通过公共池资源博弈激励异构团队中的协作
Incentivizing Collaboration in Heterogeneous Teams via Common-Pool Resource Games
AI总结：
针对异构智能体团队处理同质任务的问题，构建公共池资源博弈并设计效用函数，证明唯一纯纳什均衡存在且最佳响应动力学收敛，给出无效率指标上界。
摘要：
我们考虑一个由异构智能体组成的团队，该团队共同负责处理一系列同质任务，并随后对其进行审查。每个智能体分别具有与处理任务和审查任务相关的平均服务时间和平均审查时间。智能体根据其服务接纳率和审查接纳率获得奖励。团队的目标是协作地最大化“已服务并审查”的任务数量。我们构建了一个公共池资源（CPR）博弈，并设计了效用函数，以去中心化的方式激励异构智能体之间的协作。我们证明了唯一纯纳什均衡（PNE）的存在性，并建立了最佳响应动力学向该唯一PNE的收敛性。最后，我们建立了PNE三个无效率指标的解析上界，即无政府状态价格、总审查接纳率之比和延迟之比，并进行了实证研究。
英文摘要：
We consider a team of heterogeneous agents that is collectively responsible for servicing, and subsequently reviewing, a stream of homogeneous tasks. Each agent has an associated mean service time and a mean review time for servicing and reviewing the tasks, respectively. Agents receive a reward based on their service and review admission rates. The team objective is to collaboratively maximize the number of "serviced and reviewed" tasks. We formulate a Common-Pool Resource (CPR) game and design utility functions to incentivize collaboration among heterogeneous agents in a decentralized manner. We show the existence of a unique Pure Nash Equilibrium (PNE), and establish convergence of best response dynamics to this unique PNE. Finally, we establish an analytic upper bound on three measures of inefficiency of the PNE, namely the price of anarchy, the ratio of the total review admission rate, and the ratio of latency, along with an empirical study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
