Optimal Stopping with Multi-Dimensional Comparative Loss Aversion

TL;DR

This paper investigates multi-dimensional loss aversion in optimal stopping problems, revealing phase transitions and bounds on competitive ratios that differ significantly from single-dimensional models.

Contribution

It introduces a multi-dimensional model of loss aversion, establishing phase transition phenomena and tight bounds on competitive ratios, extending prior single-dimensional analyses.

Findings

Sharp phase transition at mbda 7 (k-1) = 1

Tight bounds on competitive ratios when mbda 7 (k-1) < 1

Constant-factor gap between random-order and worst-case prophet inequalities for k 3 2

Abstract

Despite having the same basic prophet inequality setup and model of loss aversion, conclusions in our multi-dimensional model differs considerably from the one-dimensional model of Kleinberg et al. For example, Kleinberg et al. gives a tight closed-form on the competitive ratio that an online decision-maker can achieve as a function of $\lambda$, for any $\lambda \geq 0$. In our multi-dimensional model, there is a sharp phase transition: if $k$ denotes the number of dimensions, then when $\lambda \cdot (k-1) \geq 1$, no non-trivial competitive ratio is possible. On the other hand, when $\lambda \cdot (k-1) < 1$, we give a tight bound on the achievable competitive ratio (similar to Kleinberg et al.). As another example, Kleinberg et al. uncovers an exponential improvement in their competitive ratio for the random-order vs. worst-case prophet inequality problem. In our model with $k\geq 2$ dimensions, the gap is at most a constant-factor. We uncover several additional key differences in the multi- and single-dimensional models.