Uncommon Belief in Rationality

TL;DR

This paper introduces a graph-based language for modeling complex higher-order beliefs about rationality among agents, along with a solution concept and an algorithm for simplifying belief structures.

Contribution

It presents a novel graph-based framework for representing higher-order beliefs and a method to compress these structures into a minimal form.

Findings

Developed a new solution concept for reasoning with complex belief structures

Created an efficient algorithm for belief structure compression

Enhanced understanding of rationality assumptions in multi-agent interactions

Abstract

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.