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
The Calculus of Conventional War: Dynamic Analysis Without Lanchester Theory
AbstractEpstein, J. (n.d.).Publication year
1985Abstract~The emergence of classes in a multi-agent bargaining model
AbstractAxtell, R. L., Epstein, J., & Young, H. P. (n.d.).Publication year
2012Page(s)
177-195Abstract~The Emergence of Economic Classes in an Agent-Based Bargaining Model
AbstractEpstein, J., Axtell, R., & Young, P. (n.d.). (S. Durlauf & P. Young, Eds.).Publication year
2001Page(s)
191-212Abstract~The evolution of social behavior in the prehistoric American southwest
AbstractGumerman, G. J., Swedlund, A. C., Dean, J. S., & Epstein, J. (n.d.).Publication year
2012Page(s)
130-143Abstract~The Evolution of Social Behavior in the Prehistoric American Southwest
AbstractGumerman, G. J., Swedlund, A. C., Dean, J. S., & Epstein, J. (n.d.).Publication year
2003Journal title
Artificial LifeVolume
9Issue
4Page(s)
435-444AbstractLong 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.The Extended Calculus of Spencer Brown and Related Areas of Logic and Mathematics
AbstractEpstein, J. (n.d.).Publication year
1979Abstract~The Normative Underpinnings of Population-Level Alcohol Use : An Individual-Level Simulation Model
AbstractProbst, C., Vu, T. M., Epstein, J., Nielsen, A. E., Buckley, C., Brennan, A., Rehm, J., & Purshouse, R. C. (n.d.).Publication year
2020Journal title
Health Education and BehaviorVolume
47Issue
2Page(s)
224-234AbstractBackground. By defining what is “normal,” appropriate, expected, and unacceptable, social norms shape human behavior. However, the individual-level mechanisms through which social norms impact population-level trends in health-relevant behaviors are not well understood. Aims. To test the ability of social norms mechanisms to predict changes in population-level drinking patterns. Method. An individual-level model was developed to simulate dynamic normative mechanisms and behavioral rules underlying drinking behavior over time. The model encompassed descriptive and injunctive drinking norms and their impact on frequency and quantity of alcohol use. A microsynthesis initialized in 1979 was used as a demographically representative synthetic U.S. population. Three experiments were performed in order to test the modelled normative mechanisms. Results. Overall, the experiments showed limited influence of normative interventions on population-level alcohol use. An increase in the desire to drink led to the most meaningful changes in the population’s drinking behavior. The findings of the experiments underline the importance of autonomy, that is, the degree to which an individual is susceptible to normative influence. Conclusion. The model was able to predict theoretically plausible changes in drinking patterns at the population level through the impact of social mechanisms. Future applications of the model could be used to plan norms interventions pertaining to alcohol use as well as other health behaviors.Toward a containment strategy for smallpox bioterror : An individual-based computational approach
AbstractEpstein, J., Cummings, D. A., Chakravarty, S., Singha, R. M., & Burke, D. S. (n.d.).Publication year
2012Page(s)
277-306Abstract~Toward a Containment Strategy for Smallpox Bioterror: An Individual-Based Computational Approach
AbstractEpstein, J., Cummings, D., Chakravarty, S., Singa, R., & Burke, D. (n.d.).Publication year
2004Abstract~Toward Cognitive Epidemiology:An Agent_Zero ApproachEHESS(Ecole desHaute Etudesan SciencesSociales).School forAdvancedStudies in theSocialSciences,InvitedLecturerParis October202310Paris.Institute desSystemesComplex,Paris
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~Toward inverse generative social science using multi-objective genetic programming
AbstractVu, T. M., Brennan, A., Probst, C., Strong, M., Epstein, J., & Purshouse, R. C. (n.d.).Publication year
2019Volume
2019Page(s)
1356-1363AbstractGenerative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intraagent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method - based on multi-objective genetic programming - for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.Triple contagion : A two-fears epidemic model
AbstractEpstein, J., Hatna, E., & Crodelle, J. (n.d.).Publication year
2021Journal title
Journal of the Royal Society InterfaceVolume
18Issue
181AbstractWe present a differential equations model in which contagious disease transmission is affected by contagious fear of the disease and contagious fear of the control, in this case vaccine. The three contagions are coupled. The two fears evolve and interact in ways that shape distancing behaviour, vaccine uptake, and their relaxation. These behavioural dynamics in turn can amplify or suppress disease transmission, which feeds back to affect behaviour. The model reveals several coupled contagion mechanisms for multiple epidemic waves. Methodologically, the paper advances infectious disease modelling by including human behavioural adaptation, drawing on the neuroscience of fear learning, extinction and transmission.Understanding anasazi culture change through agent-based modeling
AbstractDean, J. S., Gumerman, G. J., Epstein, J., Axtell, R. I., Swedlund, A. C., Parker, M. T., & Mccarroll, S. (n.d.).Publication year
2012Page(s)
90-116Abstract~Understanding Anasazi Culture Change Through Agent-Based Modeling
AbstractEpstein, J., Dean, J., Gumerman, G., & Axtell, R. (n.d.). (G. J. Gumerman & T. Kohler, Eds.).Publication year
2000Page(s)
179-206Abstract~War With Iraq: What Price Victory?
AbstractEpstein, J. (n.d.).Publication year
1991Journal title
Brookings Discussion PapersAbstract~Why model?
AbstractEpstein, J. (n.d.).Publication year
2008Journal title
JASSSVolume
11Issue
4AbstractThis lecture treats some enduring misconceptions about modeling. One of these is that the goal is always prediction. The lecture distinguishes between explanation and prediction as modeling goals, and offers sixteen reasons other than prediction to build a model. It also challenges the common assumption that scientific theories arise from and 'summarize' data, when often, theories precede and guide data collection; without theory, in other words, it is not clear what data to collect. Among other things, it also argues that the modeling enterprise enforces habits of mind essential to freedom. It is based on the author's 2008 Bastille Day keynote address to the Second World Congress on Social Simulation, George Mason University, and earlier addresses at the Institute of Medicine, the University of Michigan, and the Santa Fe Institute.Zones of cooperation in demographic prisoner's dilemma
AbstractEpstein, J. (n.d.).Publication year
2012Page(s)
199-221Abstract~Zones of cooperation in demographic prisoner’s dilemma
AbstractEpstein, J. (n.d.).Publication year
1998Journal title
ComplexityVolume
4Issue
2Page(s)
36-48AbstractThe emergence of cooperation in prisoner’s dilemma (PD) games is generally assumed to require repeated play (and strategies such as Tit for Tat, involving memory of previous interactions) or features (“tags”) permitting cooperators and defectors to distinguish one another. In the demographic PD, neither assumption is made: Agents with finite vision move to random sites on a lattice and play a fixed culturally-inherited zero-memory strategy of cooperate (C) or defect (D) against neighbors. Agents are indistinguishable to one another—they are “tagless.” Positive payoffs accrue to agents playing C against C, or D against C. Negative payoffs accrue to agents playing C against D, or D against D. Payoffs accumulate. If accumulated payoffs exceed some threshold, agents clone offspring of the same strategy onto neighboring sites and continue play. If accumulated payoffs are negative, agents die and are removed. Spatial zones of cooperation emerge.“Agent-Based Modeling in PublicHealth: From Playground to Planet”may be viewed herehttps://www.inet.ox.ac.uk/events/agent-based-modelling-in-public-health-from-playground-to-planet-professor-joshua-m-epstein-new-york-university/The MartinSchool, OxfordUniversityInvited PublicLectureOxford Feb 6,2024
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~