Coordinating Multiple Decision Makers in a Service Environment

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The goal of this project is to develop methods for controlling stochastic dynamic systems that are populated with multiple interacting agents endowed with private information, different performance objectives and different risk attitudes. The research will develop mechanisms based on revenue sharing/transfer price contracts as a way to modify agent incentives and to induce decentralized decisions that are also centrally optimal. Methods for computing both optimal as well as suboptimal revenue sharing contracts will be developed using ideas from approximate dynamic programming. Applications to multi-agent systems of interest to operations research and management science including the coordination of airline alliances, multi-agent risk management, and multi-agent service systems will be studied.If successful, the results of this research will lead to a greater understanding of the mechanisms that can be used to coordinate interacting decentralized agents in stochastic dynamic systems. It will also lead to the development of efficient methods for computing optimal and/or suboptimal revenue sharing contracts that are used to coordinate these decision makers. These results will provide guidelines for organizing and managing complex systems that are populated with multiple agents.