Recursive Joint Simulation in Games
CoRR(2024)
摘要
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.
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