Uncommon Belief in Rationality
This work addresses the need for more nuanced models of agent interactions in game theory or AI, though it appears incremental by extending traditional assumptions.
The paper tackles the problem of analyzing interactions among agents by proposing a graph-based language to capture complex higher-order beliefs about rationality, resulting in a solution concept for reasoning and an efficient algorithm for compressing belief structures into a minimal form.
Common knowledge/belief in rationality is the traditional standard assumption in analysing interaction among agents. This paper proposes a graph-based language for capturing significantly more complicated structures of higher-order beliefs that agents might have about the rationality of the other agents. The two main contributions are a solution concept that captures the reasoning process based on a given belief structure and an efficient algorithm for compressing any belief structure into a unique minimal form.