Computationally-enabled public policy using comprehensive data

Nov. 2, 2016 from 3:30 to 4:20 p.m. — Johnson Hall, room 075

Department Chair, Krasnow Institute for Advanced Study, George Mason University
Watch a recording of this seminar on YouTube.

Abstract

The social sciences are being revolutionized today by two distinct forces, data and computing. The ability to perform controlled experiments, both in laboratory (small scale) and web-facilitated (large scale) settings, combine with natural experiments and digital exhaust type click-stream data to provide an unprecedented window into human behavior in a wide variety of social contexts. But just as significant is the increasing availability of administratively-complete micro-data that offer nearly comprehensive portraits of important social phenomena. Computational techniques and tools are essential for managing such data, and for creating models capable of explaining the data. Specifically, agent-based computing is an emerging technology for representing individuals engaged in social behavior and grounding them in micro-data. In this talk I will start with some background material on agent computing, discussing how the approach has been utilized for abstract models of social processes. I will then go on to describe two large-scale agent models that utilize individual-level data. A model of the U.S. housing market bubble that burst c 2006-7 will be described for the Washington, D.C. area. It involves some 2 million housing units overall with more than a million homeowners and some 500K mortgages. The model combines data on the housing stock (county sources), borrowers (Census), and mortgages (from mortgage service providers), and the model output is compared to MLS transactional data. We have investigated alternative policies for attenuating the size of the bubble. Then a model of the U.S. private sector, 120 million employees organized into 6 million firms, will be presented. This model uses data on the entire population of tax-paying firms in the U.S. and closely reproduces firm sizes, ages, growth rates, job tenure, wage distributions, and so on. In these models, aggregate phenomena emerge from the interactions of the agents without any pre-specification of what might happen. That is, social phenomena grow from the bottom up.

Bio

Rob Axtell earned an interdisciplinary Ph.D. degree at Carnegie Mellon University, where he studied computing, social science, and public policy. His teaching and research involves computational and mathematical modeling of social and economic processes. Specifically, he works at the intersection of multi-agent systems computer science and the social sciences, building so-called agent-based models for a variety of market and non-market phenomena. His research has been published in the leading scientific journals, including Science and the Proceedings of the National Academy of Sciences, USA, and reprised in Nature, and has appeared in top disciplinary journals (e.g., American Economic Review, Computational and Mathematical Organization Theory, Economic Journal), in general interest journals (e.g., PLOS One) and in specialty journals (e.g., Journal of Regulatory Economics, Technology Forecasting and Social Change.) He is co-author of Growing Artificial Societies: Social Science from the Bottom Up (MIT Press) with J.M. Epstein, widely cited as an example of how to apply modern computing to the analysis of social and economic phenomena. For nearly 15 years he was Senior Fellow in Economic Studies, Foreign Policy Studies, and Governance Studies at the Brookings Institution (Washington, D.C.) where he helped found the Center on Social and Economic Dynamics (CSED). During this time he taught courses on his research as Mellon Distinguished Visiting Professor at Middlebury College (2004), Visiting Professor of Economics of the Graduate Faculty of Political and Social Science at the New School for Social Research (2004), Adjunct Professor of Computer Science at Georgetown University (2002) and Visiting Associate Professor of Economics at Johns Hopkins (1998-2000). Upon moving to Mason he helped found the Department of Computational Social Science in 2007, the first department of its kind in the world, and has served as Department Chair since 2008. During the 2013-14 academic year he was on sabbatical as Visiting Professor in the Complexity Economics Programme at Oxford University’s Mathematical Institute and Oxford Martin School, and visiting fellow of Hertford College. For many years he has been External Professor of the Santa Fe Institute in New Mexico. Most recently he has co-founded and is Co-Director of the Computational Public Policy Laboratory, a joint project of the Krasnow Institute for Advanced Study and the School of Policy, Government and International Affairs (SPGIA) at Mason.