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Understanding the state of the COVID‐19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation‐based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society

Group model building (GMB) approaches have been shown to improve participants' understanding of complexity by shifting and aligning individuals' mental models of the interconnections within complex systems. However, reviews of GMB literature have identified knowledge gaps for assessing the efficacy of GMB activities. To address these gaps, these studies recommend assessing multiple cases, shifting from controlled to applied settings, and reporting on objective measures. We address each of these items by comparing the outputs of multiple community‐based GMB workshops to participants' mental models elicited through pre‐workshop interviews. Using purposive text analysis, we developed causal loop diagrams for comparison to a group workshop model. Through a quantitative analysis, we find that individuals convened in GMB workshops have greater alignment on factors, causal links, and feedback. We believe these contributions can help other GMB practitioners better assess the efficacy of their activities with more rigor and detail. © 2020 System Dynamics Society

We introduce the Lake Urmia Vignette (LUV) as a tool to assess individuals' understanding of complexity in socio‐environmental systems. LUV is based on a real‐world case and includes a short vignette describing an environmental catastrophe involving a lake. Over a few decades, significant issues have manifested themselves at the lake because of various social, political, economic, and environmental factors. We design a rubric for assessing responses to a prompt. A pilot test with a sample of 30 engineering graduate students is conducted. We compare responses to LUV with other measures. Our findings suggest that students' understanding of complexity is positively associated with their understanding of systems concepts such as feedback loops but not with other possible variables such as self‐reported systems thinking skills or systems‐related coursework. Based on the provided instructions, researchers can use LUV as a novel assessment tool to examine understanding of complexity in socio‐environmental systems. © 2020 System Dynamics Society

This article reports the first systematic literature review of system dynamics (SD) applications in health and medicine published between 1960 and 2018. We categorize SD contributions into three groups—disease‐related modeling, organizational modeling, and regional health modeling—and explore major trends and approaches. We then focus on disease‐related modeling and discuss (1) common structures underlying models of infectious and noninfectious diseases, (2) major findings and modeling insights, and (3) avenues for future modeling efforts. While application areas cover a wide range of contexts, a considerable level of quality variation is observable, particularly in regards to model documentation, use of data, and model validation. While these shortcomings are not specific to SD modeling—and other schools of modeling often suffer from similar problems—we invite the community to address the issues both as authors and reviewers. Our study provides a reference document for several exemplary SD models, which is especially useful for early career modelers. © 2020 System Dynamics Society

This article is based on my talk at the 2019 International System Dynamics Conference on the occasion of receiving the Jay. W. Forrester Award for the article, “Structural dominance analysis of large and stochastic models” (System Dynamics Review 2016, 32(1): 26–51). I summarize here the history of the research project that led to the award‐winning article. I present the evolution of the ideas in a non‐technical way that develops the intuition for how eigenvalue elasticity analysis works and highlights the power of its explanations. I discuss what I believe to be the main benefits of structural dominance analysis and address the major criticisms that have been raised against it, and I close by reflecting on why I believe the capability to formally establish links between structure and behavior will become more salient in a context that pushes for larger models and demands higher standards of evidence. © 2020 System Dynamics Society

In recent history, there has been an increasing concern about population aging and the associated increased economic burden in terms of high health-care expenses and pension payments. The need for decision support tools that can explore population dynamics has become a prominent issue. This study presents a comprehensive framework where one can scrutinize the key demographic drives of fertility (Total Fertility Rate/Age-Specific Fertility Rate) over macroeconomic indicators (technology, education, human capital) under exogenous mortality. The integrated model in this article is developed based on a reformulation of the unified growth theory. In the reformulated model, namely the "economy-demography model," the population age/sex structure is preserved, age-specific mortality is included, and fertility is measured in conventional demographic terms. The model is then presented in system dynamics framework, and its practical use is showcased with data obtained from the Turkish Statistical Institute.

This research project bridges system dynamics and behavioral economics by importing tools and concepts, such as feedback dynamics, into mainstream behavioral economics research. This project focuses on countervailing cognitive biases, a theory where two competing cognitive biases can interact or even cancel out each other. We apply this concept to a key problem in behavioral economics: the difficulty that people have in making optimal trade‐offs between spending and saving for retirement. The simple model introduced in this paper represents the first iteration in the effort, and provides a proof‐of‐concept model that reproduces theoretical optimal behaviors from behavioral economics. Our exploratory analysis then adds to the simulation two cognitive biases that partially cancel each other out in ways that we had not initially expected. In the next step, we modify our original system dynamics model as an example of how to replace an economic optimization calculation with a decision‐making heuristic involving information feedback. © 2019 System Dynamics Society

The biomass scenario model (BSM) is a dynamic model of the biomass‐to‐biofuels supply chain in the U.S.A., developed during a multi‐year analysis effort conducted by the National Renewable Energy Laboratory (NREL), under sponsorship from the United States Department of Energy (DOE) Bioenergy Technologies Office (BETO). The BSM project, which received the 2018 Applications Award by the International System Dynamics Society, has supported collaborative analyses, developed scenarios for industry development and facilitated stakeholder engagement. We summarize insights gained from the BSM project that may be useful to other large‐scale dynamic modeling efforts. We summarize the project focus, the analysis process, key outcomes and observations on successful execution of such a product. Key points include the value of a multidisciplinary team with clear roles, engagement of experts and stakeholders, and use and reuse of simple, modular structures. The overall effort suggests that these practices may aid long‐term, team‐focused, multi‐stakeholder modeling efforts. © 2019 System Dynamics Society

Systems archetypes are effective in communicating complex behaviour with relatively simple structures, across a wide range of topics. The “power dynamics” between different power holders are critically important in decision making when it comes to formulating and implementing policies. This topic was explored at a four‐day Australasian systems workshop run in New Zealand. A synthesis approach was combined with analytical procedures from system dynamics (SD). Building on Rahn's “Fear and Greed” political archetype, a conceptual “Power and Influence” political archetype was developed. This political archetype shows the impact of public support. It is used to analyse a crowdfunding story in New Zealand. A small SD concept model was subsequently constructed to test this story and evaluate alternative public support scenarios. A library of political archetypes and concept models would be an asset for the field of SD and provide a means of synthesising insights from case studies and social theory. © 2019 System Dynamics Society

Rigor and relevance are fundamental criteria for quality modeling of complex systems. This piece is a plea for more qualified practice of professional modeling. Suggestions are made for fostering and spreading professional competency of high-quality model-building. Best modeling practices and best testing practices ultimately are one. The prerequisites for such practices, conceptual and technical, can be trained. The article makes suggestions for education to achieve modeling excellence. This contribution part of a scientific discourse following John Sterman’s “System dynamics at sixty: the path forward”, published in: System Dynamics Review, Vol. 34(1-2):5-47.

Stock‐and‐flow simulation tools, which provide experiences of cause‐and‐effect relationships, are likely to be effective ways to overcome climate misconceptions. However, little is known about the ability of simulation tools to enable the transfer of system dynamics knowledge to subsequent tasks. The primary objective of this research was to use stronger transfer problems in simulation tools and to test the resulting transfer of knowledge. In an experiment, 120 participants were randomly assigned to problems that varied in the use of a stock‐and‐flow simulation tool, Dynamic Climate Change Simulator (DCCS); and, a subsequent paper‐and‐pencil Climate Stabilization (CS) task. Climate misconceptions reduced significantly in the following CS task across problems where the stock behavior was either identical or different between DCCS and CS tasks. Playing DCCS helped people to transfer their system dynamics knowledge to the CS task. We discuss the advantages of using stock‐and‐flow simulation tools for climate education and policy research. © 2019 System Dynamics Society

Across disciplines, there has been an increasing interest in combining different simulation methods. Team science provides a particularly challenging context because of the interplay across levels of analysis. For example, team performance is decisively influenced by accumulated individual attributes, the interactions among individuals and emergent team structures—each of which is affected by multiple feedback loops at different levels of analysis. To address these challenges, we compare the modeling methods of stock‐and‐flow models, agent‐based models and social network analysis to argue for the advantages of a hybrid approach to formal mathematical modeling in a team science context. We develop a proof‐of‐concept model, which combines aspects of all three methods, to investigate the effects of expertise, the patterns of members’ interactions and diversity‐based subgroups on team performance. Novel, important insights into team science theory result from this investigation, including, among others, the dynamic tradeoff between diversity and homogeneity on teams’ performance and the importance of the communication network structure in affecting that tradeoff. © 2019 System Dynamics Society

The purpose of this article is to report on improvements on the interpretation and insights emerging from dynamic decomposition weight analysis (DDWA). These improvements emerged from efforts to further automate and expand the eigenvalue elasticity analysis methods and resolve inconsistencies in assumptions made in published reports of DDWA usage. In addition to making available to the broad system dynamics community an improved toolset to perform eigenvalue elasticity analysis, in this paper we clarify the set of assumptions needed to obtain reliable results and develop a new framework to assess the impact of model parameters on the projections of behavior modes on stock behavior. We illustrate the use of these developments by updating a previously published model analysis. The paper concludes by summarizing our findings and their implications for the further development of structural dominance analysis. © 2018 System Dynamics Society

When demand exceeds supply, retailers hedge against shortages by placing multiple orders with multiple suppliers, exceeding customer demand and leading to excess capacity, excess inventory, low capacity utilization and financial losses. This paper provides a comprehensive causal loop diagram and a formal mathematical model of a subset of supplier–retailer relationships. We obtain closed‐form solutions when supplier capacity is fixed, and analyze simulation dynamics when it is variable. Sensitivity analyses provide a deeper understanding of parameters that contribute to demand bubbles and insight on improvement policies. For instance, the ability of the supplier to build capacity quickly can reduce bubble size. The time it takes retailers to perceive and react to supply availability is an important lever in controlling demand bubbles. When retailers learn of shortages with a delay, their reaction is also delayed, which stabilizes the supply chain. © 2018 System Dynamics Society

We reflect on our past seven years of collaboration to develop systems models of U.S. higher education and scientific workforce development. Based on three recent modeling examples, we offer a methodological proposition that many traditional Operations Research (OR) models can be improved by including feedback processes as is commonly done in system dynamics (SD) modeling. Such models, even if simple and approximate, can be powerful, insightful, easy to communicate, and effective. While these modeling examples may not follow conventional SD or OR modeling, they benefit from and contribute to both schools of modeling. We argue that to build such synergy, modeling teams should be willing to create models building on the strengths of each school of modeling.

The long-term management of global disease eradication initiatives involves numerous inherently dynamic processes, health and economic trade-offs, significant uncertainty and variability, rare events with big consequences, complex and inter-related decisions, and a requirement for cooperation among a large number of stakeholders. Over the course of more than 16 years of collaborative modeling efforts to support the Global Polio Eradication Initiative, we developed increasingly complex integrated system dynamics models that combined numerous analytical approaches, including differential equation-based modeling, risk and decision analysis, discrete-event and individual-based simulation, probabilistic uncertainty and sensitivity analysis, health economics, and optimization. We discuss the central role of systems thinking and system dynamics in the overall effort and the value of integrating different modeling approaches to appropriately address the trade-offs involved in some of the policy questions. We discuss practical challenges of integrating different analytical tools and we provide our perspective on the future of integrated modeling.

Despite knowledge of factors that enable effective system dynamics modeling and organizational change, real‐life application of these tools in community settings remains challenging and often produces mixed results. We undertook a two‐part evaluation of early community use of the ReThink Health Dynamics Model (RTH model). The RTH model is a realistic, but simplified, portrait of a regional health system that supports multisector planning and strategy design. We assessed the contextual characteristics and implementation processes that promoted or undermined effective engagement with the model in five pilot sites. These learnings were used to refine a community readiness framework (Elements Affecting Modeling Use) that was then used to select and design a sixth community engagement. We use the evaluation results to assess the value of this organizing framework to identify communities ready for engagement with validated system dynamics models. Enabling better community–model matches will accelerate model adoption and health system transformation. © 2018 The Authors System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society

Contains fulltext : 200968pub.pdf (Publisher’s version ) (Open Access) 11 februari 2019 9 p.

Small Island Developing States (SIDS) face tension between economic growth and environmental impact. Tourism fuels growth, but the resulting solid waste and other pollutants threaten the SIDS’ natural beauty, quality of life for residents, attractiveness to tourists, and economic success. We assess the tension between tourism‐driven economic growth and environmental degradation from a limits‐to‐growth perspective, developing a generic system dynamics model of the problem using 38 years of data from the Maldives to estimate parameters and Monte‐Carlo methods to assess the sensitivity of results to uncertainty. We contrast development paths for the next three decades under three sets of policies focusing on promoting growth, managing tourism demand–supply balance, and improving waste management. Findings are counterintuitive; policies focused on better waste management alone are self‐defeating, because they increase tourism, growth and waste generation, undermining attractiveness and growth later. Policies that limit tourism demand improve economic and environmental health. © 2018 System Dynamics Society

Complex systems exhibit dynamics across multiple timescales. Strong couplings across timescales can be important, but common practice has been to ignore the couplings. This paper describes my experience modeling electric power systems when clients argued for models including both fast and slow dynamics. Their goals were realism, transparency for policymakers and fast simulations to enable policy design and sensitivity analysis in live workshop settings. I describe different methods developed to meet these needs using five modeling studies conducted over three decades. The studies focused on important policy issues, which are described for context. I summarize the strengths and weaknesses of each method, with emphasis on their applicability beyond the power industry. The paper concludes with an endorsement of a coupled system with a stand‐alone model of fast dynamics to support the design of the long‐term model. The appendices provide details to help those interested in applying the methods. Copyright © 2018 System Dynamics Society

Variance‐based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance‐based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance‐based sensitivity analysis to the model allows us to test for non‐additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.

This paper proposes an interpretative framework for system dynamics models using concepts from Newtonian mechanics. By considering the second derivative form of a model, it is shown that Newton’s three laws of motion have their equivalent in system dynamics, with forces between stocks being determined using the loop impact method. The concepts of mass, inertia, momentum and friction are explored as to their usefulness in understanding model behaviour. The Newtonian framework is applied to two standard system dynamics models—inventory–workforce and economic long-wave—where their behaviour is analyzed using force dominance on the stocks. Results show improved intuitive understanding of system behaviour compared with existing dominance methods, particularly for models with exogenous effects, oscillations and many loops. The framework is commended for further exploration. Copyright © 2018 System Dynamics Society

We present a new strategy for the direct optimization of the values of policy functions. This approach is particularly well suited to model actors with a global perspective on the system and relies heavily on modern mathematical white-box optimization methods. We demonstrate our strategy on two classical models: market growth and World2. Each model is first transformed into an optimization problem by defining how the actor can influence the models’ dynamics and by choosing objective functions to measure improvements. To improve comparability between different runs, we also introduce a comparison measure for possible interventions. We solve the optimization problems, discuss the resulting policies and compare them to the existing results from the literature. In particular, we present a run of the World2 model which significantly improves the published “towards a global equilibrium” run with equal cost of intervention.

This paper describes my journey starting from when I entered the field of system dynamics at MIT, to being honored by receiving the lifetime achievement award from the System Dynamics Society. After I obtained my PhD with supervision from Professor Forrester, I made an important decision to move back to Iran while the country was in the midst of a revolution. I began my career in Iran as a teacher, as a consultant, and as an institutional builder in a very harsh and turbulent environment. This paper explains how I built credibility that allowed me to found influential institutions and graduate programs and attracted talented students to the field, some of whom are now prominent members of the System Dynamics Society. In concluding the paper, I also describe my areas of research and attempt some general observations for system dynamics. Copyright © 2018 System Dynamics Society

The development of conceptual models using causal loop diagrams and their variants is a key step in the system dynamics modeling process. This work seeks to explore to what extent such models can be inferred directly from system observations using computational methods. A novel echo state neural network-based methodology is proposed to automatically learn causal loop diagram-like structures directly from system observations. The proposed data-driven approach aims at complementing the conceptual model development process by providing modelers with several probable model structures that can be accepted readily or considered for refinement. Three measures, used in comparing mental models, are adopted to compute similarity between the learned and target model structures. Using three well-known system dynamics case studies, we show the effectiveness of the proposed method in learning close model structures directly from the system observations, generated by simulating the stock-and-flow models for these cases. Copyright © 2017 System Dynamics Society

Sensitivity analysis of system dynamics models is essentially about sensitivity of patterns of output behaviors to inputs, since system dynamics modeling is behavior pattern oriented. In this study, a regression-based procedure for pattern sensitivity analysis is developed, by defining behavior pattern measures such as equilibrium level, trend, inflection point, or oscillation amplitude. A unique feature of the procedure is that it takes into account the possibility of a model generating multiple behavior modes. This pattern-oriented procedure is next applied to the tipping point project management model and a generic supply line model. These test applications yield sensitivity results that are meaningful, and also consistent with previously available sensitivity information about the parameters of these models. Finally, our pattern sensitivity analysis is shown to be a useful and effective method also for oscillatory system dynamics models, an unsolved sensitivity problem previously in the literature. Copyright © 2017 System Dynamics Society

This paper details a semi-automated method that can calculate intervention thresholds—that is, the minimum required intervention sizes, over a given timeframe, that result in a desired change in a system's output behavior pattern. The method exploits key differences in atomic behavior profiles that exist between classifiable pre- and post-intervention behavior patterns. An automated process of systematic adjustment of the intervention variable, while monitoring the key difference, identifies the intervention thresholds. The results, in turn, can be studied and presented in intervention threshold graphs in combination with final runtime graphs. Overall, this method allows modelers to move beyond ad hoc experimentation and develop a better understanding of intervention dynamics. This article presents an application of the method to the well-known World 3 model, which helps demonstrate both the procedure and its benefits. © 2017 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society

Recent evidence suggests that using the analytic mode of thinking (System 2) can improve people's performance in stock–flow (SF) tasks. In this paper, we further investigate the effects by implementing several different interventions in two studies. First, we replicate a previous finding that answering analytical questions before the SF task approximately doubles the likelihood of answering the stock questions correctly. We also investigate effects of three other interventions that can potentially prime participants to use their System 2. Specifically, the first group is asked to justify their response to the SF task; the second group is warned about the difficulty of the SF task; and the third group is offered information about cognitive biases and the role of the analytic mode of thinking. We find that the second group showed a statistically significant improvement in their performance. We claim that there are simple interventions that can modestly improve people's response in SF tasks. Copyright © 2017 System Dynamics Society.

Global industrial metal markets have experienced a drastic price decline over the past years. In this paper we link the dynamics of raw material markets and commodity price fluctuations to a delayed adjustment of supply. Drawing on the classical cobweb theorem we show how the implementation of this theorem using system dynamics may yield a valuable explanation, not only for the recent price decline, but also for possible future price movements. Starting from a simple cobweb model of general industrial markets, we couple the price-adjusting mechanics to the global copper market and demonstrate how a simple market model can be merged with a physical material flow model. This model captures both market dynamics and technical aspects of raw material processing, recycling and substitution and adds an explanation for the widely accepted fact that the cost structure of the copper industry cannot explain current price levels. Finally, we compare the system dynamics forecasting model with a traditional econometric forecasting method and found the system dynamics model to be more intuitive and better suited to capture and convey the structural market fundamentals. Copyright © 2017 System Dynamics Society

This paper develops a model of consumer choice that demonstrates why some markets with increasing returns converge to a winner-take-all outcome while many others have a power law market share distribution with a “long tail” of small-share products. The model takes the standard winner-take-all model of increasing returns and adds a simple behavioral assumption: when faced with complex choices, decision makers first quickly eliminate many of the available options using a simple heuristic before selecting from the remaining feasible set. We examine the market-level consequences of this model using an agent-based simulation. Under a wide range of parameters the model produces a power law share distribution. But when consumers have very large feasible sets the market converges to a winner-take-all outcome, and when consumers have very small feasible sets the model produces an evenly split market. Copyright © 2017 System Dynamics Society

The use of microworld simulators in classrooms and in experimental research has grown rapidly in recent years. Advocates suggest microworlds enhance learning compared with traditional instructional materials alone. However, people often have difficulty understanding the dynamics of microworld simulations, and learning and performance on microworlds typically plateau far below even simple behavioral benchmarks. Research from organization psychology suggests that assigning specific goals improves task learning and performance. This paper reports the results of two laboratory studies that examine the effects of two types of goals and two levels of goal difficulty on performance outcomes in the widely used People Express Microworld. We find that challenging compared with moderate learning goals result in higher performance outcomes. In contrast, there is no difference in performance outcomes between challenging and moderate performance goals. The findings show that including challenging learning goals in microworld instructional materials in classrooms and in experimental research will increase performance. Copyright © 2017 System Dynamics Society

There is limited methodological guidance for estimating system dynamics (SD) models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation-based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error-generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights. Copyright © 2016 System Dynamics Society