Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling Annetta Burger Department of Computational and Data Sciences George Mason University USA aburger2@gmu.edu Talha Oz Department of Computational and Data Sciences George Mason University USA toz@gmu.edu Andrew Crooks Department of Computational and Data Sciences George Mason University USA acrooks@gmu.edu William G. Kennedy Center for Social Complexity George Mason University USA wkennedy@gmu.edu ABSTRACT Agent-based modeling is a means for researchers to conduct large-scale computer experiments on synthetic human populations and study their behaviors under different conditions. These models have been applied to questions regarding disease spread in epidemiology, terrorist and criminal activity in sociology, and traffic and commuting patterns in urban studies. However, developing realistic control populations remains a key challenge for the research and experimentation. Modelers must balance the need for representative, heterogeneous populations with the computational costs of developing large population sets. Increasingly these models also need to include the social network relationships within populations that influence social interactions and behavioral patterns. To address this we used a mixed method of iterative proportional fitting and network generation to build a synthesized subset population of the New York megacity and region. Our approach demonstrates how a robust population and social network relevant to specific human behavior can be synthesized for agent-based models. KEYWORDS Agent-based Models, Geographical Systems, Population Synthesis, Social Networks, Mega- city 1 INTRODUCTION Agent-based models (ABMs) are increasingly being used to study complex systems involving human and environment interactions such as in the areas of epidemiology, transportation, migration, climate change, and urban studies [1], yet the social networks that inform and influence human interactions remain largely absent from these models. Creating robust synthetic populations with their social networks remains a challenge in agent-based models. Traditional population synthesis methods of synthetic reconstruction and combinatorial optimization involve generating the population by fitting individual agents into set distributions of attributes based on contingency tables from demographic statistical or survey data. These distributions of attributes do not extend to the social network tie information that may be latent in the demographic data or captured in social media data. The proposed method addresses the gap between current population synthesis and social network analysis with a set of algorithms to generate synthetic social networks for agent-based models. Our study uses an agent-based model to simulate human behavior in the event of a nuclear explosion in New York City. Both the