Minimal Regret Walras Equilibria for Combinatorial Markets

TL;DR

This paper introduces the concept of $ ext{Delta}$-regret Walras equilibria in combinatorial markets, characterizes their existence through social welfare duality, and connects these ideas to linear optimization sensitivity, enabling new algorithms and bounds.

Contribution

It provides a complete characterization of $ ext{Delta}$-regret equilibria using duality and integrality gaps, linking market equilibrium existence to linear optimization sensitivity and approximation algorithms.

Findings

Characterization of $ ext{Delta}$-regret equilibria via duality gaps.

Connection between regret bounds and linear optimization sensitivity.

Translation of social welfare algorithms into low-regret equilibrium algorithms.

Abstract

We consider combinatorial multi-item markets and propose the notion of a $\Delta$-regret Walras equilibrium, which is an allocation of items to players and a set of item prices that achieve the following goals: prices clear the market, the allocation is capacity-feasible, and the players' strategies lead to a total regret of $\Delta$. The regret is defined as the sum of individual player regrets measured by the utility gap with respect to the optimal item bundle given the prices. We derive a complete characterization for the existence of $\Delta$-regret equilibria by introducing the concept of a parameterized social welfare problem, where the right-hand side of the original social welfare problem is changed. Our characterization then relates the achievable regret value with the associated duality/integrality gap of the parameterized social welfare problem. For the special case of monotone valuations this translates to regret bounds recovering the duality/integrality gap of the original social welfare problem. We further establish an interesting connection to the area of sensitivity theory in linear optimization. We show that the sensitivity gap of the optimal-value function of two (configuration) linear programs with changed right-hand side can be used to establish a bound on the achievable regret. Finally, we use these general structural results to translate known approximation algorithms for the social welfare optimization problem into algorithms computing low-regret Walras equilibria. We also demonstrate how to derive strong lower bounds based on integrality and duality gaps but also based on NP-complexity theory.