Estimating the Preferences of Imperfectly Rational Agents: Behavioral Econometrics

Abstract

Modelling agent preferences has applications in a range of fields including economics and increasingly, artificial intelligence. These preferences are not always known and thus may need to be estimated from observed behavior, in which case a model is required to map agent preferences to behavior. Traditional models are based on the assumption that agents are perfectly rational: that is, they perfectly optimize and behave in accordance with their own interests. Work in the field of behavioral game theory has shown, however, that human agents often make decisions that are imperfectly rational, and the field has developed models that relax the perfect rationality assumption. In this thesis, we take a first step towards estimating agent preferences using this relaxed assumption. We apply models developed for predicting behavior towards the task of estimating preferences and show that they outperform both traditional and commonly used benchmark models on data collected from human subjects. In fact, Nash equilibrium and its relaxation, quantal response equilibrium (QRE), can induce an inaccurate estimate of agent preferences when compared against a known ground truth. A key finding is that modelling non-strategic behavior, conventionally considered as uniform noise, is important for estimating preferences. To this end, we introduce quantal-linear4, a rich non-strategic model. We also propose an augmentation to the QRE model by incorporating a non-strategic component. We call this augmented model QRE+L0 and find an improvement in estimating values over the standard QRE. QRE+L0 allows for alternative models of non-strategic behavior in addition to quantal-linear4.