Date of Degree
The study of complex systems is an important area of research. Many scenarios require the ability to simulate large multi-agent systems with minimal artificial assumptions. We are currently living in a world where the adoption of artificial intelligence (AI) in various areas is increasing rapidly. This, in turn, has serious consequences from a computational and policy perspective. The focus needs to be on designing systems that are not only computationally elegant and efficient but also ethical. The goal of this thesis is to examine some of the ways AI can be used to simulate complex social systems. In addition, we will analyze some of the key principles like fairness, efficiency, cooperation, and privacy that may potentially influence mechanism design.
We will illustrate how these principles may be integrated into the design of complex systems by proposing some versatile models that can be applied to a variety of real-life scenarios. First, we will consider an agent-based modeling framework that can be used to model complex systems involving the bilateral exchange of resources between agents. We will illustrate the versatility of these models by applying them to different real-world scenarios like kidney exchange, barter exchange, and Vickrey auctions. Next, we propose a GAN-based mechanism that can be used for sequential imputation tasks. We will then discuss how this can be useful in designing privacy-preserving recommender systems.
Chakraborty, Haripriya, "Mechanism Design and Modeling to Analyze Complex Social Systems for Public Policy" (2021). CUNY Academic Works.
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