Inferring Strategies from Observations in Long Iterated Prisoner's Dilemma Experiments
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Abstract (arXiv)
While many theoretical studies have revealed the strategies that could lead to and maintain cooperation in the Iterated Prisoner's Dilemma, less is known about what human participants actually do in this game and how strategies change when being confronted with anonymous partners in each round. Previous attempts used short experiments, made different assumptions of possible strategies, and led to very different conclusions. We present here two long treatments that differ in the partner matching strategy used, i.e. fixed or shuffled partners. Here we use unsupervised methods to cluster the players based on their actions and then Hidden Markov Model to infer what are those strategies in each cluster. Analysis of the inferred strategies reveals that fixed partner interaction leads to a behavioral self-organization. Shuffled partners generate subgroups of strategies that remain entangled, apparently blocking the self-selection process that leads to fully cooperating participants in the fixed partner treatment. Analyzing the latter in more detail shows that AllC, AllD, TFT- and WSLS-like behavior can be observed. This study also reveals that long treatments are needed as experiments less than 25 rounds capture mostly the learning phase participants go through in these kinds of experiments.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2022.
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Citing papers per year: 2025: 4; 2026: 2.
6 papers.
- 2025FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theorycited by 13
- 2025Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamicscited by 11
- 2025Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-makingcited by 8
- 2025Strategic Communication and Language Bias in Multi-Agent LLM Coordinationcited by 3
- 2026Payoff scaling shapes cooperation in LLM agents across languagescited by 1
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experimentcited by 0
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment28 Jul 2026
- 2026Payoff scaling shapes cooperation in LLM agents across languages27 Jan 2026
- 2025Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics8 Dec 2025
- 2025Strategic Communication and Language Bias in Multi-Agent LLM Coordination30 Jul 2025
- 2025Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making11 Jun 2025
- 2025FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory19 Apr 2025