COAL: Coalition Organization with Agent Leadership

该项目是由灾害响应团队的需求，其中包括有效的废物清除，人道主义援助，电子团购，航空货物运输和输电规划的动机。然而，该项目探索的策略适用于许多合作工作环境。研究的重点是在任务是动态的环境中形成联盟的问题，代理人提供不准确和不可靠的信息，代理人的信任是用来评估报价，领导和激励相结合，以提高联盟的形成和运作的效率。该项目是（1）调查导致理想群体行为的简单局部策略，（2）评估这些策略的可扩展性和适应性，（3）表明通过机制设计，联盟框架可以在不限制自利代理使用的算法的情况下缓和偏见并增加全局利益，和（4）表明领导提高效率。该项目正在开发算法和自适应策略，以帮助人工智能（虚拟，机器）代理在动态和未知的环境中，但该研究对人类社会的影响，其中包括应用于应急响应和其他合作工作，同时也为个人和团体在诸如晋升、招聘和大学录取等领域对多种形式的偏见的反应提供理论依据。

This project is motivated by the needs of disaster response teams, which include efficient waste removal, humanitarian aid, electronic group buying, air cargo transport, and power transmission planning. However, the strategies that the project explores apply to many cooperative work environments. The research focuses on the problem of forming coalitions in an environment in which tasks are dynamic, agents are presented with inaccurate and unreliable information, agent trust is used to evaluate offers, and a combination of leadership and incentives are used to improve the efficiency of coalition formation and operation. The project is (1) investigating simple local strategies that lead to desirable group behavior, (2) evaluating the scalability and adaptability of these strategies, (3) showing that through mechanism design the coalition framework can temper bias and increase the global good without restricting the algorithms used by self interested agents, and (4) showing that leadership increases efficiency.The project is developing of algorithms and adaptive strategies to aid artificial (virtual, machine) agents in dynamic and unknown environments, but the research has implications for human society, which include applications to emergency response and other cooperative work, and also for theorizing about the way individuals and groups respond to the many forms of bias in areas such as promotions, hiring, and college admissions.