Game-theoretic interactions with AI agents could differ from traditional human-human interactions in various ways. One such difference is that it may be possible to simulate an AI agent (for example because its source code is known), which allows others to accurately predict the agent's actions. This could lower the bar for trust and cooperation. In this paper, we formalize games in which one player can simulate another at a cost. We first derive some basic properties of such games and then prove a number of results for them, including: (1) introducing simulation into generic-payoff normal-form games makes them easier to solve; (2) if the only obstacle to cooperation is a lack of trust in the possibly-simulated agent, simulation enables equilibria that improve the outcome for both agents; and however (3) there are settings where introducing simulation results in strictly worse outcomes for both players.

翻译：与人工智能智能体的博弈论交互可能以多种方式区别于传统人类间交互。其中一个差异在于，模拟AI智能体（例如因其源代码已知）可能是可行的，这使得其他智能体能够准确预测该智能体的行为。这或许会降低信任与合作的门槛。本文中，我们形式化了一类博弈，其中一方玩家可以以一定代价模拟另一方。我们首先推导了此类博弈的基本属性，随后证明了一系列相关结论，包括：（1）将模拟引入通用收益标准型博弈会降低求解难度；（2）若合作唯一障碍在于对可能被模拟智能体缺乏信任，则模拟能够实现改善双方收益的均衡；（3）然而存在某些设定，引入模拟反而会导致双方玩家收益严格恶化。