Joshua Epstein

Joshua M. Epstein
Joshua Epstein
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Professor of Epidemiology

Professional overview

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.

Education

BA, Independent Scholar with Thesis in Political Economy, Amherst College, Amherst, MA
PhD, Political Science (Specialization: Security Studies, Communist Studies, and Economics), Massachusetts Institute of Technology, Cambridge, MA

Honors and awards

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)

Areas of research and study

Agent-Based Modeling
Applied Economics
Cost Analysis
Disaster Health
Epidemiology
Health Economics
Infectious Diseases
Mathematical and Computational Modeling
Modeling Social and Behavioral Dynamics
New York Department of Health and Mental Hygiene
Public Health Systems
Urban Health
Urban Informatics
Urban Science

Presentations

Agent Zero and Generative Social Science

Agent Zero and Integrative Economics

Publications

Publications

Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties

Cerdá, M., Hamilton, A. D., Hyder, A., Rutherford, C., Bobashev, G., Epstein, J. M., Hatna, E., Krawczyk, N., El-Bassel, N., Feaster, D. J., & Keyes, K. M. (n.d.).

Publication year

2024

Journal title

Epidemiology

Volume

35

Issue

3

Page(s)

418-429
Abstract
Abstract
Background: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. Methods: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. Results: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. Conclusions: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

Can Social Norms Explain Long-Term Trends in Alcohol Use? Insights from Inverse Generative Social Science

Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach

Gunaratne, C., Hatna, E., Epstein, J. M., & Garibay, I. (n.d.).

Publication year

2023

Journal title

JASSS

Volume

26

Issue

2
Abstract
Abstract
The 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 Future

Epstein, J. M. (n.d.).

Publication year

2023

Journal title

JASSS

Volume

26

Issue

2
Abstract
Abstract
The 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’ Statement

Epstein, J. M., Garibay, I., Hatna, E., Koehler, M., & Rand, W. (n.d.).

Publication year

2023

Journal title

JASSS

Volume

26

Issue

2
Abstract
Abstract
This 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 efficacy

A review and agenda for integrated disease models including social and behavioural factors

Bedson, 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.).

Publication year

2021

Journal title

Nature human behaviour

Volume

5

Issue

7

Page(s)

834-846
Abstract
Abstract
Social 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 model

Epstein, J. M., Hatna, E., & Crodelle, J. (n.d.).

Publication year

2021

Journal title

Journal of the Royal Society Interface

Volume

18

Issue

181
Abstract
Abstract
We 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 experiments

Cedeno-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.).

Publication year

2020

Journal title

PloS one

Volume

15

Issue

11
Abstract
Abstract
There 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 framework

Cedeno-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.).

Publication year

2020

Journal title

Social Network Analysis and Mining

Volume

10

Issue

1
Abstract
Abstract
Group 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 Model

Probst, C., Vu, T. M., Epstein, J. M., Nielsen, A. E., Buckley, C., Brennan, A., Rehm, J., & Purshouse, R. C. (n.d.).

Publication year

2020

Journal title

Health Education and Behavior

Volume

47

Issue

2

Page(s)

224-234
Abstract
Abstract
Background. 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 discussion

O’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–).

Publication year

2019

Page(s)

753-787
Abstract
Abstract
Contributors 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 science

Epstein, J. M. (n.d.). (1–).

Publication year

2018
Abstract
Abstract
These 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_Zero

Epstein, J., & Chelen, J. (n.d.). In A. Kirman & D. Sloan Wilson (Eds.), Complexity and Evolution: Toward a New Synthesis for Economics (1–).

Publication year

2016

Social conformity despite individual preferences for distinctiveness

Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science

Modeling the regional spread and control of vancomycin-resistant enterococci

Lee, 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.).

Publication year

2013

Journal title

American Journal of Infection Control

Volume

41

Issue

8

Page(s)

668-673
Abstract
Abstract
Background: 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 science

Epstein, J. M. (n.d.). In Generative Social Science (1–).

Publication year

2012

Page(s)

4-46

Coordination in transient social networks

Axtell, R. L., & Epstein, J. M. (n.d.). In Generative Social Science: An agent-based computational model of the timing of retirement (1–).

Publication year

2012

Page(s)

146-174

Generative social science: Studies in agent-based computational modeling

Growing adaptive organizations

Epstein, J. M. (n.d.). In Generative Social Science: An agent-based computational approach (1–).

Publication year

2012

Page(s)

309-344

Learning to be thoughtless

Epstein, J. M. (n.d.). In Generative Social Science: Social norms and individual computation (1–).

Publication year

2012

Page(s)

228-244

Modeling civil violence

Epstein, J. M. (n.d.). In Generative Social Science: An agent-based computational approach (1–).

Publication year

2012

Page(s)

247-270

Non-explanatory equilibria

Epstein, J. M., & Hammond, R. A. (n.d.). In Generative Social Science: An extremely simple game with (mostly) unattainable fixed points (1–).

Publication year

2012

Page(s)

75-85

Population growth and collapse in a multiagent model of the kayenta anasazi in long house valley

Axtell, 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–).

Publication year

2012

Page(s)

117-129

Contact

joshua.epstein@nyu.edu 708 Broadway New York, NY, 10003