Wise or mad crowds? The cognitive mechanisms underlying information cascades

Tump, Alan Novaes; Pleskac, Timothy J.; Kurvers, Ralf H. J. M.

doi:10.1126/sciadv.abb0266

“…Our third experiment considers how social information shapes belief formation in a context where social values or preferences are irrelevant [31]. In such settings, social information can help individuals acquire useful knowledge [32] and make accurate decisions [33,34], but may also misinform [35], promote beliefs in conspiracy theories [36] or fuel group polarization [37].…”

Section: Prosocialitymentioning

confidence: 99%

Social influence in adolescence as a double-edged sword

Molleman¹,

Ciranka²,

Bos³

2021

Preprint

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Social learning is fundamental to human development, helping individuals adapt to new conditions and cooperate in groups. During the formative years of adolescence, the social environment shapes people’s socio-cognitive skills needed in adulthood. Yet, peer influence during this pivotal developmental stage is generally associated with risky and unruly conduct, with eminent negative long-term effects on adolescents’ educational, economic and health outcomes. Here we show, in contrast to this traditional view, that the impact of peers on adolescents’ behaviour can also be markedly positive. Exposure to disobedient peers provoked rule breaking, and selfish peers reduced prosocial behaviour, particularly in early adolescence. However, compliant peers also promoted rule-following and fair peers increased prosociality. A belief formation task further revealed that early adolescents tend to assimilate social information, while older adolescents prioritise personal views. Our results suggest that these developmental patterns reflect a decline in an underlying domain-general factor of social sensitivity during adolescence, and highlight early adolescence as a key window for peer-based interventions to improve developmental trajectories.

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“…It ensures that individuals with strong personal information will, on average, start closer to one of the decision boundaries and thus make faster decisions, as observed in groups across many biological systems (Couzin et al, 2005;Kurvers et al, 2015;Reebs, 2000;Stroeymeyt et al, 2011). These fast decisions can, in turn, influence the drift rate of undecided individuals, thus capturing the natural dynamic of information flow from highly informed to lowly informed individuals (Tump et al, 2020).…”

Section: Model and Resultsmentioning

confidence: 99%

“…and is modeled proportionally to the majority size M (t) of individuals that had already decided at time point t (e.g., see Bikhchandani et al, 1998;Tump et al, 2020):…”

Section: Model and Resultsmentioning

confidence: 99%

“…The studies listed above assumed that all group members announce their personal decision independently, and that individuals can use the group's average opinion as social information for revising their initial independent choice. Both assumptions are unrealistic for decision-making processes in almost all biological systems (e.g., see Sankey et al, 2021), where individuals decide sequentially, with more knowledgeable (or confident) individuals making faster decisions (Couzin et al, 2005;Kurvers et al, 2015;Stroeymeyt et al, 2011;Tump et al, 2020;Watts et al, 2016). Early decisions can influence later-deciding individuals, and, in extreme cases, early-deciding individuals can trigger information cascades in which all individuals imitate these early decisions (Anderson and Holt, 1997;Banerjee, 1992;Bikhchandani et al, 1998;Gallup et al, 2012).…”

mentioning

confidence: 99%

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Avoiding costly mistakes in groups: The evolution of error management in collective decision making

Tump¹,

Wolf²,

Romanczuk³

et al. 2021

Preprint

Self Cite

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Balancing the costs of alternative decisions is a fundamental challenge for decision makers. This is especially critical in social situations, where the choices individuals face are often associated with highly asymmetric error costs---such as pedestrian groups crossing the street, police squads holding a suspect at gunpoint, or animal groups evading predation. While a broad literature has explored how individuals acting alone adapt to asymmetric error costs, little is known about how individuals in groups cope with these costs. Here we investigate adaptive decision strategies of individuals in groups facing asymmetric error costs, modeling scenarios where individuals aim to maximize group-level payoff (‘‘cooperative groups’’) or individual-level payoff (‘‘competitive groups’’). We extended the drift--diffusion model to the social domain in which individuals first gather personal information independently; they can then either wait for additional social information or decide early, thereby potentially influencing others. We combined this social drift--diffusion model with an evolutionary algorithm to derive adaptive behavior. Under asymmetric costs, small cooperative groups evolved response biases to avoid the costly error. Large cooperative groups, however, did not evolve response biases, since the danger of response biases triggering false information cascades increases with group size. We show that individuals in competitive groups face a social dilemma: They evolve higher response biases and wait for more information, thereby undermining group performance. Our results have broad implications for understanding social dynamics in situations with asymmetric costs, such as crowd panics and predator detection.

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“…Information cascade has been observed to play a significant role in sequential voting mechanisms such as presidential primaries and roll-call voting (Knight and Schiff 2010), wherein information about the previously cast votes are revealed to the voter and accordingly the voter finalizes his/her choice of the candidate that could be even different from their personal preference/initial choice. In a recent work (Tump et al 2020), the authors modeled the dynamics of social decision-making process leading to information cascade and observed the drift rate from the personal choice to the majority choice exhibited a convex increase for smaller majority size and a concave increase for medium and larger majority sizes. The impact of the decision made by the initial adopters on the final outcome of a sequential unanimous decision-making information cascade phenomenon was also experimentally studied in Anderson and Holt (1997) involving several test subjects.…”

Section: Related Workmentioning

confidence: 99%

Exploring the step function distribution of the threshold fraction of adopted neighbors versus minimum fraction of nodes as initial adopters to assess the cascade blocking intra-cluster density of complex real-world networks

Meghanathan

¹

2020

Appl Netw Sci

0

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We first propose a binary search algorithm to determine the minimum fraction of nodes in a network to be used as initial adopters ($$f_{IA}^{\min }$$ f IA min ) for a particular threshold fraction (q) of adopted neighbors (related to the cascade capacity of the network) leading to a complete information cascade. We observe the q versus $$f_{IA}^{\min }$$ f IA min distribution for several complex real-world networks to exhibit a step function pattern wherein there is an abrupt increase in $$f_{IA}^{\min }$$ f IA min beyond a certain value of q (qstep); the $$f_{IA}^{\min }$$ f IA min values at qstep and the next measurable value of q are represented as $$\underline{{f_{IA}^{\min } }}$$ f IA min ̲ and $$\overline{{f_{IA}^{\min } }}$$ f IA min ¯ respectively. The difference $$\overline{{f_{IA}^{\min } }} - \underline{{f_{IA}^{\min } }}$$ f IA min ¯ - f IA min ̲ is observed to be significantly high (a median of 0.44 for a suite of 40 real-world networks studied in this paper) such that we claim the 1 − qstep value (we propose to refer 1 − qstep as the Cascade Blocking Index, CBI) for a network could be perceived as a measure of the intra-cluster density of the blocking cluster of the network that cannot be penetrated without including an appreciable number of nodes from the cluster to the set of initial adopters (justifying a relatively larger $$\overline{{f_{IA}^{\min } }}$$ f IA min ¯ value).

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Wise or mad crowds? The cognitive mechanisms underlying information cascades

Cited by 75 publications

References 63 publications

Social influence in adolescence as a double-edged sword

Social influence in adolescence as a double-edged sword

Avoiding costly mistakes in groups: The evolution of error management in collective decision making

Exploring the step function distribution of the threshold fraction of adopted neighbors versus minimum fraction of nodes as initial adopters to assess the cascade blocking intra-cluster density of complex real-world networks

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