Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory
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Abstract (arXiv)
It is increasingly possible for real-world agents, such as software-based agents or human institutions, to view the internal programming of other such agents that they interact with. For instance, a company can read the bylaws of another company, or one software system can read the source code of another. Game-theoretic equilibria between the designers of such agents are called program equilibria, and we call this area open-source game theory. In this work we demonstrate a series of counterintuitive results on open-source games, which are independent of the programming language in which agents are written. We show that certain formal institution designs that one might expect to defect against each other will instead turn out to cooperate, or conversely, cooperate when one might expect them to defect. The results hold in a setting where each institution has full visibility into the other institution's true operating procedures. We also exhibit examples and ten open problems for better understanding these phenomena. We argue that contemporary game theory remains ill-equipped to study program equilibria, given that even the outcomes of single games in open-source settings remain counterintuitive and poorly understood. Nonetheless, some of these open-source agents exhibit desirable characteristics -- e.g., they can unexploitably create incentives for cooperation and legibility from other agents -- such that analyzing them could yield considerable benefits.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Aug 2022.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 3; 2026: 4.
11 papers.
- 2023Cooperative AI via Decentralized Commitment Devicescited by 7
- 2025Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Valuecited by 6
- 2025Evaluating LLMs in Open-Source Gamescited by 5
- 2022Similarity-based cooperative equilibriumcited by 4
- 2024Recursive Joint Simulation in Gamescited by 4
- 2026Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AIcited by 3
- 2022Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agentscited by 1
- 2025Policy-Conditioned Policies for Multi-Agent Task Solvingcited by 0
- 2026Causal Foundations of Collective Agencycited by 0
- 2026Parametric Open Source Gamescited by 0
- 2026Math for AI safety: an invitation for mathematicianscited by 0
- 2026Math for AI safety: an invitation for mathematicians14 Sep 2026
- 2026Parametric Open Source Games25 Jun 2026
- 2026Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI8 May 2026
- 2026Causal Foundations of Collective Agency30 Apr 2026
- 2025Policy-Conditioned Policies for Multi-Agent Task Solving24 Dec 2025
- 2025Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value3 Dec 2025
- 2025Evaluating LLMs in Open-Source Games29 Nov 2025
- 2024Recursive Joint Simulation in Games12 Feb 2024
- 2023Cooperative AI via Decentralized Commitment Devices14 Nov 2023
- 2022Similarity-based cooperative equilibrium26 Nov 2022
- 2022Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents5 Sep 2022