Computational Analysis of Intelligent Agents: Social and Strategic Settings

The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase `intelligent agents' to imply in some sense, a `bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be `rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.

REFUGEES AND RESETTLEMENT: A QUALITATIVE ANALYSIS OF REFUGEE INTEGRATION THROUGH SOCIAL & SUPPORT SERVICES

This dissertation studies refugee resettlement in the United States utilizing the Integration Indicator’s framework developed by Ager and Strang for the U.S. context. The study highlights the U.S. refugee admissions program and the policies in the states of Maryland and Massachusetts while analyzing the service delivery models and its effects on refugee integration in these locations. Though immigration policy and funding for refugee services are primarily the domain of the federal government, funds are allocated through and services are delivered at the state level. The Office of Refugee Resettlement (ORR), which operates under the Department of Health and Human Services, was established after the Refugee Act of 1980 to deliver assistance to displaced persons. The ORR provides funds to individual states primarily through The Refugee Social Service and Targeted Assistance Formula Grant programs. Since the inauguration of the ORR three primary models of refugee integration through service delivery have emerged. Two of the models include the publicly/privately administered programs, where resources are allocated to the state in conjunction with private voluntary agencies; and the Wilson/Fish Alternative programs, where states sub-contract all elements of the resettlement program to voluntary agencies and private organizations —in which they can cease all state level participation and voluntary agencies or private organizations contract directly from the ORR in order for all states to deliver refugee services where the live. The specific goals of this program are early employment and economic self-sufficiency. This project utilizes US Census, state, and ORR data in conjunction with interviews of refugee resettlement practitioners involved in the service delivery and refugees. The findings show that delivery models emphasizing job training, English instruction courses, institutional collaboration, and monetary assistance, increases refugee acclimation and adaptation, providing insight into their potential for integration into the United States.