ArticleNature human behaviour2025
Playing repeated games with large language models.
Article in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Disentangling interaction and bias effects in opinion dynamics of large language models.Nature communications · 2026Article
- Persona-prompted LLM agents achieve modest but genuine prediction of human social media reactions.Scientific reports · 2026Article
- The Internalized Ableism Inventory: Scale development using a hybrid artificial intelligence and community-based participatory research design.Rehabilitation psychology · 2026Article
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- Effects of personality steering on cooperative behavior in large language model agents.Scientific reports · 2026Article
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- Artificial intelligence chatbots mimic human collective behaviour.British journal of psychology (London, England : 1953) · 2026Article
- From simultaneous to leader-follower play in direct reciprocity.PNAS nexus · 2026Article
- Multi-agent game theory for precision psychiatry.Frontiers in psychiatry · 2026Article
- From LLM narratives to parameterized cooperation policies in multi-agent systems.Frontiers in artificial intelligence · 2026Article
- Decision-Making in Repeated Games: Insights from Active Inference.Behavioral sciences (Basel, Switzerland) · 2025Review
- Collaborative intelligence in AI: Evaluating the performance of a council of AIs on the USMLE.PLOS digital health · 2025Article
- Evaluating the ability of large Language models to predict human social decisions.Scientific reports · 2025Article
- Article
- A publicly available benchmark for assessing large language models' ability to predict how humans balance self-interest and the interest of others.Scientific reports · 2025Article
- Comparing AI and human decision-making mechanisms in daily collaborative experiments.iScience · 2025Article
- Emergent social conventions and collective bias in LLM populations.Science advances · 2025Article
- DruGagent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction.ArXiv · 2025Article
- Static network structure cannot stabilize cooperation among large language model agents.PloS one · 2025Article
- Using large language models to facilitate academic work in the psychological sciences.Current psychology (New Brunswick, N.J.) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Large language models (LLMs) are increasingly used in applications where they interact with humans and other agents. We propose to use behavioural game theory to study LLMs' cooperation and coordination behaviour. Here we let different LLMs play finitely repeated 2 × 2 games with each other, with human-like strategies, and actual human players. Our results show that LLMs perform particularly well at self-interested games such as the iterated Prisoner's Dilemma family. However, they behave suboptimally in games that require coordination, such as the Battle of the Sexes. We verify that these behavioural signatures are stable across robustness checks. We also show how GPT-4's behaviour can be modulated by providing additional information about its opponent and by using a 'social chain-of-thought' strategy. This also leads to better scores and more successful coordination when interacting with human players. These results enrich our understanding of LLMs' social behaviour and pave the way for a behavioural game theory for machines.
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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.