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
Coordination in transient social networks : An agent-based computational model of the timing of retirement
AbstractAxtell, R. L., & Epstein, J. (n.d.).Publication year
2012Page(s)
146-174Abstract~Coordination in Transient Social Networks: An Agent-Based Computational Model of the Timing of Retirement
AbstractEpstein, J., & Axtell, R. (n.d.). (H. Aaron, Ed.).Publication year
1999Page(s)
161-186Abstract~Coupled contagion dynamics of fear and disease : Mathematical and computational explorations
AbstractEpstein, J., 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.Data analysis and modeling pipelines for controlled networked social science experiments
AbstractCedeno-Mieles, V., Hu, Z., Ren, Y., Deng, X., Contractor, N., Ekanayake, S., Epstein, J., Goode, B. J., Korkmaz, G., Kuhlman, C. J., Machi, D., Macy, M., Marathe, M. V., Ramakrishnan, N., Saraf, P., & Self, N. (n.d.).Publication year
2020Journal title
PloS oneVolume
15Issue
11AbstractThere is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.Dynamic Analysis and the Conventional Balance in Europe
AbstractEpstein, J. (n.d.).Publication year
1988Journal title
International SecurityVolume
12Issue
4Page(s)
154-65Abstract~Economic cost and health care workforce effects of school closures in the U.S
AbstractLempel, H., Epstein, J., & Hammond, R. A. (n.d.).Publication year
2009Journal title
PLoS CurrentsIssue
OCTAbstractSchool 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.Exploring price-independent mechanisms in the obesity epidemic
AbstractHammond, R., & Epstein, J. (n.d.).Publication year
2007Journal title
Center on Social and Economic Dynamics Working PaperVolume
48Abstract~Generating Mixed Patterns of Residential Segregation : An Evolutionary Approach
AbstractGunaratne, C., Hatna, E., Epstein, J., & Garibay, I. (n.d.).Publication year
2023Journal title
JASSSVolume
26Issue
2AbstractThe Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. This work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.Generative modeling of human behavior and social interactions using abductive analysis
AbstractRen, Y., Cedeno-Mieles, V., Hu, Z., Deng, X., Adiga, A., Barrett, C., Ekanayake, S., Goode, B. J., Korkmaz, G., Kuhlman, C. J., Machi, D., Marathe, M. V., Ramakrishnan, N., Ravi, S. S., Sarat, P., Selt, N., Contractor, N., Epstein, J., & Macy, M. W. (n.d.). (A. Tagarelli, C. Reddy, & U. Brandes, Eds.).Publication year
2018Page(s)
413-420AbstractAbduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: Behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.Generative social science : Studies in agent-based computational modeling
AbstractEpstein, J. (n.d.). (2nd ed.).Publication year
2012AbstractAgent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.Generative Social Science andAgent_Zero: A Formal Alternative tothe Rational ActorComplexityScience HubViennaInvitedLecturerVienna,AustriaNov 3,2023
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~Generative Social Science: Studies in Agent-Based Computational Modeling
AbstractEpstein, J. (n.d.).Publication year
2006Abstract~Growing adaptive organizations : An agent-based computational approach
AbstractEpstein, J. (n.d.).Publication year
2012Page(s)
309-344Abstract~Growing Artificial Societies: Social Science from the Bottom Up
AbstractEpstein, J., & Axtell, R. L. (n.d.).Publication year
1996Abstract~Horizontal Escalation: Sour Notes of a Recurrent Theme
AbstractEpstein, J. (n.d.). (R. Art & K. Waltz, Eds.).Publication year
1983Page(s)
541-552Abstract~Individual-based Computational Modeling of Smallpox Epidemic Control Strategies
AbstractBurke, D. S., Epstein, J., 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."Interiew with Joshua Epstein, conducted by Romain PLassard and Francesco Sergie. Oral Histories of Economics. OECONOMICA. The Sorbonne, Paris
AbstractEpstein, J. (n.d.). In OECONOMICA.Publication year
2025Abstract~Inverse Generative Social Science : Backward to the Future
AbstractAbstractThe agent-based model is the principal scientific instrument of generative social science. Typically, we design completed agents—fully endowed with rules and parameters—to grow macroscopic target patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to growa target—the forward problem—we start with the macro-target and evolve microagents that generate it, stipulating only primitive agent-rule constituents and permissible combinators. Rather than specific agents as designed inputs, we are interested in agents—indeed, families of agents—as evolved outputs. This is the backward problem and tools from Evolutionary Computing can help us solve it. In this overarching essay of the current JASSS Special Section, Part 1 discusses the motivation for iGSS. Part 2 discusses its goals, as distinct from other approaches. Part 3 discusses how to do it concretely, previewing the five iGSS applications that follow. Part 4 discusses several foundational issues for agent-based modeling and economics. Part 5 proposes a central future application of iGSS: to evolve explicit formal alternatives to the Rational Actor, with Agent_Zero as one possible point of evolutionary departure. Conclusions and future research directions are offered in Part 6. Looking ‘backward to the future,’ I also include, as Appendices, a pair of 1992 memoranda to the then President of the Santa Fe Institute on the forward (growing artificial societies from the bottom up) and backward (iGSS) problems.Learning to be thoughtless : Social norms and individual computation
AbstractEpstein, J. (n.d.).Publication year
2012Page(s)
228-244Abstract~Learning to be thoughtless : Social norms and individual computation
AbstractEpstein, J. (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.Mechanistic and data-driven agent-based models to explain human behavior in online networked group anagram games
AbstractCedeno-Mieles, V., Hu, Z., Deng, X., Ren, Y., Adiga, A., Barrett, C., Ekanayake, S., Korkmaz, G., Kuhlman, C. J., Machi, D., Marathe, M. V., Ravi, S. S., Goode, B. J., Ramakrishnan, N., Saraf, P., Self, N., Contractor, N., Epstein, J., & Macy, M. W. (n.d.). (F. Spezzano, W. Chen, & X. Xiao, Eds.).Publication year
2019Page(s)
357-364AbstractIn anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.Mobilizing Ebola survivors to curb the epidemic
AbstractEpstein, J., Sauer, L. M., Chelen, J., Hatna, E., Parker, J., Rothman, R. E., & Rubinson, L. (n.d.).Publication year
2014Journal title
NatureVolume
516Issue
7531Page(s)
323-325Abstract~Modeling civil violence : An agent-based computational approach
AbstractEpstein, J. (n.d.).Publication year
2002Journal title
Proceedings of the National Academy of Sciences of the United States of AmericaVolume
99Issue
SUPPL. 3Page(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.Modeling civil violence : An agent-based computational approach
AbstractEpstein, J. (n.d.).Publication year
2012Page(s)
247-270Abstract~