Traditional methods such as gravity models and general equilibrium theory for two-sided network analysis focus mainly on characterizing the aggregate and macro-level outcomes of two-sided interactions that commonly occur in a wide range of applications such as trade market, transportation, and migration. As customer-oriented service and human-centered design become more feasible in the information age, theories and models that capture and represent individual behaviors are crucial and essential for the studies on two-sided networks, i.e. in understanding the observations of two-sided interactions, forecasting future activities, and designing policies, platforms, markets and mechanisms to achieve desirable outcomes. For example, in international trade analysis, we need advanced agent-based theories and models to explain two-sided phenomena observed in trade, forecast trade levels, and design rules and platforms to promote fair and efficient market operations.;Matching theory, one of the most exciting intellectual endeavors of human minds, promises, in the author's view, suitable methodologies and powerful analytical tools for the study of how the agents in a network or market make decisions and interact, hence how to formulate matching mechanisms for desirable outcomes. This dissertation aims to contribute to the matching literature by proposing and studying generalized matching, which expands the existing matching theory to multi-unit many-to-many matching with quota constraints. This is a more general and realistic framework for matching that happens in real world. First, models for two-sided and one-sided matching with newly defined preference relationships and solution concepts are developed to pave the theoretical foundation for analyzing multi-unit and multi-partner matching with quota constraints. The corresponding new matching mechanisms are then designed to produce stable and favorable matching outcomes. Second, a hybrid model for generalized matching is established to encompass both one-sided and two-sided matching under the generalized framework. Again, the corresponding hybrid matching mechanism with desired properties is proposed and discussed. Next, linking the newly proposed theoretical work to empirical application, a novel bi-level estimation model is proposed for generalized matching to make inferences of agents' matching behaviors/decisions. Last but not least, the dissertation also points out the remaining challenges and offers opinions on directions and topics for future research.
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