不同协同作用下的在线团队组建
Online Team Formation under Different Synergies
摘要：
团队形成在许多领域都很普遍：教育、劳动力市场、体育等。团队的成功取决于其成员的潜在类型，这些类型无法直接观察到，但可以（部分）从过去的表现中推断出来。从负责人试图选择团队的角度来看，这会导致自然的探索-开发权衡：保留早期发现的成功团队，或重新分配代理以了解更多关于他们类型的信息？我们研究了一个自然的模型在线团队的形成，其中一个主要的反复分区一组代理成团队。Agent具有二元潜在类型，每个团队由两名成员组成，团队的绩效是其成员类型的对称函数。在多轮比赛中，委托人选择匹配而不是代理人，并招致等于成功团队数量与给定函数的最佳匹配之间的赤字的遗憾。我们的工作提供了两个二进制输入的所有对称函数的后悔景观的完整表征。特别是，我们开发的团队选择政策，尽管是不可知的模型参数，实现最佳或接近最佳的遗憾对自适应对手。
英文摘要：
Team formation is ubiquitous in many sectors: education, labor markets, sports, etc. A team's success depends on its members' latent types, which are not directly observable but can be (partially) inferred from past performances. From the viewpoint of a principal trying to select teams, this leads to a natural exploration-exploitation trade-off: retain successful teams that are discovered early, or reassign agents to learn more about their types? We study a natural model for online team formation, where a principal repeatedly partitions a group of agents into teams. Agents have binary latent types, each team comprises two members, and a team's performance is a symmetric function of its members' types. Over multiple rounds, the principal selects matchings over agents and incurs regret equal to the deficit in the number of successful teams versus the optimal matching for the given function. Our work provides a complete characterization of the regret landscape for all symmetric functions of two binary inputs. In particular, we develop team-selection policies that, despite being agnostic of model parameters, achieve optimal or near-optimal regret against an adaptive adversary.
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。
