Robust decision-making under risk and ambiguity

TL;DR

This paper introduces a framework based on statistical decision theory for making robust decisions under model ambiguity and estimation uncertainty, demonstrated through a dynamic investment model.

Contribution

It develops a novel approach to incorporate estimation uncertainty into decision-making models, improving robustness against model misspecification.

Findings

Robust decision rules can be explicitly derived considering model ambiguity.

The framework enhances decision-making stability under uncertainty.

Application to a dynamic investment model shows improved decision robustness.

Abstract

Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model's implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to post-decision disappointment. Using statistical decision theory, we develop a framework to explore, evaluate, and optimize robust decision rules that explicitly account for estimation uncertainty. We show how to operationalize our analysis by studying robust decisions in a stochastic dynamic investment model in which a decision-maker directly accounts for uncertainty in the model's transition dynamics.