Strategic Interactions and Incentive Mechanisms on Multi-scale Networks
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The strategic interactions among a large number of interdependent agents are commonly modeled as network games. The research in network games has seen significant advances over the last decade and the network game framework allows us to model and solve real-world problems such as the provisioning of public goods, decision-making in cyber-physical systems, and the understanding of shock propagation in financial markets. In this thesis, we are interested in games on networks that enjoy various structural properties that arise naturally in many applications, such as groups, communities, and multi-relational interdependence, and see to explore such structural properties in the analysis of these games. These properties often result in a multi-scale structure, in which agents can be grouped into larger communities/units, which can then be further grouped, and so on. These communities can be physical or logical, depending on what the graphical connectivity represents. We aim to develop analytical and algorithmic tools for studying this type of network game, with a particular interest in equilibrium analysis and the design of intervention and incentive mechanisms.
Chair: Mingyan Liu