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
Remarks on the foundations of agent-based generative social science
Epstein, J. M. (n.d.). In Generative Social Science (1–).Publication year
2012Page(s)
50-71The emergence of classes in a multi-agent bargaining model
Axtell, R. L., Epstein, J. M., & Young, H. P. (n.d.). In Generative Social Science (1–).Publication year
2012Page(s)
177-195The evolution of social behavior in the prehistoric American southwest
Gumerman, G. J., Swedlund, A. C., Dean, J. S., & Epstein, J. M. (n.d.). In Generative Social Science (1–).Publication year
2012Page(s)
130-143Toward a containment strategy for smallpox bioterror
Epstein, J. M., Cummings, D. A., Chakravarty, S., Singha, R. M., & Burke, D. S. (n.d.). In Generative Social Science: An individual-based computational approach (1–).Publication year
2012Page(s)
277-306Understanding anasazi culture change through agent-based modeling
Dean, J. S., Gumerman, G. J., Epstein, J. M., Axtell, R. I., Swedlund, A. C., Parker, M. T., & Mccarroll, S. (n.d.). In Generative Social Science (1–).Publication year
2012Page(s)
90-116Zones of cooperation in demographic prisoner's dilemma
Epstein, J. M. (n.d.). In Generative Social Science (1–).Publication year
2012Page(s)
199-221A distributed platform for global-scale agent-based models of disease transmission
AbstractParker, J., & Epstein, J. M. (n.d.).Publication year
2011Journal title
ACM Transactions on Modeling and Computer SimulationVolume
22Issue
1AbstractThe Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication, synchronization, and memory usage are among the topics covered in detail. The memory usage discussion is Java specific. However, the communication and synchronization discussions apply broadly. We provide benchmarks illustrating the GSAM's speed and scalability.Combining computational fluid dynamics and agent-based modeling: A new approach to evacuation planning
AbstractEpstein, J. M., Pankajakshan, R., & Hammond, R. A. (n.d.).Publication year
2011Journal title
PloS oneVolume
6Issue
5AbstractWe introduce a novel hybrid of two fields-Computational Fluid Dynamics (CFD) and Agent-Based Modeling (ABM)-as a powerful new technique for urban evacuation planning. CFD is a predominant technique for modeling airborne transport of contaminants, while ABM is a powerful approach for modeling social dynamics in populations of adaptive individuals. The hybrid CFD-ABM method is capable of simulating how large, spatially-distributed populations might respond to a physically realistic contaminant plume. We demonstrate the overall feasibility of CFD-ABM evacuation design, using the case of a hypothetical aerosol release in Los Angeles to explore potential effectiveness of various policy regimes. We conclude by arguing that this new approach can be powerfully applied to arbitrary population centers, offering an unprecedented preparedness and catastrophic event response tool.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. M., 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.Economic cost and health care workforce effects of school closures in the U.S
AbstractLempel, H., Epstein, J. M., & Hammond, R. A. (n.d.).Publication year
2009Journal title
PLoS CurrentsAbstractSchool closure is an important component of U.S. pandemic flu mitigation strategy, but has important costs. We give estimates of both the direct economic and health care impacts for school closure durations of 2, 4, 6, and 12 weeks under a range of assumptions. We find that closing all schools in the U.S. for four weeks could cost between $10 and $47 billion dollars (0.1-0.3% of GDP) and lead to a reduction of 6% to 19% in key health care personnel.Coupled contagion dynamics of fear and disease: Mathematical and computational explorations
AbstractEpstein, J. M., Parker, J., Cummings, D., & Hammond, R. A. (n.d.).Publication year
2008Journal title
PloS oneVolume
3Issue
12AbstractBackground: In classical mathematical epidemiology, individuals do not adapt their contact behavior during epidemics. They do not endogenously engage, for example, in social distancing based on fear. Yet, adaptive behavior is well-documented in true epidemics. We explore the effect of including such behavior in models of epidemic dynamics. Methodology/Principal Findings: Using both nonlinear dynamical systems and agent-based computation, we model two interacting contagion processes: one of disease and one of fear of the disease. Individuals can "contract" fear through contact with individuals who are infected with the disease (the sick), infected with fear only (the scared), and infected with both fear and disease (the sick and scared). Scared individuals-whether sick or not-may remove themselves from circulation with some probability, which affects the contact dynamic, and thus the disease epidemic proper. If we allow individuals to recover from fear and return to circulation, the coupled dynamics become quite rich, and can include multiple waves of infection. We also study flight as a behavioral response. Conclusions/Significance: In a spatially extended setting, even relatively small levels of fear-inspired flight can have a dramatic impact on spatio-temporal epidemic dynamics. Self-isolation and spatial flight are only two of many possible actions that fear-infected individuals may take. Our main point is that behavioral adaptation of some sort must be considered.Why model?
AbstractEpstein, J. M. (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.Containing a large bioterrorist smallpox attack: a computer simulation approach
AbstractLongini, I. M., Elizabeth Halloran, M., Nizam, A., Yang, Y., Xu, S., Burke, D. S., Cummings, D. A., & Epstein, J. M. (n.d.).Publication year
2007Journal title
International Journal of Infectious DiseasesVolume
11Issue
2Page(s)
98-108AbstractBackground: A bioterrorist release of smallpox is a constant threat to the population of the USA and other countries. Design: A stochastic simulation model of the spread of smallpox due to a large bioterrorist attack in a structured population was constructed. Disease natural history parameter estimates, time lines of behavioral activities, and control scenarios were based on the literature and on the consensus opinion of a panel of smallpox experts. Results: The authors found that surveillance and containment, i.e., isolation of known cases and vaccination of their close contacts, would be sufficient to effectively contain a large intentional smallpox release. Given that surveillance and containment measures are in place, preemptive vaccination of hospital workers would further reduce the number of smallpox cases and deaths but would require large numbers of prevaccinations. High levels of reactive mass vaccination after the outbreak begins would further reduce smallpox cases and deaths to a minimum, but would require even larger numbers of vaccinations. Reactive closure of schools would have a minimal effect. Conclusion: A rapid and well-organized response to a bioterrorist attack would be necessary for effective surveillance and containment to control spread. Preemptive vaccination of hospital workers and reactive vaccination of the target population would further limit spread, but at a cost of many more vaccinated. This cost in resources and potential harm due to vaccination will have to be weighed against the potential benefits should an attack occur. Prevaccination of the general population is not necessary.Controlling pandemic flu: The value of international air travel restrictions
AbstractEpstein, J. M., Goedecke, D. M., Yu, F., Morris, R. J., Wagener, D. K., & Bobashev, G. V. (n.d.).Publication year
2007Journal title
PloS oneVolume
2Issue
5AbstractBackground. 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.Exploring price-independent mechanisms in the obesity epidemic
Hammond, R., & Epstein, J. (n.d.).Publication year
2007Journal title
Center on Social and Economic Dynamics Working PaperVolume
48Chapter 34 Remarks on the Foundations of Agent-Based Generative Social Science
AbstractEpstein, J. M. (n.d.). In L. Tesfatsion & K. Judd (Eds.), Handbook of Computational Economics (1–).Publication year
2006Page(s)
1585-1604AbstractThis 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 Modeling
Epstein, J. (n.d.). (1–).Publication year
2006Individual-based Computational Modeling of Smallpox Epidemic Control Strategies
AbstractBurke, D. S., Epstein, J. M., Cummings, D. A., Parker, J. I., Cline, K. C., Singa, R. M., & Chakravarty, S. (n.d.).Publication year
2006Journal title
Academic Emergency MedicineVolume
13Issue
11Page(s)
1142-1149AbstractIn 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 Approach
Epstein, J., Cummings, D., Chakravarty, S., Singa, R., & Burke, D. (n.d.). (1–).Publication year
2004The Evolution of Social Behavior in the Prehistoric American Southwest
AbstractGumerman, G. J., Swedlund, A. C., Dean, J. S., & Epstein, J. M. (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.Modeling civil violence: An agent-based computational approach
AbstractEpstein, J. M. (n.d.).Publication year
2002Journal title
Proceedings of the National Academy of Sciences of the United States of AmericaVolume
99Page(s)
7243-7250AbstractThis 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.Non-explanatory equilibria: An extremely simple game with (mostly) unattainable fixed points
AbstractEpstein, J. M., & 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.Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley
AbstractAxtell, R. L., Epstein, J. M., 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
99Page(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.Learning to be thoughtless: Social norms and individual computation
AbstractEpstein, J. M. (n.d.).Publication year
2001Journal title
Computational EconomicsVolume
18Issue
1Page(s)
9-24AbstractThis 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 Model
Epstein, J., Axtell, R., & Young, P. (n.d.). In S. Durlauf & P. Young (Eds.), Social Dynamics (1–).Publication year
2001Page(s)
191-212