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
"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~Agent_Zero and Evolutionary Economics
AbstractEpstein, J. (n.d.). (P. G. Dosi, Ed.).Publication year
2025Abstract~Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties
AbstractCerdá, M., Hamilton, A. D., Hyder, A., Rutherford, C., Bobashev, G., Epstein, J., Hatna, E., Krawczyk, N., El-Bassel, N., Feaster, D. J., & Keyes, K. M. (n.d.).Publication year
2024Journal title
EpidemiologyVolume
35Issue
3Page(s)
418-429AbstractBackground: 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.Agent_Zero InternationalConference onEvolutionaryEconomicsInvitedLecturerPisa, Italy Nov 6,2023
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~Agent_Zero InternationalConference onEvolutionaryEconomicsInvitedLecturerPisa, Italy Nov 6,2023
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~Can Social Norms Explain Long-Term Trends in Alcohol Use? Insights from Inverse Generative Social Science
AbstractVu, T. M., Buckley, C., Duro, J. A., Brennan, A., Epstein, J., & Purshouse, R. C. (n.d.).Publication year
2023Journal title
JASSSVolume
26Issue
2AbstractSocial 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.Civil Violence and EndogenousInequalityInstitut desSystemesComplexes,InvitedLecturerParis October2023
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~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 Social Science andAgent_Zero: A Formal Alternative tothe Rational ActorComplexityScience HubViennaInvitedLecturerVienna,AustriaNov 3,2023
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~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.Special Section on "Inverse Generative Social Science" : Guest Editors’ Statement
AbstractEpstein, J., Garibay, I., Hatna, E., Koehler, M., & Rand, W. (n.d.).Publication year
2023Journal title
JASSSVolume
26Issue
2AbstractThis 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".Toward Cognitive Epidemiology:An Agent_Zero ApproachEHESS(Ecole desHaute Etudesan SciencesSociales).School forAdvancedStudies in theSocialSciences,InvitedLecturerParis October202310Paris.Institute desSystemesComplex,Paris
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~“Agent-Based Modeling in PublicHealth: From Playground to Planet”may be viewed herehttps://www.inet.ox.ac.uk/events/agent-based-modelling-in-public-health-from-playground-to-planet-professor-joshua-m-epstein-new-york-university/The MartinSchool, OxfordUniversityInvited PublicLectureOxford Feb 6,2024
AbstractEpstein, J. (n.d.).Publication year
2023Abstract~At the Boundary of Law and Software : Toward Regulatory Design with Agent-Based Modeling
AbstractBenthall, S., Tschantz, M. C., Hatna, E., Epstein, J., & Strandburg, K. J. (n.d.).Publication year
2022Journal title
CEUR Workshop ProceedingsVolume
3182AbstractComputer systems that automate the making of decisions about people must be accountable to regulators. Such accountability requires looking at the operation of the software within an environment populated with people. We propose to use agent-based modeling (ABM) to model such environments for auditing and testing purposes. We explore our proposal by considering the use of ABM for the regulation of ad targeting to prevent housing discrimination.Privacy and contact tracing efficacy
AbstractBenthall, S., Hatna, E., Epstein, J., & Strandburg, K. J. (n.d.).Publication year
2022Journal title
Journal of the Royal Society InterfaceVolume
19Issue
194AbstractAs 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 factors
AbstractBedson, 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., Hébert-Dufresne, L., & Althouse, B. M. (n.d.).Publication year
2021Journal title
Nature human behaviourVolume
5Issue
7Page(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 model
AbstractEpstein, J., Hatna, E., & Crodelle, J. (n.d.).Publication year
2021Journal title
Journal of the Royal Society InterfaceVolume
18Issue
181AbstractWe 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
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.Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction framework
AbstractCedeno-Mieles, V., Hu, Z., Ren, Y., Deng, X., Adiga, A., Barrett, C., Contractor, N., Ekanayake, S., Epstein, J., 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
2020Journal title
Social Network Analysis and MiningVolume
10Issue
1AbstractGroup 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
AbstractProbst, C., Vu, T. M., Epstein, J., Nielsen, A. E., Buckley, C., Brennan, A., Rehm, J., & Purshouse, R. C. (n.d.).Publication year
2020Journal title
Health Education and BehaviorVolume
47Issue
2Page(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.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.Panel discussion : Moving social-behavioral modeling forward
AbstractO'Mahony, A., Davis, P. K., Appling, S., Brashears, M. E., Briscoe, E., Carley, K. M., Epstein, J., Matthews, L. J., Pavlic, T. P., Rand, W., Reilly, S. N., Rouse, W. B., Swarup, S., Tolk, A., Vardavas, R., & Yilmaz, L. (n.d.).Publication year
2019Page(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.Pipelines and their compositions for modeling and analysis of controlled online networked social science experiments
AbstractCedeno-Mieles, V., Hu, Z., Deng, X., Contractor, N., Ren, Y., Ekanayake, S., Goode, B. J., Kuhlman, C. J., Machi, D., Marathe, M. V., Mortveit, H. H., Ramakrishnan, N., Saraf, P., Self, N., Epstein, J., & Macy, M. W. (n.d.).Publication year
2019Page(s)
774-785AbstractThere has been significant growth in online social science experiments in order to understand behavior at-scale, with finer-grained data collection. Considerable work is required to perform data analytics for custom experiments. We also seek to perform repeated networked experiments and modeling in an iterative loop. In this work, we design and build four composable and extensible automated software pipelines for (1) data analytics; (2) model property inference; (3) model/simulation; and (4) results analysis and comparisons between experimental data and model predictions. To reason about experiments and models, we design a formal data model. Our data model is for scenarios where subjects can repeat actions (from a set) any number of times over the game duration. Because the types of interactions and action sets are flexible, this class of experiments is large. Two case studies, on collective identity and complex contagion, illustrate use of the system.Toward inverse generative social science using multi-objective genetic programming
AbstractVu, T. M., Brennan, A., Probst, C., Strong, M., Epstein, J., & Purshouse, R. C. (n.d.).Publication year
2019Volume
2019Page(s)
1356-1363AbstractGenerative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intraagent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method - based on multi-objective genetic programming - for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.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.