Designing Double Auction Mechanism Through Deep Learning

Abstract

Mechanism design, a branch of economics, aims at designing rules that can autonomously achieve desired outcomes in resource allocation and public decision making. The research on mechanism design using machine learning is called automated mechanism design or mechanism learning. In our research, we construct a new network based on the existing method for single auctions and aim to automatically design an mechanism by applying it to double auctions. Especially, we focus on the following four desirable properties for the mechanism: individual rationality, balanced budget, Pareto efficiency, and incentive compatibility. We conducted experiments assuming a small-scale double auction and clarified the convergence of the obtained mechanism. We also confirmed how much the learnt mechanism satisfies the four properties compared to two representative protocols. As a result, we verified that the mechanism is more budget-balanced than VCG protocol and more Pareto-efficient than MD protocol, with the incentive compatibility mostly guaranteed.