed groups, where different individuals have different behavioral tendencies. Here we describe different facets of collective behavioral organization in mixed groups of the clonal raider ant, and show that a simple theoretical model can capture even non-intuitive aspects of the behavioral data. These results begin to reveal the principles underlying emergent behavioral organization in social insects. Importantly, our insights might apply to complex biological systems more generally and be used to help engineer collective behavior in artificial systems.

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Information gerrymandering and undemocratic decisions

Cited by 154 publications

References 26 publications

Strategies for integrating disparate social information

Strategies for integrating disparate social information

Shifting attention to accuracy can reduce misinformation online

Emergent behavioral organization in heterogeneous groups of a social insect

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