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.

ADVERTISEMENT ALLOCATION AND TRUST MECHANISMS DESIGN IN SOCIAL NETWORKS

Social network sites (SNS), such as Facebook, Google+ and Twitter, have attracted hundreds of millions of users daily since their appearance. Within SNS, users connect to each other, express their identity, disseminate information and form cooperation by interacting with their connected peers. The increasing popularity and ubiquity of SNS usage and the invaluable user behaviors and connections give birth to many applications and business models. We look into several important problems within the social network ecosystem. The first one is the SNS advertisement allocation problem. The other two are related to trust mechanisms design in social network setting, including local trust inference and global trust evaluation. In SNS advertising, we study the problem of advertisement allocation from the ad platform's angle, and discuss its differences with the advertising model in the search engine setting. By leveraging the connection between social networks and hyperbolic geometry, we propose to solve the problem via approximation using hyperbolic embedding and convex optimization. A hyperbolic embedding method, \hcm, is designed for the SNS ad allocation problem, and several components are introduced to realize the optimization formulation. We show the advantages of our new approach in solving the problem compared to the baseline integer programming (IP) formulation. In studying the problem of trust mechanisms in social networks, we consider the existence of distrust (i.e. negative trust) relationships, and differentiate between the concept of local trust and global trust in social network setting. In the problem of local trust inference, we propose a 2-D trust model. Based on the model, we develop a semiring-based trust inference framework. In global trust evaluation, we consider a general setting with conflicting opinions, and propose a consensus-based approach to solve the complex problem in signed trust networks.

UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.