RI: Small: Theory and Application of Mechanism Design for Team Formation

在任务复杂性比比皆是的世界中，团队合作是常态而不是例外。因此，组建有效的团队对于几乎所有组织以及广泛的领域都至关重要，例如大型软件项目、课程项目、军事行动、建筑项目和搜救任务。然而，成功的团队组建取决于了解团队中的个人如何在完成任务时相互补充、共同工作的能力，以及对不同任务可能实现的目标可能存在的偏好不一致。该项目的目标是开发一种通用的团队组建交换服务，在潜在队友的能力和偏好不确定时，利用新的理论基础设计团队组建机制。 这种交流将显着提高组织动态组建敏捷高效的项目团队的能力，减少冲突并更好地协调个人和组织的激励措施。该项目考虑了在为一组目标服务的团队组建背景下机制设计的一个极具挑战性的概念和技术问题。核心概念挑战在于对问题进行足够详细的建模，以捕获诸如任务实现子目标、代理能力、任务要求以及代理对目标和队友的偏好等考虑因素，但避免不必要的细节以保持易处理性。拟议的研究提供了一个新颖的建模框架，旨在平衡这两个考虑因素。技术贡献涉及对问题的新颖理论处理，例如相关领域机制的新颖扩展的表征，包括组合分配、针对当前问题量身定制的全新机制，以及利用特定问题结构对最佳可扩展自动化机制设计的算法贡献。此外，拟议的研究将通过使用人体实验作为评估所开发机制的手段之一，将实验经济学研究与理论和计算经济学的贡献结合起来。

In a world where complexity of tasks abounds, teamwork is the norm rather than the exception. Formation of effective teams is therefore vital in nearly any organization, and across a broad array of domains, such as large-scale software projects, course projects, military operations, construction projects, and search-and-rescue tasks. Successful team formation, however, hinges on having knowledge about how individuals working on a team complement each other in service of a task, their ability to work together, as well as potentially misaligned preferences about goals that the different tasks may achieve. The goal of this project is to develop a general-purpose team formation exchange service, leveraging new theoretical foundations for designing team formation mechanisms when the abilities and preferences of prospective teammates are uncertain. Such an exchange would significantly improve the ability of organizations to dynamically form agile and efficient project teams, reducing conflict and better aligning individual and organizational incentives. This project considers an exceptionally challenging conceptual and technical problem of mechanism design in the context of team formation in service of a set of goals. The core conceptual challenge lies in modeling the problem in sufficient detail to capture considerations such as achievement of sub-goals by tasks, agent capabilities, task requirements, and agent preferences about goals and teammates, but avoiding unnecessary detail to maintain tractability. The proposed research offers a novel modeling framework aimed at balancing these two considerations. The technical contributions involve both novel theoretical treatment of the problem, such as characterizations of novel extensions of mechanisms from related domains, including combinatorial assignment, entirely new mechanisms tailored to the problem at hand, and algorithmic contributions to optimal scalable automated mechanism design leveraging specific problem structure. Additionally, the proposed research will blend experimental economics research with contributions in theoretical and computational economics by using human subject experiments as one of the means to evaluate the developed mechanisms.