COAL: Coalition Organization with Agent Leadership

该项目是由灾害应对小组的需要推动的，这些小组包括高效的废物清除、人道主义援助、电子团购、航空货运和电力传输规划。然而，该项目探索的策略适用于许多协作工作环境。研究的重点是动态任务环境下的联盟形成问题，向代理提供不准确和不可靠的信息，代理信任用于评估报价，领导力和激励相结合来提高联盟的形成和运行效率。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主体使用的算法的情况下缓和偏见并增加全局效益，以及(4)展示领导力提高效率。该项目正在开发算法和自适应策略，以帮助人工(虚拟、机器)主体在动态和未知的环境中，但该研究对人类社会有影响，包括应用于紧急响应和其他合作工作，也是为了对个人和团体如何应对晋升、招聘和大学招生等领域的多种形式的偏见进行理论研究。

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.