  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.
