Computing rational decisions in extensive games with limited foresight
This addresses the challenge of modeling realistic strategic interactions in game theory for scenarios where players cannot fully anticipate consequences, offering an incremental improvement over existing limited foresight models.
The paper tackles the problem of decision-making in extensive games where players have limited foresight, introducing a model with higher-order reasoning and a novel solution concept to achieve more profitable outcomes, and devises an effective procedure to compute the resulting equilibria.
We introduce a class of extensive form games where players might not be able to foresee the possible consequences of their decisions and form a model of their opponents which they exploit to achieve a more profitable outcome. We improve upon existing models of games with limited foresight, endowing players with the ability of higher-order reasoning and proposing a novel solution concept to address intuitions coming from real game play. We analyse the resulting equilibria, devising an effective procedure to compute them.