Joshua M. Epstein

Joshua M. Epstein
Joshua M. Epstein

Professor of Epidemiology

Professional overview

Dr. Joshua M. Epstein’s research focuses on modeling complex social, economic, and biological systems using agent-based computational models and nonlinear dynamic systems.

Dr. Epstein has extensive experience in mathematical and computational modeling of biomedical and social dynamics at all scales - from local to national to planetary. He pioneered the technique of agent-based computational modeling and has applied it to problems in social, behavioral, and biomedical science by modeling economic dynamics, patterns of civil violence, the evolution of norms, the computational reconstruction of the ancient civilization of the Anasazi, and the epidemiology of the 2009 Swine Flu pandemic, smallpox, HIV, and Ebola. To design evacuation and longer-term adaptation to climate change, he combined computational fluid dynamics (i.e. toxic plume dispersion) with human behavior to create a stunning 3D artificial Los Angeles. In response to Zika and in collaboration with colleagues and the New York Department of Health and Mental Hygiene, Dr. Epstein has developed an artificial New York City to be applied to urban health policy challenges. His work has had a profound influence on emerging infectious diseases, bioterrorism, and the nascent field of disaster health, which is being developed under Presidential Directive (I-ISPD-21).

Dr. Epstein is the Director of the Agent-Based Modeling Lab, which works with large-scale epidemic models and cognitively plausible agents in order to produce a transformative synthesis for global public health modeling through the generative social science approach.

Previously, Dr. Epstein has worked at John Hopkins University, Princeton University, University of Pittsburgh, George Mason University, the Sante Fe Institute, and the Brookings Institution. He has also  authored and co-authored seminal books, including Growing Artificial Societies: Social Science from the Bottom Up; Generative Social Science: Studies in Agent-Based Computational Modeling; and Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science.

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

Publications

Advancing Agent_Zero

Epstein, J., & Chelen, J.

Publication year

2016

Social conformity despite individual preferences for distinctiveness

Smaldino, P.E., & Epstein, J.

Publication year

2015

Journal title

Royal Society Open Science

Volume

2
10.1098/rsos.140437
Abstract

We 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 Science

Epstein, J.

Publication year

2014
Abstract

The 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.

Mobilizing Ebola survivors to curb the epidemic

Epstein, J., Sauer, L.M., Chelen, J., Hatna, E., Parker, J., Rothman, R.E., & Rubinson, L.

Publication year

2014

Journal title

Nature

Volume

516

Page(s)

323-325
10.1038/516323a

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., … Huang, S.S.

Publication year

2013

Journal title

American Journal of Infection Control

Volume

41

Page(s)

668-673
10.1016/j.ajic.2013.01.013
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.

Publication year

2012

Page(s)

4-46

Coordination in transient social networks: An agent-based computational model of the timing of retirement

Axtell, R.L., & Epstein, J.

Publication year

2012

Page(s)

146-174

Generative social science: Studies in agent-based computational modeling

Epstein, J.

Publication year

2012
Abstract

Agent-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 organizations: An agent-based computational approach

Epstein, J.

Publication year

2012

Page(s)

309-344

Learning to be thoughtless: Social norms and individual computation

Epstein, J.

Publication year

2012

Page(s)

228-244

Modeling civil violence: An agent-based computational approach

Epstein, J.

Publication year

2012

Page(s)

247-270

Non-explanatory equilibria: An extremely simple game with (mostly) unattainable fixed points

Epstein, J., & Hammond, R.A.

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., Dean, J.S., Gumerman, G.J., Swedlund, A.C., Harburger, J., … Parker, M.

Publication year

2012

Page(s)

117-129

Remarks on the foundations of agent-based generative social science

Epstein, J.

Publication year

2012

Page(s)

50-71

The emergence of classes in a multi-agent bargaining model

Axtell, R.L., Epstein, J., & Young, H.P.

Publication year

2012

Page(s)

177-195

The evolution of social behavior in the prehistoric American southwest

Gumerman, G.J., Swedlund, A.C., Dean, J.S., & Epstein, J.

Publication year

2012

Page(s)

130-143

Toward a containment strategy for smallpox bioterror: An individual-based computational approach

Epstein, J., Cummings, D.A.T., Chakravarty, S., Singha, R.M., & Burke, D.S.

Publication year

2012

Page(s)

277-306

Understanding anasazi culture change through agent-based modeling

Dean, J.S., Gumerman, G.J., Epstein, J., Axtell, R.I., Swedlund, A.C., Parker, M.T., & Mccarroll, S.

Publication year

2012

Page(s)

90-116

Zones of cooperation in demographic prisoner's dilemma

Epstein, J.

Publication year

2012

Page(s)

199-221

A distributed platform for global-scale agent-based models of disease transmission

Parker, J., & Epstein, J.

Publication year

2011

Journal title

ACM Transactions on Modeling and Computer Simulation

Volume

22
10.1145/2043635.2043637
Abstract

The 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

Epstein, J., Pankajakshan, R., & Hammond, R.A.

Publication year

2011

Journal title

PLoS ONE

Volume

6
10.1371/journal.pone.0020139
Abstract

We 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

Lee, B.Y., McGlone, S.M., Wong, K.F., Yilmaz, S.L., Avery, T.R., Song, Y., … Huang, S.S.

Publication year

2011

Journal title

Infection Control and Hospital Epidemiology

Volume

32

Page(s)

562-572
10.1086/660014
Abstract

Background. 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

Lempel, H., Epstein, J., & Hammond, R.A.

Publication year

2009

Journal title

PLoS Currents
10.1371/currents.RRN1051
Abstract

School 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.

Modelling to contain pandemics

Epstein, J.

Publication year

2009

Journal title

Nature

Volume

460
10.1038/460687a

Coupled contagion dynamics of fear and disease: Mathematical and computational explorations

Epstein, J., Parker, J., Cummings, D., & Hammond, R.A.

Publication year

2008

Journal title

PLoS ONE

Volume

3
10.1371/journal.pone.0003955
Abstract

Background: 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.

Contact

je65@nyu.edu +1 (212) 992-3702 715/719 Broadway New York, NY 10003