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).
Oceanic Games: Centralization Risks and Incentives in Blockchain Mining
为了参与无权限区块链的分布式共识，潜在的节点-或矿工-提供指定的昂贵资源的证明。然而，与预期的去中心化相反，目前关于区块链采矿的数据显示，这些资源越来越集中在少数几个主要实体中，通常是矿池。为了研究这种情况下的战略考虑，我们采用了米尔诺和沙普利（Milnor and Shapley，1978）的海洋游戏概念。海洋游戏已经被用来分析公司环境中的决策，其中有少数占主导地位的参与者（股东）和大量的个人无关紧要的参与者，海洋。与标准均衡模型不同，它们侧重于衡量给定资源分配中每个实体和每单位资源的价值（或权力）。这些价值观被视为联盟形成、合并和资源收购的战略组成部分。考虑到这些与区块链治理和长期可持续性相关的问题，我们将海洋游戏应用于区块链采矿，并通过示例说明定义的概念。现有结果的应用揭示了个体采矿者合并以增加其资源价值的动机。这为所观察到的采矿权的集中和集中提供了另一种视角。除了数值模拟之外，我们还使用该模型来识别与未来加密货币设计相关的问题，并制定前瞻性的研究问题。
