ArticlePLoS computational biology2024
Moderate confirmation bias enhances decision-making in groups of reinforcement-learning agents.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Deterministic dynamics of distributional multi-agent reinforcement learning.PLoS computational biology · 2026Article
- Modeling surgeon belief updating under bias: a Bayesian simulation in shoulder arthroplasty.JSES international · 2026Article
- How social learning enhances-or undermines-efficiency and flexibility in collective decision-making under uncertainty.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Apparent learning biases emerge from optimal inference: Insights from master equation analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evolving choice hysteresis in reinforcement learning: Comparing the adaptive value of positivity bias and gradual perseveration.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Perceived challenges and enablers to evaluating a whole systems approach initiative: Reflections of embedded researchers.PloS one · 2025Article
- Human reinforcement learning processes and biases: computational characterization and possible applications to behavioral public policy.Mind & society · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Humans tend to give more weight to information confirming their beliefs than to information that disconfirms them. Nevertheless, this apparent irrationality has been shown to improve individual decision-making under uncertainty. However, little is known about this bias' impact on decision-making in a social context. Here, we investigate the conditions under which confirmation bias is beneficial or detrimental to decision-making under social influence. To do so, we develop a Collective Asymmetric Reinforcement Learning (CARL) model in which artificial agents observe others' actions and rewards, and update this information asymmetrically. We use agent-based simulations to study how confirmation bias affects collective performance on a two-armed bandit task, and how resource scarcity, group size and bias strength modulate this effect. We find that a confirmation bias benefits group learning across a wide range of resource-scarcity conditions. Moreover, we discover that, past a critical bias strength, resource abundance favors the emergence of two different performance regimes, one of which is suboptimal. In addition, we find that this regime bifurcation comes with polarization in small groups of agents. Overall, our results suggest the existence of an optimal, moderate level of confirmation bias for decision-making in a social context.
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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.