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Atlas / Sociology of AI / Method

Iterated social dilemmas

Repeated Prisoner’s Dilemma, public-goods, donor and common-pool resource games that measure cooperation, reciprocity and punishment.

Used in

  • Vallinder Hughes 2024 - Cultural evolution of cooperation
  • Horibe Itao Toyokawa 2026 - Reputation-based cooperation
  • Piatti et al 2024 - Cooperate or collapse
  • Akata et al 2025 - Playing repeated games with LLMs
  • Li Shirado 2025 - Spontaneous giving and calculated greed
  • Willis et al 2026 - Collective behaviour of hundreds of agents
  • Chang et al 2026 - Misalignment contagion
  • Kundu et al 2026 - Similarity signals induce cooperation
  • Long Teplica 2025 - The AI in the mirror
  • Ferraz de Arruda et al 2026 - Cooperation without individual fidelity

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Around this method
Playing repeated games with large language modelsMitigating Misalignment Contagion by Steering with Implicit TraitsCollective cooperation without individual fidelity in LLM agentsEmergence of Reputation-Based Cooperation in LLM AgentsDo LLMs Take Care of Their Own? Similarity Signals Can Induce CooperationSpontaneous Giving and Calculated Greed in Language ModelsThe AI in the Mirror: LLM Self-Recognition in an Iterated Public Goods GameCooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM AgentsCultural Evolution of Cooperation among LLM AgentsThis method

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Linked from

  • Playing repeated games with large language models
  • Mitigating Misalignment Contagion by Steering with Implicit Traits
  • Collective cooperation without individual fidelity in LLM agents
  • Emergence of Reputation-Based Cooperation in LLM Agents
  • Do LLMs Take Care of Their Own? Similarity Signals Can Induce Cooperation
  • Spontaneous Giving and Calculated Greed in Language Models
  • The AI in the Mirror: LLM Self-Recognition in an Iterated Public Goods Game
  • Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
  • Cultural Evolution of Cooperation among LLM Agents
  • Evaluating Collective Behaviour of Hundreds of LLM Agents
  • Can single-agent evaluations predict how agents behave in groups?
  • Do AI agent populations build cumulative culture, or only conventions?
  • Do AI agents treat other AIs as their in-group?
  • Does an agent's individual psychology predict what its group will do?
  • Do agent groups need institutions rather than instructions to stay honest?
  • Do high stakes and impossible goals push agent groups to break norms?
  • What spreads between AI agents, and through which channels?
  • Which self does an agent identify with, and how does that shape how it treats other agents?

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