HCC: Small: Incentive-Compatible Machine Learning

计算机制设计已经非常成功地提供了一个理论和实践框架，协调决策系统中的多个，自利代理。机制设计理论中引入的一个关键属性是“激励相容性”，即即使在战略相互依赖的环境中，真实地揭示其对不同结果的偏好的私人信息也是代理人的最佳利益。然而，已经有相对较少的注意力给予使用的激励机制的目的，协调计算过程中，计算的输入分布在代理。 该项目的重点是激励相容学习的问题，其中代理的私人信息代表“训练数据”，设计目标是允许系统从这些信息代表的分布式经验中集体学习。该研究寻求促进与合作代理一样有效的自利代理学习的机制，并且在不可能的情况下理解。它将为机械设计理论和计算机科学之间架起一座新的桥梁。该研究围绕三个主题展开：（1）激励相容强化学习，其设计目标是当每个智能体拥有关于状态空间某个子集的奖励的私有信息并且可能误报时，快速学习整个状态空间的最优策略;（2）激励相容的监督学习，其中，每个代理具有一组标记的训练示例的私有知识，并且设计目标是学习使全局误差最小化的假设，尽管代理有误报训练数据的能力;（3）激励相容的信息聚合，每个主体对某些不确定事件的概率有一个主观信念，并参与到一个利用其信息的机制中，其设计目标是实现信息的在线聚合，而不考虑主体的内在私利。该项目具有广泛的溢出效益，以社会，工程和商业环境的学习是执行与自利代理的潜力。

Computational mechanism design has been highly successful in providing a theoretical and practical framework for coordinated decision making in systems with multiple, self-interested agents. A key property introduced within mechanism design theory is that of "incentive-compatibility," namely that it is an agent's best interest to truthfully reveal private information about its preferences for different outcomes even in settings of strategic interdependence. However, there has been relatively little attention given to the use of incentive mechanisms for the purpose of coordinating computational processes, where the inputs to the computation are distributed across agents. The focus of this project is on the problem of incentive-compatible learning, where the private information of agents represents "training data" and the design goal is to allow the system to collectively learn from the distributed experience that this information represents.The research seeks mechanisms that promote learning with self-interested agents that is just as effective as it would be with cooperative agents, and to otherwise understand when this is not possible. It will provide a new bridge between mechanism design theory and computer science. The research is centered around three themes: (1) incentive-compatible reinforcement learning, where the design goal is to quickly learn an optimal policy for the entire state space when each agent has private information about the rewards for some subset of the state space and may misreport them; (2) incentive-compatible supervised learning, where each agent has private knowledge of a set of labeled training examples and the design goal is to learn a hypothesis that minimizes global error despite agents' ability to misreport training data; (3) incentive-compatible information aggregation, where each agent has a subjective belief about the probability of some uncertain events and participates in a mechanism to capitalize on its information, and the design goal is to achieve online aggregation of information despite the intrinsic self-interest of agents. This project has the potential for broad spillover benefits to societal, engineering, and business settings where learning is performed with self-interested agents.