Game-to-Real Gap: Quantifying the Effect of Model Misspecification in Network Games

TL;DR

This paper introduces the game-to-real gap, a metric quantifying how model misspecification affects outcomes in multi-agent network games, revealing that standard measures often fail to capture these effects.

Contribution

The paper defines the game-to-real gap, analyzes its causes in quadratic network games, and develops new centrality measures to evaluate the impact of model misspecification.

Findings

Misspecifications can cause arbitrarily large gaps in outcomes.

Standard centrality measures fail to capture the effects of misspecification.

Numerical experiments show counterintuitive impacts of model misspecification.

Abstract

Game-theoretic models and solution concepts provide rigorous tools for predicting collective behavior in multi-agent systems. In practice, however, different agents may rely on different game-theoretic models to design their strategies. As a result, when these heterogeneous models interact, the realized outcome can deviate substantially from the outcome each agent expects based on its own local model. In this work, we introduce the game-to-real gap, a new metric that quantifies the impact of such model misspecification in multi-agent environments. The game-to-real gap is defined as the difference between the utility an agent actually obtains in the multi-agent environment (where other agents may have misspecified models) and the utility it expects under its own game model. Focusing on quadratic network games, we show that misspecifications in either (i) the external shock or (ii) the player interaction network can lead to arbitrarily large game-to-real gaps. We further develop novel network centrality measures that allow exact evaluation of this gap in quadratic network games. Our analysis reveals that standard network centrality measures fail to capture the effects of model misspecification, underscoring the need for new structural metrics that account for this limitation. Finally, through illustrative numerical experiments, we show that existing centrality measures in network games may provide a counterintuitive understanding of the impact of model misspecification.