Mechanism Learning for Trading Networks

TL;DR

This paper investigates the design of trading network mechanisms that satisfy key economic properties, proving their non-existence in some cases and proposing learning-based solutions in a Bayesian framework.

Contribution

It establishes the impossibility of ex post mechanisms for broad trading networks and introduces computational methods to learn mechanisms satisfying key properties in a Bayesian setting.

Findings

Proved non-existence of certain mechanisms in broad trading networks.

Developed techniques for efficient mechanism computation and learning.

Empirically demonstrated successful mechanism design in complex networks.

Abstract

We study the problem of designing mechanisms for trading networks that satisfy four desired properties: dominant-strategy incentive compatibility, efficiency, weak budget balance (WBB), and individual rationality (IR). Although there exist mechanisms that simultaneously satisfy these properties ex post for combinatorial auctions, we prove the impossibility that such mechanisms do not exist for a broad class of trading networks. We thus propose approaches for computing and learning the mechanisms that satisfy the four properties, in a Bayesian setting, where WBB and IR, respectively, are relaxed to ex ante and interim. For computational and sample efficiency, we introduce several techniques, including game theoretical analysis to reduce the input feature space. We empirically demonstrate that the proposed approaches successfully find the mechanisms with the four properties for those trading networks where the impossibility holds ex post.