Yamil Velez │ Stony Brook University │ yamil.velez@stonybrook

1. The Promise of Empirically-Grounded Agent-Based Models:An Application to Immigration Politics in the United States Yamil Velez │ Stony Brook University │ yamil.velez@stonybrook.edu Problem Recent research in political science has found that changes in local immigrant populations produce hostility toward immigrants, leading to increases in anti-immigration sentiment and local policies directed toward immigrant groups (Hopkins 2010; Newman 2012; Enos 2014). Most of this research assumes that local area demographics are exogenous to racial and ethnic preferences. However, a growing body of work on the topic of “native flight” suggests otherwise (Crowder, Hall, and Tolnay 2011). Solution Agent-based modeling is a flexible simulation-based method that is well suited to formalizing dynamic relationships between agents and their environments. Traditionally, agent-based modeling has been used as a tool for solving models that do not have closed-form solutions. However, advances in computing have made possible to construct agent-based models (ABMs) that can be tied to empirical data. This allows us to model relationships that would otherwise be impossible to model using traditional regression-based methods. Application In the past couple of decades, local communities across the United States have proposed anti-immigration ordinances and states like Arizona have enacted exclusionary policies targeting immigrant groups. During that same time period, native-born residents have been moving out of ethnically diversifying neighborhoods and settling into more homogenous neighborhoods(Crowder, Hall, and Tolnay 2011). I build an agent-based model that integrates insights from both of these literatures in order to build a more comprehensive model of localized nativism. Estimation Procedure and Results Gather geo-tagged data from the 2000 Census. Convert these data into a raster matrix. Incorporate these data into agents’ utility functions and randomly generate weights for each parameter during every simulation. Execute the simulation using the behavioral rules outlined above. For each simulation (out of 10,000), measure the fit between predicted values and observed values (online anti-immigration petition signatures) using R2. Select the best-fitting models (99th percentile), and assess the prevalence of certain parameter values in this subset of “best-fitting models” Model of Immigrant Mobility ABMs can have predictive power. However, due to their non-linear nature, finding consistent parameter estimates is difficult for some variables. Nonetheless, they provide an opportunity to build dynamic theory-guided estimators. Next steps: Tune the model using both mobility and political action data. Experiment with simpler ABMs. Model of Native Resident Reactions Implementation Discussion