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 a unified pipeline and DEB. This architecture solves the full distribution of problem instances, parameterized by a context, handling all games across a wide range of scales (from two to sixteen actions per player).

翻译：由于计算复杂性、非唯一性及均衡结果的不稳定性，自动设计具有理想均衡结果的多智能体交互本质上具有挑战性。本研究提出使用与具体博弈无关的可微分均衡模块，将其作为新型可微分框架中的组件，以解决经济学与计算机科学中广泛存在的各类激励设计问题。我们将此框架称为深度激励设计。为验证该方法，我们考察了三种多样且具有挑战性的激励设计任务：契约设计、机器调度与逆均衡问题。针对每项任务，我们使用统一的处理流程与可微分均衡模块训练单一神经网络。该架构能够求解由上下文参数化的问题实例的完整分布，处理从两名玩家到每名玩家最多十六种行动的大规模博弈。