About

Hazhir Rahmandad is the Schussel Family Professor of Management Science and a Professor of System Dynamics at the MIT Sloan School of Management. His research demonstrates how complex organizational dynamics can lead to heterogeneity in organizational practices and outcomes. He has analyzed how organizations learn in the presence of delays between taking action and observing results, using empirical data and simulations to illustrate the resulting learning challenges. His strategy research explores the shape of organizational performance landscapes, pathways to align firm and worker outcomes in low-cost services, capability development tradeoffs under competition, and the erosion of organizational capabilities through adaptation traps. Additionally, he studies public health and policy issues, including the dynamics of cities, COVID-19, obesity, and depression. Hazhir contributes to expanding the dynamic modeling toolbox by advancing parameter estimation and validation methods for dynamic models. He holds a BS in industrial engineering from Sharif University of Technology and a PhD in management with a concentration in system dynamics from MIT. Before joining MIT Sloan in 2015, he was an Associate Professor of Industrial and Systems Engineering at Virginia Tech.

Research topics

• Medicine
• Computer Science
• Environmental health
• Geography
• Business
• Demography
• Virology
• Economics
• Telecommunications
• Mathematics education
• Biology
• Climatology
• Macroeconomics
• Environmental science
• Ecology
• Atmospheric sciences
• Psychology
• Industrial organization
• Marketing
• Meteorology
• Econometrics
• Mathematics
• Engineering
• Statistics

Selected publications

• Replication Data for: Population-Level Behavioral and Structural Drivers of COVID-19 Vaccine Uptake in the US

  Harvard Dataverse · 2026-05-19

  datasetOpen accessSenior author

  This webpage contains data and code for replication of "Population-Level Behavioral and Structural Drivers of COVID-19 Vaccine Uptake in the US"

  PublisherDOI

• Faster Uptake, Slower Let‐Down: Asymmetric Community Responses to Changing Risks During a Pandemic

  Risk Analysis · 2026-03-30

  articleOpen access

  From how public opinion responds to economic outcomes to how risk perception shapes social interactions during a pandemic, many important social processes involve the assimilation of information to form opinions and perceptions, which in turn guide individual and societal actions. While such perception delays are well recognized, empirically identifying them is nontrivial. We study this problem in the context of human responses to disease dynamics, mediated by public risk perception. Public risk perception changes through an information diffusion process with significant delays, where perception adjustment delay may differ depending on whether the risk is increasing or decreasing. Despite these complexities, most models assume either fixed-delay structures or exponential structures with symmetric delay periods. First, using synthetic data (where ground truth is known), we show that incorrect delay structures and the assumption of symmetric delay periods can lead to biased and misleading estimates. We then explore alternative approaches to identify more appropriate delay structures that can overcome these challenges. Second, we apply these asymmetric delay structures to state-level US COVID-19 disease and mobility data to demonstrate how estimates of public sensitivity to mortality depend on the assumed delay structure. Our results provide evidence that during the pandemic, human risk perception adjusted with asymmetric delays to changing risk: people perceived rising risks more quickly than they perceived declining risks.

  PublisherDOI

• Effectiveness of a Participatory Voice Intervention on Psychological Well-being among Warehouse Workers: Results from the Fulfillment Center Intervention Study

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Beyond Least Squares: Estimation of Dynamic Models With Alternative Likelihoods and Kalman Filtering

  System Dynamics Review · 2025-04-01

  articleOpen access

  ABSTRACT From business to healthcare and operations to strategy, grounding system dynamics models in data is indispensable for theory and practice. However, formal estimation is difficult due to incomplete data, model mis‐specification, process noise, and measurement error. This complexity has limited the quantity and quality of formal estimation. We argue that comparing generic and easy‐to‐apply estimation methods for common models is fruitful for identifying methods that work well for SD practitioners. Using the classical SEIR model, we compare standard least squares against maximum likelihood estimators including variance‐scaled Gaussian, log Gaussian, Poisson, and negative binomial estimators, and assess the value of (extended) Kalman filtering. Under different assumptions about data availability and noise, we find that least squares, log Gaussian, and scaled Gaussian likelihoods perform poorly in estimating confidence intervals. The negative binomial and Kalman filtering with variance scaling and auto‐correlated process noise are promising across different setups. Implications for modelers are discussed.

  PublisherOA PDFDOI

• Incorporating Deep Learning Into System Dynamics: Amortized Bayesian Inference for Scalable Likelihood‐Free Parameter Estimation

  System Dynamics Review · 2025-01-01 · 3 citations

  articleOpen access1st authorCorresponding

  ABSTRACT Estimating parameters and their credible intervals for complex system dynamics models is challenging but critical to continuous model improvement and reliable communication with an increasing fraction of audiences. The purpose of this study is to integrate Amortized Bayesian Inference (ABI) methods with system dynamics. Utilizing Neural Posterior Estimation (NPE), we train neural networks using synthetic data (pairs of ground truth parameters and outcome time series) to estimate parameters of system dynamics models. We apply this method to two example models: a simple Random Walk model and a moderately complex SEIRb model. We show that the trained neural networks can output the posterior for parameters instantly given new unseen time series data. Our analysis highlights the potential of ABI to facilitate a principled, scalable, and likelihood‐free inference workflow that enhance the integration of models of complex systems with data. Accompanying code streamlines application to diverse system dynamics models.

  PublisherDOI

• Can a Voice Channel Reduce Turnover? Evidence on Employee Voice and Exit from a Cluster-randomized Trial in U.S. Fulfillment Centers

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Identifying delayed human response to external risks: an econometric analysis of mobility change during a pandemic

  BMC Medical Research Methodology · 2025-10-29 · 1 citations

  articleOpen access

  BACKGROUND: Human behavioral responses to changes in risks are often delayed. Methods for estimating these delayed responses either rely on rigid assumptions about the delay distribution (e.g., Erlang distribution), producing a poor fit, or yield period-specific estimates (e.g., estimates from the Autoregressive Distributed Lag (ARDL) model) that are difficult to integrate into simulation models. We propose a hybrid ARDL-Erlang approach that yields an interpretable summary of behavioral responses suitable for incorporation into simulation models. METHOD: We apply the ARDL-Erlang approach to estimate the effect of COVID-19 deaths on mobility across US counties from October 2020 to July 2021. A standard panel autoregressive distributed lag (ARDL) model first estimates the effect of past deaths and past mobility on current mobility. The ARDL model is then transformed into an Infinite Distributed Lag (IDL) model consisting of only past deaths. The coefficients of the past deaths are aggregated into an overall effect and fit to an Erlang distribution, summarized by average delay length and shape parameter. RESULTS: Our results show that on the national level, a one-standard-deviation permanent increase in weekly deaths per 100,000 population (log-transformed) is associated with a 0.46-standard-deviation decrease in human mobility in the long run, where the delay distribution follows a first-order Erlang distribution, and the average delay length is about 3.2 weeks. However, there is much heterogeneity across states, with first- to third-order Erlang delays and 2 to 18 weeks of average delay providing a theoretically cogent summary of how mobility followed changes in deaths during the first year and a half of the pandemic. CONCLUSION: This study provides a novel approach to estimating delayed human responses to health risks using a hybrid ARDL-Erlang model. Our findings highlight significant variability in the impact and timing of responses across states, underscoring the need for tailored public health policies. This study can also serve as guidelines and an example for identifying delayed human behavior in other settings.

  PublisherOA PDFDOI

• Are On-Demand Platforms Winner-Take-All Markets?

  SSRN Electronic Journal · 2023-01-01

  preprintOpen accessSenior author
  PublisherDOI

• How Does Network Structure Impact Socially Reinforced Diffusion?

  Organization Science · 2023-02-17 · 7 citations

  articleSenior author

  How does network structure impact the speed and reach of social contagions? The current view holds that random links facilitate “simple” contagion, but when agents require multiple reinforcements for “complex” adoption, clustered networks are better conduits of social influence. We show that in complex contagion, even low probabilities of adoption upon a single contact would activate an exponential contagion process that tilts the balance in favor of random networks. On the other hand, underappreciated but critical to the race between random and clustered networks is how long agents engage with contagion. Switching back to prior practice and the inactivation of senders and especially receivers shorten the window of engagement for convincing distant contacts and weaken the reach of diffusion on random networks. We propose a simplified framework where clustering primarily enables contagion when repetition matters and receivers lose interest quickly; otherwise, diffusion, simple or complex, is faster on random networks than clustered ones. These mechanisms can inform designing social networks, structuring groups, and seeding of ideas and innovations at a time when the increasing inflow of content from various media limits actors’ engagement with each item, whereas expanding network size and connections speeds up diffusion through distant contacts. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2023.1658 .

  PublisherDOI

• Call for submissions to the 2024 <scp>ISDC</scp>

  System Dynamics Review · 2023-10-01

  articleOpen access1st authorCorresponding

  We invite you to join us in Bergen, Norway, for the 42nd International Conference of the System Dynamics Society on 4–8 August 2024. Whether you are new to the practice of system dynamics or already an expert, we welcome you to Bergen in 2024, where you may contribute your original work and learn from leaders in the field about the state of the art in system dynamics. Submissions are encouraged on all topics relating to the theory and practical application of system dynamics modeling. The theme of the 2024 International System Dynamics Conference is “bridging perspectives for new insights.” This theme highlights the need for building bridges across different fields and approaches to benefit everyone. In particular, the unique strengths of system dynamics in bringing an endogenous, feedback-based perspective on important real-world problems complements methodological advancements in neighboring fields, from network science and epidemiology to operations research and econometrics. By building on these synergies, system dynamics research will be stronger and more impactful. Together, these complementary approaches can enrich theoretical and practical conversations in social, medical, and engineering sciences, ultimately leading to more holistic, long-term, and sustainable policies and insights. We invite experts from different fields with an interest in connecting with system dynamics in order to understand dynamic problems to join us in this stimulating and innovative event. Through sharing perspectives, combining approaches, and fostering productive collaborations we can advance our understanding of complex systems. The 2024 conference program will include invited and contributed sessions. Special proposals for plenary or parallel sessions, panel discussions, roundtables, and other pre- or post-conference activities are encouraged. If you have ideas for sessions and workshops focused on practical issues, please contact us. Proposals should be sent to: [email protected] You can find more information here: https://systemdynamics.org/submission-instructions/#submission-types The hybrid conference will take place in Bergen, Norway, as well as virtually. The venue will be the Scandic Bergen City Hotel. A comfortable and well-equipped hotel at the heart of a beautiful city, it offers guests a delightful experience with all the necessary amenities. Bergen, known as the “Gateway to the Fjords,” is a picturesque coastal city surrounded by seven mountains. Rich in maritime history, Bergen boasts a vibrant cultural scene, with the historic Bryggen Wharf, a UNESCO World Heritage Site, as one of its prime attractions. The city's narrow cobblestone streets, colorful wooden houses, and vibrant fish market offer a unique blend of old-world charm and modern Scandinavian flair. We hope you can join us there! Program Chairs Hazhir Rahmandad is the Schussel Family Professor of Management and Associate Professor of System Dynamics at the MIT Sloan School of Management. Hazhir's research applies dynamic modeling to problems spanning strategy, healthcare, and system dynamics methodology. [email protected] Mohammad S. Jalali (MJ) is an Assistant Professor at Harvard Medical School and leads MJ Lab, which develops simulation models to address complex health problems. He works closely with decision and policy makers, with research support from the FDA, NIH, NSF, and the European Commission, among others. Susan Howick is a Professor and Vice-Dean (Academic) at Strathclyde Business School in Glasgow, UK. Susan's research has involved using system dynamics to model highly disrupted engineering and construction projects and problems within the healthcare and energy sectors. She is particularly interested in developing approaches to combining system dynamics with other modeling methods. Her research interests also include developing systems approaches for risk assessment and management.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21HL113680

  NIH · $419k · 2015

• Understanding the dynamics of online communities

  NSF · $149k · 2011–2014

Frequent coauthors

• Mohammad S. Jalali

  42 shared

• Navid Ghaffarzadegan

  Virginia Tech

  25 shared

• Tse Yang Lim

  Harvard University

  24 shared

• Ran Xu

  23 shared

• Nick Ruktanonchai

  16 shared

• Lauren M. Childs

  16 shared

• Omar Saucedo

  16 shared

• John D. Sterman

  11 shared

Labs

• System DynamicsPI

Awards & honors

• Jay Wright Forrester Award