Differentiable Economics: Auctions, Data Markets, and Matching Markets

Abstract

Economic mechanism design shapes our economic and social systems, silently influencing outcomes for billions worldwide—from auctions powering global online advertising to matching algorithms determining hospital placements for medical residents. Yet, despite decades of significant theoretical advances, classical economic approaches have encountered analytical bottlenecks, leaving fundamental questions unresolved. Inspired by transformative breakthroughs in the application of AI and machine learning to the natural and physical sciences, this thesis pioneers and extends Differentiable Economics, a computational framework that integrates economic theory with deep learning to systematically design optimal economic mechanisms. Differentiable Economics reformulates economic design as an end-to-end optimization problem, representing auctions, data markets, and matching mechanisms through differentiable neural architectures. This allows previously intractable economic problems to be solved systematically and with practical computational and statistical efficiency. This thesis is organized into three main parts: In Part 1, Auctions, I propose two neural architectures: RegretNet, a characterization-free method that flexibly addresses the design of complex multi-item auctions, and RochetNet, a characterization-based architecture that explicitly enforces incentive compatibility for single buyer settings. These architectures successfully replicate known optimal solutions, validate important conjectures, and discover entirely new mechanisms in scenarios where analytical solutions remain elusive. Additionally, I extend the RochetNet framework to a sequential setting by developing a new reinforcement learning approach that outperforms traditional RL methods and analytical baselines.

In Part 2, Data Markets, I design neural network architectures capable of learning signaling schemes for selling information. This framework can effectively handle new economic constraints arising from obedience conditions, modeling the action that an agent will take upon receiving information, and incentive compatibility. This approach not only replicates established theoretical benchmarks but also identifies and validates novel optimal signaling strategies previously unknown in economic theory.

In Part 3, Matching Markets, I introduce permutation-equivariant convolutional neural networks alongside novel differentiable surrogate metrics for stability and incentive compatibility in the design of two-sided matching markets. This combination enables the computational exploration and characterization of previously unknown trade-offs between stability and incentive compatibility.

Taken together, these contributions highlight the potential of Differentiable Economics as a flexible and powerful methodology for economic design. This thesis takes foundational steps towards democratizing economic mechanism design, providing economists and computer scientists alike with new, accessible tools to address long-standing theoretical challenges in optimal economic design.