Games in Multi-Agent Dynamic Systems: Decision-Making with Compressed Information
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The model of multi-agent dynamic systems has a wide range of applications in numerous socioeconomic and engineering settings: spectrum markets, e-commerce, transportation networks, power systems, etc. In this model, each agent takes actions over time to interact with the underlying system as well as each other to achieve their respective objectives. In many applications of this model, agents have access to a huge amount of information that increases over time. Determining solutions of such multi-agent dynamic games can be complicated due to the huge domains of strategies. Meanwhile, agents have restrictions on their computational power and communication capability as well as latency limitations, which prevent them from implementing complicated strategies. Therefore, it is important to identify suitable compression schemes so that at equilibrium each agent can make decisions based on a compressed version of their information instead of the full information. However, compression of information could result in loss of some or all equilibrium outcomes. This thesis studies this issue for a general class of multi-agent dynamic games, and designs and analyzes appropriate information compression schemes. Our results highlight the tension among information compression, preservation of equilibrium outcomes, and applicability of sequential decomposition algorithms to find compression-based equilibria.
Chair: Professor Vijay Subramanian