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.
