Deep Incentive Design with Differentiable Equilibrium Blocks
Automated design of multi-agent interactions with desirable equilibrium outcomes is inherently difficult due to the computational hardness, non-uniqueness, and instability of the resulting equilibria. In this work, we propose the use of game-agnostic differentiable equilibrium blocks (DEBs) as modules in a novel, differentiable framework to address a wide variety of incentive design problems from economics and computer science. We call this framework deep incentive design (DID). To validate our approach, we examine three diverse, challenging incentive design tasks: contract design, machine scheduling, and inverse equilibrium problems. For each task, we train a single neural network using ...
| Authors | Vinzenz Thoma, Georgios Piliouras, Luke Marris |
|---|---|
| Primary category | cs.GT |
| Published | 8 March 2026 |
| Last updated at source | 12 March 2026 |
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