Joshua Epstein
Joshua Epstein
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Professor of Epidemiology
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Professional overview
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Joshua Epstein is Professor of Epidemiology in the NYU School of Global Public Health, and founding Director of the NYU Agent-Based Modeling Laboratory, with affiliated appointments at The Courant Institute of Mathematical Sciences, and the College of Arts & Sciences. Prior to joining NYU, he was Professor of Emergency Medicine at Johns Hopkins, and Director of the Center for Advanced Modeling in the Social, Behavior, and Health Sciences, with Joint appointments in Economics, Applied Mathematics, International Health, and Biostatistics. Before that, he was Senior Fellow in Economic Studies at the Brookings Institution and Director of the Center on Social and Economic Dynamics. His research interest has been modeling complex social dynamics using mathematical and computational methods, notably the method of Agent-Based Modeling in which he is a recognized pioneer. For this transformative innovation, he was awarded the NIH Director’s Pioneer Award in 2008, an Honorary Doctorate of Science from Amherst College in 2010, and was elected to the Society of Sigma XI in 2018. He has applied this method to the study of infectious diseases (e.g., Ebola, pandemic influenza, and smallpox), vector-borne diseases (e.g., zika), urban disaster preparedness, contagious violence, the evolution of norms, economic dynamics, computational archaeology, and the emergence of social classes, among many other topics. His books include Nonlinear Dynamics, Mathematical Biology, and Social Science (Wiley 1997), Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton, 2006), Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science (Princeton, 2013), and with Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up (MIT, 1996). Dr. Epstein earned his BA from Amherst College and his Ph.D. from The Massachusetts Institute of Technology.
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Education
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BA, Independent Scholar with Thesis in Political Economy, Amherst College, Amherst, MAPhD, Political Science (Specialization: Security Studies, Communist Studies, and Economics), Massachusetts Institute of Technology, Cambridge, MA
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Honors and awards
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Honorary Doctorate of Science, Amherst College (2010)Director’s Pioneer Award, National Institutes of Health (2008)Rockefeller Foundation International Relations Fellowship (1984)Council on Foreign Relations International Affairs Fellowship (1983)Ford Foundation Dual Expertise Fellowship in Soviet/East European Area Studies and International Security/Arms Control (1981)Institute for the Study of World Politics Fellowship (1981)
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Areas of research and study
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Agent-Based ModelingApplied EconomicsCost AnalysisDisaster HealthEpidemiologyHealth EconomicsInfectious DiseasesMathematical and Computational ModelingModeling Social and Behavioral DynamicsNew York Department of Health and Mental HygienePublic Health SystemsUrban HealthUrban InformaticsUrban Science
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Presentations
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Publications
Publications
Modeling the regional spread and control of vancomycin-resistant enterococci
AbstractLee, B. Y., Yilmaz, S. L., Wong, K. F., Bartsch, S. M., Eubank, S., Song, Y., Avery, T. R., Christie, R., Brown, S. T., Epstein, J., Parker, J. I., & Huang, S. S. (n.d.).Publication year
2013Journal title
American Journal of Infection ControlVolume
41Issue
8Page(s)
668-673AbstractBackground: Because patients can remain colonized with vancomycin-resistant enterococci (VRE) for long periods of time, VRE may spread from one health care facility to another. Methods: Using the Regional Healthcare Ecosystem Analyst, an agent-based model of patient flow among all Orange County, California, hospitals and communities, we quantified the degree and speed at which changes in VRE colonization prevalence in a hospital may affect prevalence in other Orange County hospitals. Results: A sustained 10% increase in VRE colonization prevalence in any 1 hospital caused a 2.8% (none to 62%) average relative increase in VRE prevalence in all other hospitals. Effects took from 1.5 to >10 years to fully manifest. Larger hospitals tended to have greater affect on other hospitals. Conclusions: When monitoring and controlling VRE, decision makers may want to account for regional effects. Knowing a hospital's connections with other health care facilities via patient sharing can help determine which hospitals to include in a surveillance or control program.Modeling the spread of methicillin-resistant staphylococcus aureus (MRSA) outbreaks throughout the hospitals in Orange County, California
AbstractLee, B. Y., McGlone, S. M., Wong, K. F., Yilmaz, S. L., Avery, T. R., Song, Y., Christie, R., Eubank, S., Brown, S. T., Epstein, J., Parker, J. I., Burke, D. S., Platt, R., & Huang, S. S. (n.d.).Publication year
2011Journal title
Infection Control and Hospital EpidemiologyVolume
32Issue
6Page(s)
562-572AbstractBackground. Since hospitals in a region often share patients, an outbreak of methicillin-resistant Staphylococcus aureus (MRSA) infection in one hospital could affect other hospitals. methods. Using extensive data collected from Orange County (OC), California, we developed a detailed agent-based model to represent patient movement among all OC hospitals. Experiments simulated MRSA outbreaks in various wards, institutions, and regions. Sensitivity analysis varied lengths of stay, intraward transmission coefficients (β), MRSA loss rate, probability of patient transfer or readmission, and time to readmission. results. Each simulated outbreak eventually affected all of the hospitals in the network, with effects depending on the outbreak size and location. Increasing MRSA prevalence at a single hospital (from 5% to 15%) resulted in a 2.9% average increase in relative prevalence at all other hospitals (ranging from no effect to 46.4%). Single-hospital intensive care unit outbreaks (modeled increase from 5% to 15%) caused a 1.4% average relative increase in all other OC hospitals (ranging from no effect to 12.7%). conclusion. MRSA outbreaks may rarely be confined to a single hospital but instead may affect all of the hospitals in a region. This suggests that prevention and control strategies and policies should account for the interconnectedness of health care facilities.Modelli computazionali fondati su agenti e scienza sociale generativa
AbstractEpstein, J. (n.d.).Publication year
2000Journal title
Sistemi IntelligentiVolume
12Issue
2Page(s)
177-221Abstract~Modelling to contain pandemics
AbstractEpstein, J. (n.d.).Publication year
2009Journal title
NatureVolume
460Issue
7256Page(s)
687Abstract~Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction framework
AbstractCedeno-Mieles, V., Hu, Z., Ren, Y., Deng, X., Adiga, A., Barrett, C., Contractor, N., Ekanayake, S., Epstein, J., Goode, B. J., Korkmaz, G., Kuhlman, C. J., Machi, D., Macy, M. W., Marathe, M. V., Ramakrishnan, N., Ravi, S. S., Saraf, P., & Self, N. (n.d.).Publication year
2020Journal title
Social Network Analysis and MiningVolume
10Issue
1AbstractGroup or collective identity is an individual’s cognitive, moral, and emotional connection with a broader community, category, practice, or institution. There are many different contexts in which collective identity operates, and a host of application domains where collective identity is important. Collective identity is studied across myriad academic disciplines. Consequently, there is interest in understanding the collective identity formation process. In laboratory and other settings, collective identity is fostered through priming a group of human subjects. However, there have been no works in developing agent-based models for simulating collective identity formation processes. Our focus is understanding a game that is designed to produce collective identity within a group. To study this process, we build an online game platform; perform and analyze controlled laboratory experiments involving teams; build, exercise, and evaluate network-based agent-based models; and form and evaluate hypotheses about collective identity. We conduct these steps in multiple abductive iterations of experiments and modeling to improve our understanding of collective identity as this looping process unfolds. Our work serves as an exemplar of using abductive looping in the social sciences. Findings on collective identity include the observation that increased team performance in the game, resulting in increased monetary earnings for all players, did not produce a measured increase in collective identity among them.Non-explanatory equilibria : An extremely simple game with (mostly) unattainable fixed points
AbstractEpstein, J., & Hammond, R. A. (n.d.).Publication year
2012Page(s)
75-85Abstract~Non-explanatory equilibria : An extremely simple game with (mostly) unattainable fixed points
AbstractEpstein, J., & Hammond, R. A. (n.d.).Publication year
2002Journal title
ComplexityVolume
7Issue
4Page(s)
18-22AbstractEquilibrium analysis pervades mathematical social science. This paper calls into question the explanatory significance of equilibrium by offering an extremely simple game, most of whose equilibria are unattainable in principle from any of its initial conditions. Moreover, the number of computation steps required to reach those (few) equilibria that are attainable is shown to grow exponentially with the number of players—making long-run equilibrium a poor predictor of the game's observed state. The paper also poses a number of combinatorially challenging problems raised by the game.Nonlinear dynamics, mathematical biology, and social science
AbstractAbstractThese lectures develop simple models of complex social processes using nonlinear dynamics and mathematical biology. Dynamical analogies between seemingly disparate social and biological phenomena, revolutions and epidemics, arms races, and ecosystem dynamics, are revealed and exploited. Nonlinear Dynamics, Mathematical Biology, and Social Science invites social scientists to relax, in some cases abandon, the predominant assumption of perfectly informed utility maximization and explore social dynamics from such perspectives as epidemiology and predator-prey theory. The volume includes a concentrated course on nonlinear dynamical systems.On Conventional Deterrence in Europe: Questions of Soviet Confidence
AbstractEpstein, J. (n.d.).Publication year
1982Journal title
OrbisVolume
26Issue
1Page(s)
71-86Abstract~On the Mathematical Biology of Arms Races, Wars, and Revolutions
AbstractEpstein, J. (n.d.). (L. Nadel & D. L. Stein, Eds.).Publication year
1994Abstract~Panel discussion : Moving social-behavioral modeling forward
AbstractO'Mahony, A., Davis, P. K., Appling, S., Brashears, M. E., Briscoe, E., Carley, K. M., Epstein, J., Matthews, L. J., Pavlic, T. P., Rand, W., Reilly, S. N., Rouse, W. B., Swarup, S., Tolk, A., Vardavas, R., & Yilmaz, L. (n.d.).Publication year
2019Page(s)
753-787AbstractContributors offered suggestions to improve multi-scale modeling that focused mainly on getting model substance right. This chapter is an edited but not iterated recounting of responses to questions that deal with simulation and emergence, how to relate models at different levels of resolution, and how to assure more humanness in agents. Contributors differed on whether simulations can generate true emergence but differed also on what true means. In simulating human behavior, multi-scale investigations are often necessary because, e.g. not enough empirical data is available to establish the true causal relationships at a single level. In many cases social-behavioral problems are complex and volatile and the environmental volatility is such that by the time the training sets are developed they are no longer useful and that it is unlikely that all information can be known and processed - at least in the time available.Pipelines and their compositions for modeling and analysis of controlled online networked social science experiments
AbstractCedeno-Mieles, V., Hu, Z., Deng, X., Contractor, N., Ren, Y., Ekanayake, S., Goode, B. J., Kuhlman, C. J., Machi, D., Marathe, M. V., Mortveit, H. H., Ramakrishnan, N., Saraf, P., Self, N., Epstein, J., & Macy, M. W. (n.d.).Publication year
2019Page(s)
774-785AbstractThere has been significant growth in online social science experiments in order to understand behavior at-scale, with finer-grained data collection. Considerable work is required to perform data analytics for custom experiments. We also seek to perform repeated networked experiments and modeling in an iterative loop. In this work, we design and build four composable and extensible automated software pipelines for (1) data analytics; (2) model property inference; (3) model/simulation; and (4) results analysis and comparisons between experimental data and model predictions. To reason about experiments and models, we design a formal data model. Our data model is for scenarios where subjects can repeat actions (from a set) any number of times over the game duration. Because the types of interactions and action sets are flexible, this class of experiments is large. Two case studies, on collective identity and complex contagion, illustrate use of the system.Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley
AbstractAxtell, R. L., Epstein, J., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M. (n.d.).Publication year
2002Journal title
Proceedings of the National Academy of Sciences of the United States of AmericaVolume
99Issue
SUPPL. 3Page(s)
7275-7279AbstractLong House Valley in the Black Mesa area of northeastern Arizona (U.S.) was inhabited by the Kayenta Anasazi from about 1800 before Christ to about anno Domini 1300. These people were prehistoric ancestors of the modern Pueblo cultures of the Colorado Plateau. Paleoenvironmental research based on alluvial geomorphology, palynology, and dendroclimatology permits accurate quantitative reconstruction of annual fluctuations in potential agricultural production (kg of maize per hectare). The archaeological record of Anasazi farming groups from anno Domini 200-1300 provides information on a millennium of sociocultural stasis, variability, change, and adaptation. We report on a multiagent computational model of this society that closely reproduces the main features of its actual history, including population ebb and flow, changing spatial settlement patterns, and eventual rapid decline. The agents in the model are monoagriculturalists, who decide both where to situate their fields as well as the location of their settlements. Nutritional needs constrain fertility. Agent heterogeneity, difficult to model mathematically, is demonstrated to be crucial to the high fidelity of the model.Population growth and collapse in a multiagent model of the kayenta anasazi in long house valley
AbstractAxtell, R. L., Epstein, J., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M. (n.d.).Publication year
2012Page(s)
117-129Abstract~Privacy and contact tracing efficacy
AbstractBenthall, S., Hatna, E., Epstein, J., & Strandburg, K. J. (n.d.).Publication year
2022Journal title
Journal of the Royal Society InterfaceVolume
19Issue
194AbstractAs the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual's ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.Remarks on the foundations of agent-based generative social science
AbstractEpstein, J. (n.d.).Publication year
2012Page(s)
50-71Abstract~Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties
AbstractCerdá, M., Hamilton, A. D., Hyder, A., Rutherford, C., Bobashev, G., Epstein, J., Hatna, E., Krawczyk, N., El-Bassel, N., Feaster, D. J., & Keyes, K. M. (n.d.).Publication year
2024Journal title
EpidemiologyVolume
35Issue
3Page(s)
418-429AbstractBackground: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. Methods: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. Results: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. Conclusions: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.Social conformity despite individual preferences for distinctiveness
AbstractSmaldino, P. E., & Epstein, J. (n.d.).Publication year
2015Journal title
Royal Society Open ScienceVolume
2Issue
3AbstractWe demonstrate that individual behaviours directed at the attainment of distinctiveness can in fact produce complete social conformity. We thus offer an unexpected generative mechanism for this central social phenomenon. Specifically, we establish that agents who have fixed needs to be distinct and adapt their positions to achieve distinctiveness goals, can nevertheless self-organize to a limiting state of absolute conformity. This seemingly paradoxical result is deduced formally from a small number of natural assumptions and is then explored at length computationally. Interesting departures from this conformity equilibrium are also possible, including divergence in positions. The effect of extremist minorities on these dynamics is discussed. A simple extension is then introduced, which allows the model to generate and maintain social diversity, including multimodal distinctiveness distributions. The paper contributes formal definitions, analytical deductions and counterintuitive findings to the literature on individual distinctiveness and social conformity.Soviet Vulnerabilities in Iran and the RDF Deterrent
AbstractEpstein, J. (n.d.).Publication year
1981Journal title
International SecurityVolume
6Issue
2Page(s)
126-59Abstract~Special Section on "Inverse Generative Social Science" : Guest Editors’ Statement
AbstractEpstein, J., Garibay, I., Hatna, E., Koehler, M., & Rand, W. (n.d.).Publication year
2023Journal title
JASSSVolume
26Issue
2AbstractThis is a guest editors’ statement accompanying the publication of a special issue on "Inverse Generative Social Science", published in volume 26, issue 2, 2023 of JASSS-Journal of Artificial Societies and Social Simulation".Strategy and Force Planning: The Case of the Persian Gulf
AbstractEpstein, J. (n.d.).Publication year
1987Abstract~The 3:1 Rule, the Adaptive Dynamic Model, and the Future of Security Studies
AbstractEpstein, J. (n.d.).Publication year
1989Journal title
International SecurityVolume
13Issue
4Page(s)
90-127Abstract~The Adaptive Dynamic Model of War
AbstractEpstein, J. (n.d.). (L. Nadel & D. L. Stein, Eds.).Publication year
1994Abstract~