ArticleSIAM journal on applied dynamical systems2020
Bayesian Evidence Accumulation on Social Networks.
Article in SIAM journal on applied dynamical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Individual differences in speed-accuracy trade-off influence social decision-making in dyads.Proceedings. Biological sciences · 2025Article
- Fast decisions reflect biases; slow decisions do not.Physical review. E · 2024Article
- A Cognitive Computational Approach to Social and Collective Decision-Making.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- Earlier social information has a stronger influence on judgments.Scientific reports · 2024Article
- Article
- Impact of correlated information on pioneering decisions.Physical review research · 2023Article
- Asynchrony rescues statistically optimal group decisions from information cascades through emergent leaders.Royal Society open science · 2023Article
- Avoiding costly mistakes in groups: The evolution of error management in collective decision making.PLoS computational biology · 2022Article
- Heterogeneity Improves Speed and Accuracy in Social Networks.Physical review letters · 2020Article
- Stochastic dynamics of social patch foraging decisions.Physical review researchArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
To make decisions we are guided by the evidence we collect and the opinions of friends and neighbors. How do we combine our private beliefs with information we obtain from our social network? To understand the strategies humans use to do so, it is useful to compare them to observers that optimally integrate all evidence. Here we derive network models of rational (Bayes optimal) agents who accumulate private measurements and observe the decisions of their neighbors to make an irreversible choice between two options. The resulting information exchange dynamics has interesting properties: When decision thresholds are asymmetric, the absence of a decision can be increasingly informative over time. In a recurrent network of two agents, the absence of a decision can lead to a sequence of belief updates akin to those in the literature on common knowledge. On the other hand, in larger networks a single decision can trigger a cascade of agreements and disagreements that depend on the private information agents have gathered. Our approach provides a bridge between social decision making models in the economics literature, which largely ignore the temporal dynamics of decisions, and the single-observer evidence accumulator models used widely in neuroscience and psychology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.