$\alpha$-Rank-Collections: Analyzing Expected Strategic Behavior with Uncertain Utilities

TL;DR

This paper introduces $eta$-Rank-collections, a new solution concept for Bayesian games that captures the expected strategic behavior under utility uncertainty, especially useful in matching markets without dominant strategies.

Contribution

It extends $eta$-Rank to Bayesian games, providing a novel way to analyze strategic play with uncertain utilities in complex matching markets.

Findings

$eta$-Rank-collections effectively predict strategic behavior under utility uncertainty.

The concept is invariant to positive affine transformations, ensuring robustness.

Experimental results on the Boston mechanism demonstrate nuanced predictions beyond Bayes-Nash equilibria.

Abstract

Game theory relies heavily on the availability of cardinal utility functions, but in fields such as matching markets, only ordinal preferences are typically elicited. The literature focuses on mechanisms with simple dominant strategies, but many real-world applications lack dominant strategies, making the intensity of preferences between outcomes important for determining strategies. Even though precise information about cardinal utilities is not available, some data about the likelihood of utility functions is often accessible. We propose to use Bayesian games to formalize uncertainty about the decision-makers' utilities by viewing them as a collection of normal-form games. Instead of searching for the Bayes-Nash equilibrium, we study how uncertainty in utilities is reflected in uncertainty of strategic play. To do this, we introduce a novel solution concept called $\alpha$-Rank-collections, which extends $\alpha$-Rank to Bayesian games. This allows us to analyze strategic play in, for example, non-strategyproof matching markets, for which appropriate solution concepts are currently lacking. $\alpha$-Rank-collections characterize the expected probability of encountering a certain strategy profile under replicator dynamics in the long run, rather than predicting a specific equilibrium strategy profile. We experimentally evaluate $\alpha$-Rank-collections using instances of the Boston mechanism, finding that our solution concept provides more nuanced predictions compared to Bayes-Nash equilibria. Additionally, we prove that $\alpha$-Rank-collections are invariant to positive affine transformations, a standard property for a solution concept, and are efficient to approximate.