Aggregate Efficiency in Games

TL;DR

This paper demonstrates that in large population games, decentralized information aggregation often corrects individual biases, but economic structures like networks and platforms can create inefficiencies by exploiting these biases, suggesting policy focus on information regulation.

Contribution

It introduces a new aggregate efficiency benchmark in large games and analyzes how economic forces can lead to systemic inefficiencies.

Findings

Decentralized aggregation corrects individual biases in large games.

Economic structures can exploit biases, causing inefficiencies.

Policy should focus on regulating information structures, not just individual behavior.

Abstract

We show that, in large population games, decentralized information aggregation generically corrects for individual-level biases. This establishes a new testable aggregate efficiency benchmark where the behavior of boundedly rational agents mimics that of fully rational agents. However, we find that structural economic forces such as strategic network formation and profit-maximizing platforms can systematically select pathological environments to exploit individuals' biases, thereby causing aggregate inefficiencies. We characterize these inefficiencies in monopoly and labor markets. Our findings therefore suggest that policy should shift focus from correcting individuals' behavior to monitoring and regulating information structures.