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This research investigates how large language models adjudicate interactions in the Open-Strategy Dictator Game, where agents' decision-making logic is explicitly visible as natural-language documents to all participants. The study demonstrates that conditionally cooperative strategies—those designed to share with cooperators and exploit non-cooperators—are evolutionarily robust when an LLM interprets these transparent procedures within a multi-agent environment. These findings offer significant insights into the dynamics of trust, transparency, and equilibrium stability in AI-driven multiagent systems where decision protocols are open for inspection.
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The Open-Strategy Dictator Game: Cooperation Under Mutual Transparency

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arXiv:2608.14913v1 Announce Type: cross Abstract: We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each player's strategy is a natural-language document visible to all participants. The dictator's decision, to SHARE or TAKE an endowment, may depend on the text of the recipient's strategy. A large language model adjudicates each interaction by interpreting the dictator's strategy in the context of the recipient's. We run round-robin tournaments among diverse strategies and analyze the resulting payoff matrix using softmax equilibrium frequencies, dominance analysis, and sensitivity to the relative value of cooperation. Conditionally cooperative strategies, those that share with cooperators and take from exploiters, consistently dominate, while unconditional strategies (always share or always take) are weakly dominated. The results suggest that in environments where agents can inspect each other's decision procedures, conditional cooperation is evolutionarily robust across a wide range of payoff parameters.

Abstract:We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each player's strategy is a natural-language document visible to all participants. The dictator's decision, to SHARE or TAKE an endowment, may depend on the text of the recipient's strategy. A large language model adjudicates each interaction by interpreting the dictator's strategy in the context of the recipient's. We run round-robin tournaments among diverse strategies and analyze the resulting payoff matrix using softmax equilibrium frequencies, dominance analysis, and sensitivity to the relative value of cooperation. Conditionally cooperative strategies, those that share with cooperators and take from exploiters, consistently dominate, while unconditional strategies (always share or always take) are weakly dominated. The results suggest that in environments where agents can inspect each other's decision procedures, conditional cooperation is evolutionarily robust across a wide range of payoff parameters.

Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2608.14913 [cs.GT]

(or arXiv:2608.14913v1 [cs.GT] for this version)

https://doi.org/10.48550/arXiv.2608.14913 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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