Game-theoretic dynamics between AI agents could differ from traditional human-human interactions in various ways. One such difference is that it may be possible to accurately simulate an AI agent, for example because its source code is known. Our aim is to explore ways of leveraging this possibility to achieve more cooperative outcomes in strategic settings. In this paper, we study an interaction between AI agents where the agents run a recursive joint simulation. That is, the agents first jointly observe a simulation of the situation they face. This simulation in turn recursively includes additional simulations (with a small chance of failure, to avoid infinite recursion), and the results of all these nested simulations are observed before an action is chosen. We show that the resulting interaction is strategically equivalent to an infinitely repeated version of the original game, allowing a direct transfer of existing results such as the various folk theorems.

翻译：人工智能体之间的博弈动力学可能以多种方式不同于传统的人类-人类交互。其中一种差异在于，由于AI智能体的源代码已知，或许能够精确模拟其行为。本研究旨在探索如何利用这一可能性在策略性场景中实现更优的合作结果。本文研究了AI智能体间运行递归联合模拟的交互过程：智能体首先联合观察其面临情境的模拟结果，该模拟又递归地包含更多子模拟（以较小失败概率避免无限递归），最终在观察所有嵌套模拟结果后做出行动选择。研究表明，这种交互在策略上等价于原始游戏的无限重复版本，从而可以直接迁移现有结论，例如各类民间定理。