AI Plays? {\delta}-Rationality Games with Nash Equilibrium as Special Case

TL;DR

This paper introduces a new framework for analyzing games using a distortion function that differentiates between players' actual and true payoffs, providing insights into behavior prediction and welfare analysis.

Contribution

It proposes a novel approach that employs actual payoffs for behavior modeling and true payoffs for welfare evaluation, extending traditional game theory.

Findings

Distortion function effectively captures payoff gaps.

Framework distinguishes between behavior prediction and welfare analysis.

Nash equilibrium is a special case within this framework.

Abstract

A distortion function, which captures the payoff gap between a player's actual payoff and her true payoff, is introduced and used to analyze games. In our proposed framework, we argue that players' actual payoff functions should be used to explain and predict their behaviors, while their true payoff functions should be used to conduct welfare analysis of the outcomes.