Complexity and Misspecification

TL;DR

This paper introduces a unified framework to analyze how concerns about model misspecification and complexity influence decision-making, revealing that simplicity preferences can improve safety and welfare.

Contribution

It develops a tractable model linking model misspecification concerns and complexity aversion, explaining various empirical phenomena and suggesting that simplicity enhances decision safety.

Findings

Pathological cycles from misspecification concerns can be eliminated by penalizing complexity.

Preferences for simplicity tend to promote safety and long-term welfare.

The framework explains phenomena like scale heterogeneity, probability neglect, and home bias.

Abstract

We propose a tractable unified framework to study the evolution and interaction of model-misspecification concerns and complexity aversion in repeated decision problems. This aims to capture environments where decision makers worry that their models are misspecified while also disliking overly complex models. We find that pathological cycles caused by endogenous concerns for misspecification can be eliminated by penalizing complex models and show that such preferences for simplicity tend to favor safety, which can enhance welfare in the long run. We use our framework to provide new microfoundations for pervasive empirical phenomena such as "scale heterogeneity" in discrete-choice analysis, "probability neglect" in behavioral economics, and "home bias" in international finance.