Joshua M. Epstein
Professor of Epidemiology
Dr. Joshua M. Epstein’s research focuses on modeling complex social, economic, and biological systems using agent-based computational models and nonlinear dynamic systems.
Dr. Epstein has extensive experience in mathematical and computational modeling of biomedical and social dynamics at all scales - from local to national to planetary. He pioneered the technique of agent-based computational modeling and has applied it to problems in social, behavioral, and biomedical science by modeling patterns of infectious diseases (e.g., Ebola, 2009 Swine Flu pandemic, smallpox, HIV), vector-borne diseases (e.g., Zika), urban disaster preparedness, contagious violence, the evolution of norms, economic dynamics, computational reconstruction of the ancient civilization of the Anasazi, and the emergence of social classes, among many other topics. To design evacuation and longer-term adaptation to climate change, he combined computational fluid dynamics (i.e. toxic plume dispersion) with human behavior to create a stunning 3D artificial Los Angeles. In response to Zika and in collaboration with colleagues and the New York Department of Health and Mental Hygiene, Dr. Epstein has developed an artificial New York City to be applied to urban health policy challenges. His work has had a profound influence on emerging infectious diseases, bioterrorism, and the nascent field of disaster health, which is being developed under Presidential Directive (I-ISPD-21).
Dr. Epstein is the Director of the Agent-Based Modeling Lab, which works with large-scale epidemic models and cognitively plausible agents in order to produce a transformative synthesis for global public health modeling through the generative social science approach. 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 holds multiple appointments within NYU, including affiliated faculty positions at the Courant Institute of Mathematical Sciences and the College of Arts and Science. In addition, he is an External Professor at the Santa Fe Institute. 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.
He has also authored and co-authored seminal books, including Nonlinear Dynamics, Mathematical Biology, and Social Science (Wiley 1997); Growing Artificial Societies: Social Science from the Bottom Up (MIT, 1996); Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton, 2006); and Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science (Princeton, 2013).
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
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)
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
Controlling pandemic flu: The value of international air travel restrictionsEpstein, J., Goedecke, D. M., Yu, F., Morris, R. J., Wagener, D. K., & Bobashev, G. V.
Journal titlePLoS One
Issue5Background. Planning for a possible influenza pandemic is an extremely high priority, as social and economic effects of an unmitigated pandemic would be devastating. Mathematical models can be used to explore different scenarios and provide insight into potential costs, benefits, and effectiveness of prevention and control strategies under consideration. Methods and Findings. A stochastic, equation-based epidemic model is used to study global transmission of pandemic flu, including the effects of travel restrictions and vaccination. Economic costs of intervention are also considered. The distribution of First Passage Times (FPT) to the United States and the numbers of infected persons in metropolitan areas worldwide are studied assuming various times and locations of the initial outbreak. International air travel restrictions alone provide a small delay in FPT to the U.S. When other containment measures are applied at the source in conjunction with travel restrictions, delays could be much longer. If in addition, control measures are instituted worldwide, there is a significant reduction in cases worldwide and specifically in the U.S. However, if travel restrictions are not combined with other measures, local epidemic severity may increase, because restriction-induced delays can push local outbreaks into high epidemic season. The per annum cost to the U.S. economy of international and major domestic air passenger travel restrictions is minimal: on the order of 0.8% of Gross National Product. Conclusions. International air travel restrictions may provide a small but important delay in the spread of a pandemic, especially if other disease control measures are implemented during the afforded time. However, if other measures are not instituted, delays may worsen regional epidemics by pushing the outbreak into high epidemic season. This important interaction between policy and seasonality is only evident with a global-scale model. Since the benefit of travel restrictions can be substantial while their costs are minimal, dismissal of travel restrictions as an aid in dealing with a global pandemic seems premature.
Chapter 34 Remarks on the Foundations of Agent-Based Generative Social ScienceEpstein, J.
Page(s)1585-1604This chapter treats a variety of epistemological issues surrounding generative explanation in the social sciences, and discusses the role of agent-based computational models in generative social science.
Generative Social Science: Studies in Agent-Based Computational ModelingEpstein, J.
Individual-based Computational Modeling of Smallpox Epidemic Control StrategiesBurke, D. S., Epstein, J., Cummings, D. A. T., Parker, J. I., Cline, K. C., Singa, R. M., & Chakravarty, S.
Journal titleAcademic Emergency Medicine
Page(s)1142-1149In response to concerns about possible bioterrorism, the authors developed an individual-based (or "agent-based") computational model of smallpox epidemic transmission and control. The model explicitly represents an "artificial society" of individual human beings, each implemented as a distinct object, or data structure in a computer program. These agents interact locally with one another in code-represented social units such as homes, workplaces, schools, and hospitals. Over many iterations, these microinteractions generate large-scale macroscopic phenomena of fundamental interest such as the course of an epidemic in space and time. Model variables (incubation periods, clinical disease expression, contagiousness, and physical mobility) were assigned following realistic values agreed on by an advisory group of experts on smallpox. Eight response scenarios were evaluated at two epidemic scales, one being an introduction of ten smallpox cases into a 6,000-person town and the other an introduction of 500 smallpox cases into a 50,000-person town. The modeling exercise showed that contact tracing and vaccination of household, workplace, and school contacts, along with prompt reactive vaccination of hospital workers and isolation of diagnosed cases, could contain smallpox at both epidemic scales examined.
Toward a Containment Strategy for Smallpox Bioterror: An Individual-Based Computational ApproachEpstein, J., Cummings, D., Chakravarty, S., Singa, R., & Burke, D.
The Evolution of Social Behavior in the Prehistoric American SouthwestGumerman, G. J., Swedlund, A. C., Dean, J. S., & Epstein, J.
Journal titleArtificial Life
Page(s)435-444Long House Valley, located in the Black Mesa area of northeastern Arizona (USA), was inhabited by the Kayenta Anasazi from circa 1800 B.C. to circa A.D. 1300. These people were prehistoric precursors of the modern Pueblo cultures of the Colorado Plateau. A rich paleoenvironmental record, based on alluvial geomorphology, palynology, and dendroclimatology, permits the accurate quantitative reconstruction of annual fluctuations in potential agricultural production (kg maize/hectare). The archaeological record of Anasazi farming groups from A.D. 200 to 1300 provides information on a millennium of sociocultural stasis, variability, change, and adaptation. We report on a multi-agent 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 and where to locate their settlements.
Modeling civil violence: An agent-based computational approachEpstein, J.
Journal titleProceedings of the National Academy of Sciences of the United States of America
Page(s)7243-7250This article presents an agent-based computational model of civil violence. Two variants of the civil violence model are presented. In the first a central authority seeks to suppress decentralized rebellion. In the second a central authority seeks to suppress communal violence between two warring ethnic groups.
Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House ValleyAxtell, R. L., Epstein, J., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M.
Journal titleProceedings of the National Academy of Sciences of the United States of America
Page(s)7275-7279Long 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.
Learning to be thoughtless: Social norms and individual computationEpstein, J.
Journal titleComputational Economics
Page(s)9-24This paper extends the literature on the evolution of norms with an agent-based model capturing a phenomenon that has been essentially ignored, namely that individual thought - or computing - is often inversely related to the strength of a social norm. Once a norm is entrenched, we conform thoughtlessly. In this model, agents learn how to behave (what norm to adopt), but - under a strategy I term Best Reply to Adaptive Sample Evidence - they also learn how much to think about how to behave. How much they are thinking affects how they behave, which - given how others behave - affects how much they think. In short, there is feedback between the social (inter-agent) and internal (intra-agent) dynamics. In addition, we generate the stylized facts regarding the spatio-temporal evolution of norms: local conformity, global diversity, and punctuated equilibria.
The Emergence of Economic Classes in an Agent-Based Bargaining ModelEpstein, J., Axtell, R., & Young, P.
Modelli computazionali fondati su agenti e scienza sociale generativaEpstein, J.
Journal titleSistemi Intelligenti
Understanding Anasazi Culture Change Through Agent-Based ModelingEpstein, J., Dean, J., Gumerman, G., & Axtell, R.
Agent-Based Computational Models and Generative Social ScienceEpstein, J.
Coordination in Transient Social Networks: An Agent-Based Computational Model of the Timing of RetirementEpstein, J., & Axtell, R.
Aligning Simulation Models: A Case Study and ResultsAxtell, R., Axelrod, R., Cohen, M., & Epstein, J.
Journal titleComputational and Mathematical Organization Theory
Growing Artificial Societies: Social Science from the Bottom UpEpstein, J., & Axtell, R. L.
Agent-Based Modeling: Understanding Our CreationsAxtell, R., & Epstein, J.
Journal titleThe Bulletin of the Santa Fe Institute
On the Mathematical Biology of Arms Races, Wars, and RevolutionsEpstein, J.
The Adaptive Dynamic Model of WarEpstein, J.
War With Iraq: What Price Victory?Epstein, J.
Journal titleBrookings Discussion Papers
Controlling the Greenhouse Effect: Five Global Regimes ComparedEpstein, J., & Gupta, R.
Conventional Force Reductions: A Dynamic AssessmentEpstein, J.
The 3:1 Rule, the Adaptive Dynamic Model, and the Future of Security StudiesEpstein, J.
Journal titleInternational Security
Dynamic Analysis and the Conventional Balance in EuropeEpstein, J.
Journal titleInternational Security
Strategy and Force Planning: The Case of the Persian GulfEpstein, J.