Whether getting vaccinated, buying stocks, or crossing streets, people rarely make decisions alone. Rather, multiple people decide sequentially, setting the stage for information cascades whereby early-deciding individuals can influence others’ choices. To understand how information cascades through social systems, it is essential to capture the dynamics of the decision-making process. We introduce the social drift–diffusion model to capture these dynamics. We tested our model using a sequential choice task. The model was able to recover the dynamics of the social decision-making process, accurately capturing how individuals integrate personal and social information dynamically over time and when their decisions were timed. Our results show the importance of the interrelationships between accuracy, confidence, and response time in shaping the quality of information cascades. The model reveals the importance of capturing the dynamics of decision processes to understand how information cascades in social systems, paving the way for applications in other social systems.

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.

1. Responding to the information provided by others is an important foraging strategy in many species. Through social foraging, individuals can more efficiently find unpredictable resources and thereby increase their foraging success.2. When individuals are more socially responsive to particular phenotypes than others, however, the advantage they obtain from foraging socially is likely to depend on the phenotype composition of the social environment. We tested this hypothesis by performing experimental manipulations of guppy, Poecilia reticulata, sex compositions in the wild.3. Males found fewer novel food patches in the absence of females than in mixedsex compositions, while female patch discovery did not differ regardless of the presence or absence of males. 4. We argue that these results were driven by sex-dependent mechanisms of social association: Markov chain-based fission-fusion modelling revealed that less social individuals found fewer patches and that males reduced sociality when females were absent. In contrast, females were similarly social with or without males. Our findings highlight the relevance of considering how individual-and popula-tion-level traits interact in shaping the advantages of social foraging in the wild. K E Y W O R D Sfission-fusion, foraging ecology, guppy, Markov chain analysis, Poecilia reticulata, sex ratio, social facilitation, social learning | INTRODUC TI ON

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.