Optimal Stopping with Behaviorally Biased Agents: The Role of Loss Aversion and Changing Reference Points

TL;DR

This paper models how loss aversion and shifting reference points influence decision-making in optimal stopping problems, revealing bounds on biased agents' performance and fundamental differences from rational agents.

Contribution

It introduces a behavioral model of optimal stopping incorporating reference dependence and loss aversion, providing bounds and fundamental insights into biased decision-making.

Findings

Tight bounds on biased agents' performance relative to the best possible outcome.

Exponential gap in performance between worst-case and random order scenarios.

Fundamental differences identified between rational and biased agents in stopping problems.

Abstract

People are often reluctant to sell a house, or shares of stock, below the price at which they originally bought it. While this is generally not consistent with rational utility maximization, it does reflect two strong empirical regularities that are central to the behavioral science of human decision-making: a tendency to evaluate outcomes relative to a reference point determined by context (in this case the original purchase price), and the phenomenon of loss aversion in which people are particularly prone to avoid outcomes below the reference point. Here we explore the implications of reference points and loss aversion in optimal stopping problems, where people evaluate a sequence of options in one pass, either accepting the option and stopping the search or giving up on the option forever. The best option seen so far sets a reference point that shifts as the search progresses, and a biased decision-maker's utility incurs an additional penalty when they accept a later option that is below this reference point. We formulate and study a behaviorally well-motivated version of the optimal stopping problem that incorporates these notions of reference dependence and loss aversion. We obtain tight bounds on the performance of a biased agent in this model relative to the best option obtainable in retrospect (a type of prophet inequality for biased agents), as well as tight bounds on the ratio between the performance of a biased agent and the performance of a rational one. We further establish basic monotonicity results, and show an exponential gap between the performance of a biased agent in a stopping problem with respect to a worst-case versus a random order. As part of this, we establish fundamental differences between optimal stopping problems for rational versus biased agents, and these differences inform our analysis.