In this study, I present and validate the first version of MAPES, an agent-based model (ABM) that facilitates the study of international migration (IM) by providing a framework within which theories from different fields can be easily modeled and reconciled. To validate this model, I examine three scenarios that test the capabilities of the model and provide opportunities to make substantive contributions to the field of international migration studies. I use the first of these scenarios, the bilateral economic gradient scenario, to show how rational decisionmaking by individuals gives rise to anti-immigrant sentiment and the flow patterns predicted by macro-economic migration theory. The second scenario, which I call the trilateral equal scenario, exhibits the theoretical phenomenon of path dependence, in which small random changes lead to large differences in migration system outcomes. Finally, I use a scenario that models the real-world border between Mexico and the US to predict the impacts of raising the cost of border crossings from south to north. Through these efforts, I show that MAPES is a research tool which can used to make valuable contributions to the field of IM studies.

Many educational websites offer tools that predict students' chances of admissions to college using models estimated on crowd-sourced data. Unfortunately, these data are generally not representative of the actual population of college applicants, so admission probability calculators (APCs) which use them offer biased estimates of admission probability, Pr(A). As a solution, I propose a simulation-based framework for estimating Pr(A) that is not modeled on crowd-sourced data. I model the joint distribution of enrollee characteristics at a particular university using a Gaussian copula and official margin data published by the College Board and the university itself. I fit a model for Pr(A) to this data based on several reasonable assumptions about the character of the applicant pool. I show that the estimates of Pr(A) yielded by my framework are excellent predictors of admission outcome for a sample of student data collected from the Internet.