Professor of Epidemiology
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.
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
Can Social Norms Explain Long-Term Trends in Alcohol Use? Insights from Inverse Generative Social ScienceVu, T. M., Buckley, C., Duro, J. A., Brennan, A., Epstein, J. M., & Purshouse, R. C. (n.d.).
Issue2AbstractSocial psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent’s behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e., habitual) processes. In order to pass the generative sufficiency test as an explanation of alcohol use, we argue that the theory should be able to explain notable patterns in alcohol use that exist in the population, e.g., the distinct differences in drinking prevalence and average quantities consumed by males and females. In this study, we further develop and apply inverse generative social science (iGSS) methods to an existing agent-based model of dual process theory of alcohol use. Using iGSS, implemented within a multi-objective grammar-based genetic program, we search through the space of model structures to identify whether a single parsimonious model can best explain both male and female drinking, or whether separate and more complex models are needed. Focusing on alcohol use trends in New York State, we identify an interpretable model structure that achieves high goodness-of-fit for both male and female drinking patterns simultaneously, and which also validates success-fully against reserved trend data. This structure offers a novel interpretation of the role of norms in formulating drinking intentions, but the structure’s theoretical validity is questioned by its suggestion that individuals with low autonomy would act against perceived descriptive norms. Improved evidence on the distribution of autonomy in the population is needed to understand whether this finding is substantive or is a modeling artefact.
Generating Mixed Patterns of Residential Segregation: An Evolutionary ApproachGunaratne, C., Hatna, E., Epstein, J. M., & Garibay, I. (n.d.).
Issue2AbstractThe 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.
Inverse Generative Social Science: Backward to the FutureEpstein, J. M. (n.d.).
Issue2AbstractThe 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.
Special Section on "Inverse Generative Social Science": Guest Editors’ StatementEpstein, J. M., Garibay, I., Hatna, E., Koehler, M., & Rand, W. (n.d.).
Issue2AbstractThis 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".
Privacy and contact tracing efficacyBenthall, S., Hatna, E., Epstein, J. M., & Strandburg, K. J. (n.d.).
Journal titleJournal of the Royal Society Interface
Issue194AbstractAs 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.
A review and agenda for integrated disease models including social and behavioural factorsBedson, J., Skrip, L. A., Pedi, D., Abramowitz, S., Carter, S., Jalloh, M. F., Funk, S., Gobat, N., Giles-Vernick, T., Chowell, G., De Almeida, J. R., Elessawi, R., Scarpino, S. V., Hammond, R. A., Briand, S., Epstein, J. M., Hébert-Dufresne, L., & Althouse, B. M. (n.d.).
Journal titleNature human behaviour
Page(s)834-846AbstractSocial and behavioural factors are critical to the emergence, spread and containment of human disease, and are key determinants of the course, duration and outcomes of disease outbreaks. Recent epidemics of Ebola in West Africa and coronavirus disease 2019 (COVID-19) globally have reinforced the importance of developing infectious disease models that better integrate social and behavioural dynamics and theories. Meanwhile, the growth in capacity, coordination and prioritization of social science research and of risk communication and community engagement (RCCE) practice within the current pandemic response provides an opportunity for collaboration among epidemiological modellers, social scientists and RCCE practitioners towards a mutually beneficial research and practice agenda. Here, we provide a review of the current modelling methodologies and describe the challenges and opportunities for integrating them with social science research and RCCE practice. Finally, we set out an agenda for advancing transdisciplinary collaboration for integrated disease modelling and for more robust policy and practice for reducing disease transmission.
Triple contagion: A two-fears epidemic modelEpstein, J. M., Hatna, E., & Crodelle, J. (n.d.).
Journal titleJournal of the Royal Society Interface
Issue181AbstractWe 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.
Data analysis and modeling pipelines for controlled networked social science experimentsCedeno-Mieles, V., Hu, Z., Ren, Y., Deng, X., Contractor, N., Ekanayake, S., Epstein, J. M., Goode, B. J., Korkmaz, G., Kuhlman, C. J., Machi, D., Macy, M., Marathe, M. V., Ramakrishnan, N., Saraf, P., & Self, N. (n.d.).
Journal titlePloS one
Issue11AbstractThere 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.
Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction frameworkCedeno-Mieles, V., Hu, Z., Ren, Y., Deng, X., Adiga, A., Barrett, C., Contractor, N., Ekanayake, S., Epstein, J. M., 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.).
Journal titleSocial Network Analysis and Mining
Issue1AbstractGroup 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.
The Normative Underpinnings of Population-Level Alcohol Use: An Individual-Level Simulation ModelProbst, C., Vu, T. M., Epstein, J. M., Nielsen, A. E., Buckley, C., Brennan, A., Rehm, J., & Purshouse, R. C. (n.d.).
Journal titleHealth Education and Behavior
Page(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.
Panel discussionO’Mahony, A., Davis, P. K., Appling, S., Brashears, M. E., Briscoe, E., Carley, K. M., Epstein, J. M., Matthews, L. J., Pavlic, T. P., Rand, W., Reilly, S. N., Rouse, W. B., Swarup, S., Tolk, A., Vardavas, R., & Yilmaz, L. (n.d.). In Social-Behavioral Modeling for Complex Systems: Moving social-behavioral modeling forward (1–).
Page(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.
Nonlinear dynamics, mathematical biology, and social scienceEpstein, J. M. (n.d.). (1–).
Publication year2018AbstractThese 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.
Advancing Agent_ZeroEpstein, J., & Chelen, J. (n.d.). In A. Kirman & D. Sloan Wilson (Eds.), Complexity and Evolution: Toward a New Synthesis for Economics (1–).
Social conformity despite individual preferences for distinctivenessSmaldino, P. E., & Epstein, J. M. (n.d.).
Journal titleRoyal Society Open Science
Issue3AbstractWe 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.
Agent_Zero: Toward Neurocognitive Foundations for Generative Social ScienceEpstein, J. M. (n.d.). (1–).
Volume9781400848256AbstractThe Final Volume of the Groundbreaking Trilogy on Agent-Based Modeling. In this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent_Zero. This software individual, or "agent," is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics. Epstein weaves a computational tapestry with threads from Plato, Hume, Darwin, Pavlov, Smith, Tolstoy, Marx, James, and Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Epstein's computer programs are provided in the book or on its Princeton University Press website, along with movies of his "computational parables." Agent_Zero is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community. For generative social science, Agent_Zero presents a groundbreaking vision and the tools to realize it.
Modeling the regional spread and control of vancomycin-resistant enterococciLee, 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. M., Parker, J. I., & Huang, S. S. (n.d.).
Journal titleAmerican Journal of Infection Control
Page(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.
Agent-based computational models and generative social scienceEpstein, J. M. (n.d.). In Generative Social Science (1–).
Coordination in transient social networksAxtell, R. L., & Epstein, J. M. (n.d.). In Generative Social Science: An agent-based computational model of the timing of retirement (1–).
Generative social science: Studies in agent-based computational modelingEpstein, J. M. (n.d.). (2nd ed., 1–).
Publication year2012AbstractAgent-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.
Growing adaptive organizationsEpstein, J. M. (n.d.). In Generative Social Science: An agent-based computational approach (1–).
Learning to be thoughtlessEpstein, J. M. (n.d.). In Generative Social Science: Social norms and individual computation (1–).
Modeling civil violenceEpstein, J. M. (n.d.). In Generative Social Science: An agent-based computational approach (1–).
Non-explanatory equilibriaEpstein, J. M., & Hammond, R. A. (n.d.). In Generative Social Science: An extremely simple game with (mostly) unattainable fixed points (1–).
Population growth and collapse in a multiagent model of the kayenta anasazi in long house valleyAxtell, 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.). In Generative Social Science (1–).
Remarks on the foundations of agent-based generative social scienceEpstein, J. M. (n.d.). In Generative Social Science (1–).