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Selected on-line publications:
Sterman, John. What the Future May Bring (Book review of "2052: A Global Forecast for the Next Forty Years", by Jorgen Randers). MIT Sloan Management Review. 18 December 2012: 13-14.
Rahmandad, H. and John Sterman (2012). Reporting Guidelines for Simulation-based Research in Social Sciences. System Dynamics Review. DOI: 10.1002/sdr.1481.
Sterman, John, Thomas Fiddaman, Travis Franck, Andrew Jones, Stephanie McCauley, Philip Rice, Elizabeth Sawin, Lori Siegel (Forthcoming) Management Flight Simulators to Support Climate Negotiations. Environmental Modelling and Software.
Croson, R., Donohue, K., Katok, E. & Sterman, J. (Forthcoming) Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock. Production and Operations Management.
Sterman, J., T. Fiddaman, T. Franck, A. Jones, S. McCauley, P. Rice, E. Sawin, L. Siegel (2012). Climate Interactive: The C-ROADS Climate Policy Model. System Dynamics Review 28(3): 295-305.
STERMAN, J. (2012) Sustaining Sustainability: Creating a Systems Science in a Fragmented Academy and Polarized World. In M. Weinstein and R.E. Turner (eds), Sustainability Science: The Emerging Paradigm and the Urban Environment. Springer. 21-58.
STERMAN, J.D. (2011) Communicating Climate Change Risks in a Skeptical World. Climatic Change. DOI 10.1007/s10584-011-0189-3
LANE, D., J. STERMAN (2011) Jay Wright Forrester. In Profiles in Operations Research: Pioneers and Innovators, S. Gass and A. Assad (eds). New York, Springer: 363-386.
OLIVA, R. & STERMAN, J. (2010) Death Spirals and Virtuous Cycles: Human Resource Dynamics in Knowledge-Based Services. The Handbook of Service Science. P. Maglio, J. Spohrer & C. Kieliszewski. London, Springer: 321-358.
STERMAN, J.D. (2010). Does Formal System Dynamics Training Improve People's Understanding of Accumulation? System Dynamics Review 26(4), 313-334.
CRONIN, M. A., GONZALEZ, C. & STERMAN, J. D. (2009) Why Don't Well-Educated Adults Understand Accumulation? A Challenge to Researchers, Educators, and Citizens. Organizational Behavior and Human Decision Processes 108:(1): 116-130.
RAHMANDAD, H., REPENNING, N. & STERMAN, J. (2009) Effects of Feedback Delay on Learning. System Dynamics Review. 25(4), 309-338.
SAWIN, E., JONES, A., FIDDAMAN, T., SIEGEL, L., WRIGHT, D., FRANCK, T., BARKMAN, A., CUMMINGS, T., VON PETER, J., CORELL, R., STERMAN, J. (2009). Current Emissions Reductions in the Lead-up to COP-15 are Likely to be Insufficient to Stabilize Atmospheric CO2 Levels: Using C-ROADS – a Simple Computer Simulation of Climate Change – to Support Long-term Climate Policy Development. International Scientific Congress on Climate Change, Copenhagen, DK, 10 March 2009.
STERMAN, J.D. (2008) Risk Communication on Climate: Mental Models and Mass Balance. Science 322 (24 October): 532-533. Details
RAHMANDAD, H. & STERMAN, J. D. (2008) Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models. Management Science 54(5): 998-1014.
STRUBEN, J. & STERMAN, J. D. (2008) Transition Challenges for Alternative Fuel Vehicle and Transportation Systems. Environment & Planning B 35: 1070-1097.
STERMAN, J. D., HENDERSON, R., BEINHOCKER, E. D. & NEWMAN, L. I. (2007) Getting Big Too Fast: Strategic Dynamics with Increasing Returns and Bounded Rationality. Management Science, 53, 683-696.
STERMAN, J. D. & BOOTH SWEENEY, L. (2007) Understanding Public Complacency About Climate Change: Adults' Mental Models of Climate Change Violate Conservation of Matter. Climatic Change, 80, 213-238.
STERMAN, J. D. (2006) Learning from Evidence in a Complex World. American Journal of Public Health, 96, 505-514.
OLIVA, R., STERMAN, J. D. & GIESE, M. (2003) Limits to Growth in the New Economy: Exploring the "Get Big Fast" Strategy in e-commerce. System Dynamics Review, 19, 83-117.
FORD, D. & STERMAN, J. D. (2003) The Liar’s Club: Concealing Rework in Concurrent Development. Concurrent Engineering: Research and Applications, 11.
FORD, D. & STERMAN, J. D. (2003) Overcoming the 90% Syndrome: Iteration Management in Concurrent Development Projects.Concurrent Engineering: Research and Applications, 11, 211.220.
STERMAN, J. D. & BOOTH SWEENEY, L. (2002) Cloudy Skies: Assessing Public Understanding of Global Warming. System Dynamics Review, 18(2): 207-240.
STERMAN, J. D. (2002) All Models are Wrong: Reflections on Becoming a Systems Scientist. System Dynamics Review, 18, 501-531.
REPENNING, N. P. & STERMAN, J. D. (2002) Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly, 47, 265 - 295.
REPENNING, N. P. & STERMAN, J. D. (2001) Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement. California Management Review 43, 64-88.
OLIVA, R. & STERMAN, J. D. (2001) Cutting corners and working overtime: Quality erosion in the service industry. Management Science, 47, 894-914.
BOOTH SWEENEY, L. & STERMAN, J. D. (2000) Bathtub Dynamics: Initial Results of a Systems Thinking Inventory. System Dynamics Review, 16, 249-294.
WITTENBERG, J. & STERMAN, J. D. (1999) Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution. Organization Science, 10, 322-341.
KEATING, E. K., OLIVA, R., REPENNING, N. P., ROCKART, S. & STERMAN, J. D. (1999) Overcoming the Improvement Paradox. European Management Journal, 17, 120-134.
LANGLEY, P. A., PAICH, M. & STERMAN, J. D. (1998) Explaining Capacity Overshoot and Price War: Misperceptons of Feedback in Competitive Growth Markets. International System Dynamics Conference, Quebec.
KAMPMANN, C. & STERMAN, J. D. (1998) Feedback complexity, bounded rationality, and market dynamics. MIT Sloan School Of Management.
STERMAN, J. D., REPENNING, N. P. & KOFMAN, F. (1997) Unanticipated Side Effects of Successful Quality Programs: Exploring a Paradox of Organizational Improvement. Management Science, 43(4), 501-521.
REPENNING, N. P. & STERMAN, J. D. (1997) Getting Quality the Old-Fashioned Way: Self-Confirming Attributions in the Dynamics of Process Improvement. In SCOTT, R. & COLE, R. (Eds.) The Quality Movement and Organizational Theory. Newbury Park, CA, Sage.
FORD, D. & STERMAN, J. D. (1997) Dynamic Modeling of Product Development Processes. System Dynamics Review, 14, 31-68.
FORD, D. & STERMAN, J. D. (1997) Expert Knowledge Elicitation to Improve Mental and Formal Models. System Dynamics Review, 14, 309-340.
STERMAN, J. D. (1992) Teaching Takes Off: Flight Simulators for Management Education. OR/MS Today, 40-44.
STERMAN, J. D. (1992) System Dynamics Modeling for Project Management. MIT Sloan School of Management.
STERMAN, J. D. (1991) A Skeptic's Guide to Computer Models. IN RICHARDSON, G. P. (Ed.) Modelling for Management. Aldershot, UK, Dartmouth Publishing Company.
Reporting Guidelines for Simulation-based Research in Social Sciences.
H. Rahmandad, J. Sterman
Reproducibility of research is critical for the healthy growth and accumulation of reliable knowledge, and simulation-based research is no exception. However, studies show many simulation-based studies in the social sciences are not reproducible. Better standards for documenting simulation models and reporting results are needed to enhance the reproducibility of simulation-based research in the social sciences. We provide an initial set of Reporting Guidelines for Simulation-based Research (RGSR) in the social sciences, with a focus on common scenarios in system dynamics research. We discuss these guidelines separately for reporting models, reporting simulation experiments, and reporting optimization results. The guidelines are further divided into minimum and preferred requirements, distinguishing between factors that are indispensable for reproduction of research and those that enhance transparency. We also provide a few guidelines for improved visualization of research to reduce the costs of reproduction. Suggestions for enhancing the adoption of these standards are discussed at the end.
Cite as: Rahmandad, H. and John Sterman (2012). Reporting Guidelines for Simulation-based Research in Social Sciences. System Dynamics Review. DOI: 10.1002/sdr.1481.
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Management Flight Simulators to Support Climate Negotiations
(Forthcoming, Environmental Modelling and Software)
Sterman, John, Thomas Fiddaman, Travis Franck, Andrew Jones, Stephanie McCauley, Philip Rice, Elizabeth Sawin, Lori Siegel
Under the United Nations Framework Convention on Climate Change (UNFCCC) the nations of the world have pledged to limit warming to no more than 2°C above preindustrial levels. However, negotiators and policymakers lack the capability to assess the impact of greenhouse gas (GHG) emissions reduction proposals offered by the parties on warming and the climate. The climate is a complex dynamical system driven by multiple feedback processes, accumulations, time delays and nonlinearities, but research shows poor understanding of these processes is widespread, even among highly educated people with strong technical backgrounds. Existing climate models are opaque to policymakers and too slow to be effective either in the fast-paced context of policymaking or as learning environments to help improve people’s understanding of climate dynamics. Here we describe C-ROADS (Climate Rapid Overview And Decision Support), a transparent, intuitive policy simulation model that provides policymakers, negotiators, educators, businesses, the media, and the public with the ability to explore, for themselves, the likely consequences of GHG emissions policies. The model runs on an ordinary laptop in seconds, offers an intuitive interface and has been carefully grounded in the best available science. We describe the need for such tools, the structure of the model, and calibration to climate data and state of the art general circulation models. We also describe how C-ROADS is being used by officials and policymakers in key UNFCCC parties, including the United States, China and the United Nations.
Cite as: Sterman, John, Thomas Fiddaman, Travis Franck, Andrew Jones, Stephanie McCauley, Philip Rice, Elizabeth Sawin, Lori Siegel (Forthcoming) Management Flight Simulators to Support Climate Negotiations. Environmental Modelling and Software.
Croson, R., Donohue, K., Katok, E. & Sterman, J.
The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. Previous research attributes this phenomenon to both operational and behavioral causes. We report on a set of laboratory experiments with a serial supply chain, using the Beer Distribution Game. The experimental conditions eliminate all operational causes of the bullwhip effect. Nevertheless, we find that the bullwhip effect persists in this setting and offer one possible explanation based on coordination risk. Coordination risk exists when individuals’ decisions contribute to a collective outcome and the decision rules followed by each individual are not known with certainty, e.g., where managers cannot be sure how their supply chain partners will behave. We conjecture that the existence of coordination risk may contribute to bullwhip behavior. We test this conjecture by controlling for environment factors that lead to coordination risk and find these controls lead to significant reduction in order oscillations and amplification. Next, we investigate a managerial intervention to reduce the bullwhip effect, inspired by our conjecture that coordination risk contributes to bullwhip behavior. While the intervention, holding additional on-hand inventory, does not change the existence of coordination risk, we find that it reduces order oscillation and amplification by providing a buffer against the endogenous risk of coordination failure. We conclude that the magnitude of the bullwhip can be mitigated, but that its behavioral causes appear robust.
Cite as: Croson, R., Donohue, K., Katok, E. & Sterman, J. (Forthcoming) Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock. Production and Operations Management.
Sterman, J., T. Fiddaman, T. Franck, A. Jones, S. McCauley, P. Rice, E. Sawin, L. Siegel
Under the United Nations Framework Convention on Climate Change (UNFCCC) the nations of the world have pledged to limit warming to no more than 2°C above preindustrial levels. However, negotiators and policymakers lack the capability to assess the impact of greenhouse gas (GHG) emissions reduction proposals offered by the parties. Existing climate models are opaque to policymakers and too slow to be effective in the fast-paced context of policymaking or as learning environments to help improve people’s understanding of climate dynamics. Consequently, policymakers, educators, business and civic leaders, the media and the general public rely on their intuition to assess the likely impacts of emissions reduction proposals. However, research shows common mental models, even among experts, are highly unreliable when applied to understanding how proposals affect likely future climate. Here we describe C-ROADS (Climate-Rapid Overview And Decision Support), a transparent, intuitive policy simulation model that provides policymakers, negotiators, educators, businesses, the media, and the public with the ability to explore, for themselves, the likely consequences of GHG emissions policies. The model runs on an ordinary laptop in seconds, offers an intuitive interface and has been carefully grounded in the best available science. We describe the need for such tools, the structure of the model, calibration to historical data, and how C-ROADS is used by key UNFCCC parties, including the United States, China and the United Nations. The model and full documentation are available at http://climateinteractive.org.
Cite as: Sterman, J., T. Fiddaman, T. Franck, A. Jones, S. McCauley, P. Rice, E. Sawin, L. Siegel (2012). Climate Interactive: The C-ROADS Climate Policy Model. System Dynamics Review 28(3): 295-305.
(2012, Sustainability Science: The Emerging Paradigm and the Urban Environment)
From climate change, deforestation, and depletion of fossil fuels to overexploited fisheries, species extinction, and poisons in our food and water, our society is unsustainable and it is getting worse fast. Many advocate that overcoming these problems requires the development of systems thinking. We’ve long been told that the unsustainability of our society arises because we treat the world as unlimited and problems unconnected when we live on a finite “spaceship Earth” in which “there is no away” and “everything is connected to everything else.” The challenge lies in moving from slogans to specific tools and processes that help us understand complexity, design better policies, facilitate individual and organizational learning, and catalyze the technical, economic, social, political and personal changes we need to create a sustainable society. Here I outline a design for a systems science of sustainability that rises to this challenge. Where the dynamics of complex systems are conditioned by multiple feedbacks, time delays, accumulations and nonlinearities, our mental models generally ignore these elements of dynamic complexity; where the consequences of our actions spill out across time and space, across disciplinary and organizational boundaries, our universities, corporations, and governments are organized in silos that focus on the short term and fragment knowledge. I describe how sustainability research, teaching and engagement with the policy process can be organized to provide scientifically grounded, reliable knowledge that crosses disciplinary boundaries, that engages multiple stakeholders, that grapples with unavoidable issues of ethics, values and purpose, and that leads to action.
Cite as: STERMAN, J. (2012) Sustaining Sustainability: Creating a Systems Science in a Fragmented Academy and Polarized World. In M. Weinstein and R.E. Turner (eds),Sustainability Science: The Emerging Paradigm and the Urban Environment. Springer. 21-58.
The Intergovernmental Panel on Climate Change (IPCC) has been extraordinarily successful in the task of knowledge synthesis and risk assessment. However, the strong scientific consensus on the detection, attribution, and risks of climate change stands in stark contrast to widespread confusion, complacency and denial among policymakers and the public. Risk communication is now a major bottleneck preventing science from playing an appropriate role in climate policy. Here I argue that the ability of the IPCC to fulfill its mission can be enhanced through better understanding of the mental models of the audiences it seeks to reach, then altering the presentation and communication of results accordingly. Few policymakers are trained in science, and public understanding of basic facts about climate change is poor. But the problem is deeper. Our mental models lead to persistent errors and biases in complex dynamic systems like the climate and economy. Where the consequences of our actions spill out across space and time, our mental models have narrow boundaries and focus on the short term. Where the dynamics of complex systems are conditioned by multiple feedbacks, time delays, accumulations and nonlinearities, we have difficulty recognizing and understanding feedback processes, underestimate time delays, and do not understand basic principles of accumulation or how nonlinearities can create regime shifts. These problems arise not only among laypeople but also among highly educated elites with significant training in science. They arise not only in complex systems like the climate but also in familiar contexts such as filling a bathtub. Therefore they cannot be remedied merely by providing more information about the climate, but require different modes of communication, including experiential learning environments such as interactive simulations.
Cite as: Sterman, J.D. (2011) Communicating Climate Change Risks in a Skeptical World. Climatic Change. DOI 10.1007/s10584-011-0189-3
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R. Oliva & J. Sterman
While the productivity and quality of manufactured products steadily improve, service sector productivity lags and quality has fallen. Many service organizations fall into “death spirals” in which pressure to boost throughput and control costs leads to worker burnout and corner cutting, lowering service quality, raising costs while revenue falls, forcing still greater cuts in capacity and even lower quality. We present a formal model to explore the dynamics of service delivery and quality, focusing on the service quality death spiral and how it can be overcome. We use the system dynamics modeling method as it is well suited to dynamic environments in which human behavior interacts with the physics of an operation, and in which there are multiple feedbacks connecting servers, managers, customers, and other actors. Through simulations we demonstrate that major recurring problems in the service industry—erosion of service quality, high turnover, and low profitability—can be explained by the organization’s internal responses to work pressure. Although the reinforcing feedbacks can operate as virtuous as well as vicious cycles, the system is biased toward quality erosion by basic asymmetries and nonlinearities. We show how, with the right mix of policies, these same feedbacks can become virtuous cycles that lead to higher employee, customer satisfaction and additional resources to invest in still greater service quality improvement.
Cite as: Oliva, R. & Sterman, J. (2010). Death Spirals and Virtuous Cycles: Human Resource Dynamics in Knowledge-Based Services. The Handbook of Service Science. P. Maglio, J. Spohrer & C. Kieliszewski. London, Springer: 321-358.
Research shows widespread misunderstanding of stocks and flows, even among highly educated adults. People fail to grasp that any stock rises (falls) when the inflow exceeds (is less than) the outflow. Rather, people often use the correlation heuristic, concluding that a system’s output is positively correlated with its inputs. Although many argue that system dynamics training will help, evidence is scant. This paper reports an experiment with MIT graduate students to assess the impact of an introductory system dynamics course on intuitive understanding of accumulation. Results show large, statistically significant improvements in overall performance and a reduction in the prevalence of the correlation heuristic. Modest exposure to stocks and flows improves
understanding of accumulation, at least among these highly educated adults. However, a minority still show evidence of correlational reasoning. The discussion considers additional experiments to deepen our knowledge of the training required to develop people’s intuitive understanding of accumulation.
Cite as: Sterman, J.D. Does Formal System Dynamics Training Improve People's Understanding of Accumulation? System Dynamics Review. 26 (4), 313-334.
M. A. Cronin, C. Gonzalez and J. D. Sterman
Accumulation is a fundamental process in dynamic systems: inventory accumulates production less shipments; the national debt accumulates the federal deficit. Effective decision making in such systems requires an understanding of the relationship between stocks and the flows that alter them. However, highly educated people are often unable to infer the behavior of simple stock-flow systems. Poor performance has been ascribed to complex information displays, lack of contextual knowledge, the cognitive burden of calculation, or the inability to interpret graphs.
Here, we demonstrate that poor understanding of accumulation, termed stock-flow failure, is more fundamental. In a series of experiments we find that persistent poor performance is not attributable to an inability to interpret graphs, lack of contextual knowledge, motivation, or cognitive capacity. Rather, stock-flow failure is a robust phenomenon that appears to be a function of the mental models constructed and used when encountering a dynamic system. We show that many, including highly educated individuals with strong technical training, use what we term the “correlation heuristic”, erroneously assuming that the behavior of a stock matches the pattern of its flows. We discuss the origins of stock-flow failure and implications for management and education.
Cite as: M. A. Cronin, C. Gonzalez and J. D. Sterman (2009) Why Don't Well-Educated Adults Understand Accumulation? A Challenge to Researchers, Educators, and Citizens. Organizarional Behavior and Human Division Processes 108(1): 116-130.
H. Rahmandad, J. D. Sterman, N. P. Repenning
Understanding barriers to learning is central to understanding firm performance. This paper investigates the role of time delays between taking an action and observing the results in impeding learning. These delays, ubiquitous in real-world settings, can introduce important tradeoffs between long-term and short-term performance. Here we build four learning heuristics, with different levels of complexity and rationality, and analyze their performance in a simple resource allocation task. All four reliably converge to the optimal solution when there are no short delays, and when those delays are correctly assessed by the decision-maker. However, learning is slowed significantly when decision-makers err in assessing the length of delay. In many cases, the decision-maker finds optimal solution wandering in the action space or converging to a suboptimal allocation. Simulation analysis shows errors in estimates of delays can impede learnin significantly regardless of the organization's level of rationality. The proposed heuristics can be applied to a range of problems for modeling learning from experience in presence delays
Cite as: H. Rahmandad., J. D. Sterman, N. P. Repenning (Forthcoming) "Effects of Feedback Delay on Learning" System Dynamics Review.
Current Emissions Reductions in the Lead-up to COP-15 are Likely to be Insufficient to Stabilize Atmospheric CO2 Levels: Using C-ROADS – a Simple Computer Simulation of Climate Change – to Support Long-term Climate Policy Development
SAWIN, E., JONES, A., FIDDAMAN, T., SIEGEL, L., WRIGHT, D., FRANCK, T., BARKMAN, A., CUMMINGS, T., VON PETER, J., CORELL, R., STERMAN, J.
We report the creation and initial use of the Climate-Rapid Overview And Decision Support Simulator (C- ROADS) (1), a simple, fast, user-friendly simulation of climate change that conforms with accepted climate science while allowing decision makers to discover through interactive exploration the range of greenhouse gas emissions trajectories sufficient to achieve widely accepted goals for climate stabilization – such as stabilizing CO2 levels at or below 350–450 parts per million (ppm) or limiting temperature increase to no more than 2° Centigrade over pre-industrial temperatures. As an example of the utility of this computer simulation model for informing policy makers, other leaders, and the public about progress within the UNFCCC negotiations leading up to COP-15 (Copenhagen, Denmark, December 2009) we use C-ROADS to analyze the expected long-term impacts on the climate of
proposals currently being put forth by national and regional governments. Our results show that these proposals – even if fully implemented – would be far from sufficient to meet the goals of stabilizing atmospheric CO2 levels at or below 450 ppm (reaching ~ 730 ppm by 2100) or limiting warming to 2°C over pre-industrial temperatures (reaching ~4°C by 2100 (at the central estimate of climate sensitivity of the IPCC (2)).
Cite as: SAWIN, E., JONES, A., FIDDAMAN, T., SIEGEL, L., WRIGHT, D., FRANCK, T., BARKMAN, A., CUMMINGS, T., VON PETER, J., CORELL, R., STERMAN, J. (2009). Current Emissions Reductions in the Lead-up to COP-15 are Likely to be Insufficient to Stabilize Atmospheric CO2 Levels: Using C-ROADS – a Simple Computer Simulation of Climate Change – to Support Long-term Climate Policy Development. International Scientific Congress on Climate Change, Copenhagen, DK, 10 March 2009.
The strong scientific consensus on the causes and risks of climate change stands in stark contrast to widespread confusion and complacency among the public. This policy forum explores the reasons for the gap, reports experimental results with MIT graduate students showing poor understanding of the basic processes of accumulation governing the concentration of greenhouse gases in the atmosphere, and discusses the implications for climate research and policy.
Cite as: Sterman, John D. (2008) Risk Communication on Climate: Mental Models and Mass Balance. Science 322 (24 October): 532-533.
H. Rahmandad and J. D. Sterman
When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Where DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity in agent attributes and in the network of interactions among them. The costs and benefits of such disaggregation should guide the choice of model type. AB models may enhance realism but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Using contagious disease as an example, we contrast the dynamics of AB models with those of the analogous mean-field DE model. We examine agent heterogeneity and the impact of different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Surprisingly, in many conditions differences between the DE and AB dynamics are not statistically significant for key metrics relevant to public health, including diffusion speed, peak load on health services infrastructure and total disease burden. We discuss implications for the choice between AB and DE models, level of aggregation, and model boundary. The results apply beyond epidemiology: from innovation adoption to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion.
Cite as: H. Rahmandad and J. D. Sterman (2008) Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models. Management Science 54(5):998-1014.
J. Struben and J. D. Sterman
Automakers are now developing alternatives to internal combustion engines (ICE), including hydrogen fuel cells and ICE-electric hybrids. Adoption dynamics for alternative vehicles are complex due to the size and importance of the auto industry and vehicle installed base. Diffusion of alternative vehicles is both enabled and constrained by powerful positive feedbacks arising from scale and scope economies, R&D, learning by doing, driver experience, word of mouth, and complementary resources such as fueling infrastructure. We describe a dynamic model of the diffusion of and competition among alternative fuel vehicles, including coevolution of the fleet, technology, consumer behavior, and complementary resources. Here we focus on the generation of consumer awareness of alternatives through feedback from consumers’ experience, word of mouth and marketing, with a reduced form treatment of network effects and other positive feedbacks (which we treat in other papers). We demonstrate the existence of a critical threshold for sustained adoption of alternative technologies, and show how the threshold depends on economic and behavioral parameters. We show that word of mouth from those not driving an alternative vehicle is important in stimulating diffusion. Expanding the model boundary to include learning, technological spillovers and spatial coevolution of fueling infrastructure adds additional feedbacks that condition the diffusion of alternative vehicles. Results show scenarios for successful diffusion of alternative vehicles, but also suggest that marketing programs and subsidies for alternatives must remain in place for long periods for diffusion to become self-sustaining
Cite as: J. Struben and J. D. Sterman (2008) Transition Challenges for Alternative Fuel Vehicle and Transportation Systems. Environment and Planning B 35:1070-1097.
J. D. Sterman, R. Henderson, E. D. Beinhocker and L. I. Newman
Neoclassical models of strategic behavior have yielded many insights into competitive behavior, despite the fact that they often rely on a number of assumptions-including instantaneous market clearing and perfect foresight-that have been called into question by a broad range of research. Researchers generally argue that these assumptions are "good enough" to predict an industry's probable equilibria, and that disequilibrium adjustments and bounded rationality have limited competitive implications. Here we focus on the case of strategy in the presence of increasing returns to highlight how relaxing these two assumptions can lead to outcomes quite different from those predicted by standard neoclassical models. Prior research suggests that in the presence of increasing returns, tight appropriability and accommodating rivals, in some circumstances early entrants can achieve sustained competitive advantage by pursuing Get Big Fast (GBF) strategies: rapidly expanding capacity and cutting prices to gain market share advantage and exploit positive feedbacks faster than their rivals. Using a simulation of the duopoly case we show that when the industry moves slowly compared to capacity adjustment delays, boundedly rational firms find their way to the equilibria predicted by conventional models. However, when market dynamics are rapid relative to capacity adjustment, forecasting errors lead to excess capacity, overwhelming the advantage conferred by increasing returns. Our results highlight the risks of ignoring the role of disequilibrium dynamics and bounded rationality in shaping competitive outcomes, and demonstrate how both can be incorporated into strategic analysis to form a dynamic, behavioral game theory amenable to rigorous analysis.
Cite as: J. D. Sterman, R. Henderson, E. D. Beinhocker and L. I. Newman (2007) Getting Big Too Fast: Strategic Dynamics with Increasing Returns and Bounded Rationality. Management Science, 53, 683-696.
J. D. Sterman and L. Booth Sweeney
Public attitudes about climate change reveal a contradiction. Surveys show most Americans believe climate change poses serious risks but also that reductions in greenhouse gas (GHG) emissions sufficient to stabilize atmospheric GHG concentrations or net radiative forcing can be deferred until there is greater evidence that climate change is harmful. US policymakers likewise argue it is prudent to wait and see whether climate change will cause substantial economic harm before undertaking policies to reduce emissions. Such wait-and-see policies erroneously presume climate change can be reversed quickly should harm become evident, underestimating substantial delays in the climate's response to anthropogenic forcing. We report experiments with highly educated adults-graduate students at MIT-showing widespread misunderstanding of the fundamental stock and flow relationships, including mass balance principles, that lead to long response delays. GHG emissions are now about twice the rate of GHG removal from the atmosphere. GHG concentrations will therefore continue to rise even if emissions fall, stabilizing only when emissions equal removal. In contrast, results show most subjects believe atmospheric GHG concentrations can be stabilized while emissions into the atmosphere continuously exceed the removal of GHGs from it. These beliefs-analogous to arguing a bathtub filled faster than it drains will never overflow-support wait-and-see policies but violate conservation of matter. Low public support for mitigation policies may be based more on misconceptions of climate dynamics than high discount rates or uncertainty about the risks of harmful climate change.
Cite as: J. D. Sterman and L. Booth Sweeney (2007) Understanding Public Complacency About Climate Change: Adults' mental models of climate change violate conservation of matter. Climatic Change, 80, 213-238.
J. D. Sterman
Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended “side effects.” Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but selfconfirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies.
Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond.
Cite as: J. D. Sterman (2006) Learning from Evidence in a Complex World. American Journal of Public Health, 96, 505-514.
R. Oliva, J. D. Sterman and M. Giese
Many ebusinesses have pursued a 'get big fast' (GBF) strategy, pricing low and marketing heavily to build their user base, in the belief that there were significant sources of increasing returns favoring early entrants and large players. Until early 2000 the capital markets rewarded the GBF strategy, but since then market values have collapsed and scores of new economy firms have failed. The rise and fall of the dot coms is not merely a case of a speculative bubble. Many firms stumbled when they grew so rapidly that they were unable to fulfill orders or provide quality service. GBF proponents focus on the positive feedbacks that create increasing returns and favor aggressive firms, but have not paid adequate attention to the negative feedbacks that can limit growth, e.g., service quality erosion. The faster a firm grows, the stronger these negative feedbacks may be. We address these issues with a formal dynamic model of competition among online and click-and-mortar companies in B2C e-commerce. The model endogenously generates demand, market share, service quality, employee skill and retention, content creation, market valuation, and other key variables. The model is calibrated to the online book market and Amazon.com as a test case. We explore growth strategies for e-commerce firms and their sustainability under different scenarios for customer, competitor, and capital market behavior.
Cite as: R. Oliva, J. D. Sterman and M. Giese (2003) Limits to Growth in the New Economy: Exploring the "Get Big Fast" Strategy in e-commerce. System Dynamics Review, 19, 83-117.
D. Ford and J. D. Sterman
Successfully implementing concurrent development has proven difficult for many organizations. However, many theories addressing concurrent development treat either technical aspects of the development process (e.g., precedence relationships) or behavioral issues (e.g., creating effective cross-functional teams), but not their linkages. We argue that much of the complexity of concurrent development—and the implementation failures that plague many organizations—arises from interactions between the technical and behavioral dimensions. We use a dynamic project model that explicitly represents these interactions to investigate how a ‘‘Liar’s Club’’—concealing known rework requirements from managers and colleagues—can aggravate the ‘‘90% syndrome,’’ a common form of schedule failure, and disproportionately degrade schedule performance and project quality. We discuss the role of the incentives on and behavior of engineers and managers in concurrent development failure and explore policies to improve project performance.
Cite as: D. Ford and J. D. Sterman (2003) The Liar’s Club: Concealing Rework in Concurrent Development. Concurrent Engineering: Research and Applications, 11.
D. Ford and J. D. Sterman
Successfully implementing concurrent development to reduce cycle time has proven difficult due to unanticipated iterations. We develop a dynamic project model that explicitly models these interactions to investigate the causes of the ‘‘90% syndrome,’’ a common form of schedule failure in concurrent development. We find that increasing concurrence and common managerial responses to schedule pressure aggravate the syndrome and degrade schedule performance and project quality. We show how understanding of and policies to avoid the 90% syndrome require integration of the technical attributes of the project, the flows of information among participants, and the behavioral decisionmaking heuristics participants use to respond to unanticipated problems and perturbations
Cite as: D. Ford and J. D. Sterman (2003) Overcoming the 90% Syndrome: Iteration Management in Concurrent Development Projects. Concurrent Engineering: Research and Applications, 11.
J. D. Sterman and L. Booth Sweeney
Surveys show most Americans believe global warming is real. But many advocate delaying action until there is more evidence that warming is harmful. The stock and flow structure of the climate, however, means "wait and see" policies guarantee further warming. Atmospheric CO2 concentration is now higher than any time in the last 420,000 years, and growing faster than any time in the past 20,000 years. The high concentration of CO2 and other greenhouse gases (GHGs) generates significant radiative forcing that contributes to warming. To reduce radiative forcing and the human contribution to warming, GHG concentrations must fall. To reduce GHG concentrations, emissions must fall below the rate at which GHGs are removed from the atmosphere. Anthropogenic CO2 emissions are now roughly double the removal rate, and the removal rate is projected to fall as natural carbon sinks saturate. Emissions must therefore fall by more than half even to stabilize CO2 at present record levels. Such reductions greatly exceed the Kyoto targets, while the Bush administration's Clear Skies Initiative calls for continued emissions growth. Does the public understand these physical facts? We report experiments assessing people's intuitive understanding of climate change. We presented highly educated graduate students with descriptions of greenhouse warming drawn from the IPCC's nontechnical reports. Subjects were then asked to identify the likely response to various scenarios for CO2 emissions or concentrations. The tasks require no mathematics, only an understanding of stocks and flows and basic facts about climate change. Overall performance was poor. Subjects often select trajectories that violate conservation of matter. Many believe temperature responds immediately to changes in CO2 emissions or concentrations. Still more believe that stabilizing emissions near current rates would stabilize the climate, when in fact emissions would continue to exceed removal, increasing GHG concentrations and radiative forcing. Such beliefs support wait and see policies, but violate basic laws of physics. We discuss implications for education and public policy.
Cite as: J. D. Sterman and L. Booth Sweeney (2002) Cloudy Skies: Assessing Public Understanding of Global Warming. System Dynamics Review, 18(2): 207-240.
J. D. Sterman
Thoughtful leaders increasingly recognize that we are not only failing to solve the persistent problems we face, but are in fact causing them. System dynamics is designed to help avoid such policy resistance and identify high-leverage policies for sustained improvement. What does it take to be an effective systems thinker, and to teach system dynamics fruitfully? Understanding complex systems requires mastery of concepts such as feedback, stocks and flows, time delays, and nonlinearity. Research shows that these concepts are highly counterintuitive and poorly understood. It also shows how they can be taught and learned. Doing so requires the use of formal models and simulations to test our mental models and develop our intuition about complex systems. Yet, though essential, these concepts and tools are not sufficient. Becoming an effective systems thinker also requires the rigorous and disciplined use of scientific inquiry skills so that we can uncover our hidden assumptions and biases. It requires respect and empathy for others and other viewpoints. Most important, and most difficult to learn, systems thinking requires understanding that all models are wrong and humility about the limitations of our knowledge. Such humility is essential in creating an environment in which we can learn about the complex systems in which we are embedded and work effectively to create the world we truly desire.
Cite as: J. D. Sterman (2002) All Models are Wrong: Reflections on Becoming a Systems Scientist. System Dynamics Review, 18, 501-531.
N. P. Repenning and J. D. Sterman
To better understand the factors that support or inhibit internally-focused change, we report the results of an inductive study of one firm's attempt to improve two of its core business processes. Our data suggest that the critical determinants of success in efforts to learn and improve are the interactions between managers' attributions regarding the cause of poor organizational performance and the physical structure of the workplace, particularly delays between investing in improvement and recognizing the rewards. Building on this observation, we propose a dynamic model capturing the mutual evolution of those attributions, managers' and workers' actions, and the production technology. We use the model to show how managers' beliefs about those that work for them, workers' beliefs about those who manage them, and the physical structure of the environment can coevolve to yield an organization characterized by conflict, mistrust, and control structures that prevent useful change of any type.
Cite as: N. P. Repenning and J. D. Sterman (2002) Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly, 47, 265 - 295.
N. P. Repenning and J. D. Sterman
Today's managers face a paradox. On the one hand, the number of tools, techniques, and technologies available to improve operational performance is growing rapidly. On the other hand, despite the rapid proliferation of such innovations and the fact that they produce dramatic success in a few companies, most efforts to use them fail to produce significant results. To understand and resolve this paradox, in this paper we study the difficulties organizations face in implementing tools, processes, and techniques like TQM, lean production methods, computer-aided design and development tools, stage-gate product development processes, and improved customer service systems. Our research suggests that the inability of most organizations to reap the full benefit of these innovations has little to do with the specific technique. Instead, the problem has its roots in how the introduction of a new improvement effort interacts with the physical, economic, social and psychological structures in which implementation takes place. We present a simple framework to understand how these failures arise and then offer some strategies for overcoming the pathological behaviors that we identify.
Cite as: N. P. Repenning and J. D. Sterman (2001) Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement. California Management Review 43, 64-88.
R. Oliva and J. D. Sterman
The erosion of service quality throughout the economy is a frequent concern in the popular press. The American Customer Satisfaction Index for services fell in 1997 to 67.7, down nine percentage points from its 1994 value. We hypothesize that the characteristics of services Ð inseparability, intangibility, and labor intensity Ð interact with management practices to bias service providers to reduce the level of service they deliver, often locking entire industries into a vicious cycle of eroding service standards. To explore this proposition we developed a formal model that integrates the structural elements of service delivery. We used econometric estimation, interviews, observations, and archival data to calibrate the model for a consumer lending service center in a major UK bank. We find that temporary imbalances between service capacity and demand interact with decision rules for effort allocation, capacity management, overtime, and quality aspirations to yield permanent erosion of the service standards and loss of revenue. We explore policies to improve performance and implications for organizational design in the service sector.
Cite as: R. Oliva and J. D. Sterman (2001) Cutting corners and working overtime: Quality erosion in the service industry. Management Science, 47, 894-914.
L. Booth Sweeney and J. D. Sterman
In a world of accelerating change, educators, business leaders, environmentalists and scholars are calling for the development of systems thinking to improve our ability to take effective actions. Through courses in the K-12 grades, universities, business schools, and corporations, advocates seek to teach people to think systemically. These courses range from one-day workshops with no mathematics to graduate level courses stressing formal modeling. But how do people learn to think systemically? What skills are required? Does a particular type of academic background improve one’s ability to think systemically? What systems concepts are most readily understood? Which tend to be most difficult to grasp? We describe initial results from an assessment tool or systems thinking inventory. The inventory consists of brief tasks designed to assess particular systems thinking concepts such as feedback, delays, and stocks and flows. Initial findings indicate that subjects from an elite business school with essentially no prior exposure to system dynamics concepts have a poor level of understanding of stock and flow relationships and time delays. Performance did not vary systematically with prior education, age, national origin, or other demographic variables. We hope the inventory will eventually provide a means for testing the effectiveness of training and decision aids used to improve systems thinking skills. We discuss the implications of these initial results and explore steps for future research.
Cite as: L. Booth Sweeney and J. D. Sterman (2000) Bathtub Dynamics: Initial Results of a Systems Thinking Inventory. System Dynamics Review, 16, 249-294.
J. Wittenberg and J. D. Sterman
What is the relative importance of structural versus contextual forces in the birth and death of scientific theories? We describe a dynamic model of the birth, evolution, and death of scientific paradigms based on Kuhn's Structure of Scientific Revolutions. The model creates a simulated ecology of interacting paradigms in which the creation of new theories is stochastic and endogenous. The model captures the sociological dynamics of paradigms as they compete against one another for members. Puzzle solving and anomaly recognition are also endogenous. We specify various regression models to examine the role of intrinsic versus contextual factors in determining paradigm success. We find that situational factors attending the birth of a paradigm largely determine its probability of rising to dominance, while the intrinsic explanatory power of a paradigm is only weakly related to the likelihood of success. For those paradigms that do survive the emergence phase, greater explanatory power is significantly related to longevity. However, the relationship between a paradigm's 'strength' and the duration of normal science is also contingent on the competitive environment during the emergence phase. Analysis of the model shows the dynamics of competition and succession among paradigms to be conditioned by many positive feedback loops. These self-reinforcing processes amplify intrinsically unobservable micro-level perturbations in the environment - the local conditions of science, society, and self faced by the creators of a new theory - until they reach macroscopic significance. Such dynamics are the hallmark of self-organizing evolutionary systems.
We consider the implications of these results for the rise and fall of new ideas in contexts outside the natural sciences such as management fads.
Cite as: J. Wittenberg and J. D. Sterman (1999) Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution. Organization Science, 10.
E. K. Keating, R. Oliva, N. P. Repenning, S. Rockart and J. D. Sterman
Despite the demonstrated benefits of improvement programs such as total quality management and reengineering, most improvement programs end in failure. Companies have found it extremely difficult to sustain even initially successful process improvement programs. Even more puzzling, successful improvement programs sometimes worsen business performance, triggering layoffs, low morale, and the collapse of commitment to continuous improvement. We term this phenomenon the "Improvement Paradox." For the last four years, we have worked with a variety of firms to understand the paradox and design policies to overcome it. Our findings suggest that the inability to manage an improvement program as a dynamic process is the main determinant of program failure. Improvement programs are tightly coupled to other functions and processes in the firm, and to the firmÕs customers, suppliers, competitors and capital markets. Failure to account for the feedbacks among these tightly coupled activities leads to unanticipated and often harmful side effects. We describe these dynamics and offer some guidance for managers seeking to design sustainable process improvement programs.
Cite as: E. K. Keating, R. Oliva, N. P. Repenning, S. Rockart and J. D. Sterman (1999) Overcoming the Improvement Paradox. European Management Journal, 17, 120-134.
P. A. Langley, M. Paich and J. D. Sterman
Companies consistently get into trouble in rapid growth markets. Frequently they grow too fast, overshoot when the market saturates, then get into price wars and suffer huge losses due to low prices and excess capacity. The companies that grow most aggressively sometimes lose the most, contrary to the new conventional wisdom that you have to be the largest player to benefit from increasing returns and positive feedbacks that confer success to the successful. How can the prevalence and persistence of this dynamic be explained? Is it just bad luck or is there a systematic explanation. And how can firms do better? To explore these issues, we designed an experiment involving over 270 subjects (MBA and short course Executives). Subjects played the role of a management team for one firm in a simulated duopoly market, with a rapidly growing demand for the new product. As in the real world, market potential and the course of the product lifecycle were highly uncertain. Subjects made quarterly capacity, pricing and marketing decisions over a simulated ten year period. Performance was measured by cumulative net income. The results showed that subjects systematically made pricing decisions that were not only far from the "optimal" price, but were often in the opposite direction from the optimal change. Subject performance was very poor, compared to a benchmark performance computed using simple behavioral decision rules. Subjects did not substantially modify their policies under different market structures or different competitor strategies. Neither did they modify their policies over trials - little learning took place. The poor performance is explained in terms of flaws in the subjects' mental models - their "misperceptions of feedback". We close with discussion of implications for improved senior management strategies in new product markets.
Cite as: P. A. Langley, M. Paich and J. D. Sterman (1998) Explaining Capacity Overshoot and Price War: Misperceptons of Feedback in Competitive Growth Markets. MIT Sloan School of Management.
C. Kampmann and J. D. Sterman
In a set of experimental markets, we investigate how the dynamic structure of the market as well as its price-setting institutions affect market performance and stability. We compare the outcomes to two alternative hypotheses: The standard neoclassical assumption of optimality and rational expectations, and a behavioral hypothesis based on previous studies of humans in dynamic decision making tasks, where subjects frequently ignore critical elements of the feedback structure in which they operate. We consider the implications of such misperceptions for the process of market adjustment, as well as the ability of market forces and financial incentives to mitigate these effects.
We find that feedback structure has a strong effect on performance relative to optimal, as the markets showed large fluctuations in prices and quantities. The only condition where performance (but not the dynamics) are relatively unaffected is where computer-mediated automatic price-clearing eliminates cumulative imbalances. The observed behavior is consistent with individual misperceptions of feedback, thus demonstrating that markets moderate but do not eliminate the negative impact of misperceptions of feedback.
We analyze the decisions of individual subjects by fitting them to simple equations and then used the estimated equations in a dynamic simulation model of the complete market. The simulations reproduce the most salient features of the dynamic behavior, and variations between markets can be related to differences in certain parameter values of the decision functions that can be interpreted as reflecting the degree of misperceptions of feedback. In this manner, the analysis constitutes a link between observation of individual decision making behavior and the theory of aggregate market outcomes.
An examination of decision timing data and verbal protocols provide evidence that increasing complexity of the decision-making task leads subjects to simplify their task by ignoring certain aspects, particularly strategic interactions between firms, and revert to simpler, more reactive behavior. Moreover, subjects tend to attribute observed oscillations and other systematic behavior patterns to exogenous forces rather than their own interaction with the system. This latter phenomenon may have important implications for the ability of agents to learn over time to improve their behavior.
Cite as: C. Kampmann and J. D. Sterman (1998) Feedback complexity, bounded rationality, and market dynamics. MIT Sloan School of Management.
J. D. Sterman, N. P. Repenning and F. Kofman
Recent evidence suggests the connection between quality improvement and financial results may be weak. Consider the case of Analog Devices, Inc., a leading manufacturer of integrated circuits. Analog's TQM program was a dramatic success. Yield doubled, cycle time was cut in half, and product defects fell by a factor of ten. However, financial performance worsened. To explore the apparent paradox we develop a detailed simulation model of Analog, including operations, financial and cost accounting, product development, human resources, the competitive environment, and the financial markets. We used econometric estimation, interviews, observation, and archival data to specify and estimate the model. We find that improvement programs like TQM can present firms with a tradeoff between short and long run effects. In the long run TQM can increase productivity, raise quality, and lower costs. In the short run, these improvements can interact with prevailing accounting systems and organizational routines to create excess capacity, financial stress, and pressures for layoffs that undercut commitment to continuous improvement. We explore policies to integrate improvement programs with the dynamics of the firm as a whole to promote sustained improvement in financial as well as nonfinancial measures of performance.
Cite as: J. D. Sterman, N. P. Repenning and F. Kofman (1997) Unanticipated Side Effects of Successful Quality Programs: Exploring a Paradox of Organizational Improvement. Management Science, 43.
N. P. Repenning and J. D. Sterman
Managers, consultants, and scholars have increasingly begun to recognize the value of considering an organizationUs activities in terms of processes rather than functions. Process oriented improvement techniques such as Total Quality Management and Business Process Reengineering have proven to be powerful tools for improving the effectiveness of many organizations. However, while suggesting new and valuable improvement opportunities, process-focused improvement techniques often fail, many times despite initial success. Existing theory does not explain many of these failures in part because process improvement involves interactions among physical structures and decision making processes in the firm while existing frameworks tend to address one at the expense of the other. Operations research and management science focus on the physical aspects of process improvement while organization theorists focus on the behavioral side. In this paper the beginnings of an integrated, interdisciplinary theory are developed. Drawing on the results of two in-depth case studies of process improvement efforts within a major US corporation, we develop a model that integrates the basic physical structure of process improvement with established theories on human cognition, learning, and organizational behavior to explain the dynamics of process improvement efforts. We show how these interactions can lead to self-confirming attributions which can thwart improvement efforts. We consider implications for practitioners and future research.
Cite as: N. P. Repenning and J. D. Sterman (1997) Getting Quality the Old-Fashioned Way: Self-Confirming Attributions in the Dynamics of Process Improvement. MIT Sloan School of Management.
D. Ford and J. D. Sterman
Successful development projects are critical to success in many industries. To improve project performance managers must understand the dynamic concurrence relationships that constrain the sequencing of tasks as well as the effects of and interactions with resources (such as labor), project scope and targets (such as delivery dates). This paper describes a multiple phase project model which explicitly models process, resources, scope and targets. The model explicitly portrays iteration, four distinct development activities and available work constraints to describe development processes. The model is calibrated to a semiconductor chip development project. Impacts of the dynamics of development process structures on research and practice are discussed.
Cite as: D. Ford and J. D. Sterman (1997) Dynamic Modeling of Product Development Processes. MIT Sloan School of Management.
D. Ford and J. D. Sterman
Knowledge intensive processes are often driven and constrained by the mental models of experts acting as direct participants or managers. For example, product development is guided by expert knowledge including critical process relationships which are dynamic, biased by individual perspectives and goals, conditioned by experience, aggregate many system components and relationships and are often nonlinear. Descriptions of these relationships are not generally available from traditional data sources such as company records but are stored in the mental models of the process experts. Often the knowledge is not explicit but tacit, so it is difficult to describe, examine and use. Consequently, improvement of complex processes is plagued by false starts, failures, institutional and interpersonal conflict, and policy resistance. Formal modeling approaches such as system dynamics are often used to help improve system performance. However, modelers face great difficulties in eliciting and representing the knowledge of the experts in these systems so useful models can be developed. Increased clarity and specificity are required concerning the methods used to elicit expert knowledge for modeling. We propose, describe and illustrate an elicitation method which uses formal modeling and three description format transformations to help experts explicate their tacit knowledge. To illustrate the approach we describe the use of the method to elicit detailed process knowledge describing the development of a new semiconductor chip. The method improved model accuracy and credibility in the eyes of the participants and provided tools for development team mental model improvement. We evaluate our method to identify future research opportunities.
Cite as: D. Ford and J. D. Sterman (1997) Expert Knowledge Elicitation to Improve Mental and Formal Models. MIT Sloan School of Management.
J. D. Sterman
Cite as: J. D. Sterman (1992) Teaching Takes Off: Flight Simulators for Management Education. OR/MS Today, 40-44.
J. D. Sterman
Project management is at once one of the most important and most poorly understood areas of management. Delays and cost overruns are the rule rather than the exception in construction, defense, power generation, aerospace, product development, software, and other areas. Project management suffers from numerous problems of costing and scheduling. Cost overruns of 100 to 200% are common. Projects are often delayed to the point where the market conditions for which they were designed have changed. Many projects suffer from the "90% syndrome" in which a project is thought to be 90% complete for half the total time required. Project management is often counterintuitive. For example, software development often suffers from Brooks' Law, which states "adding resources to a late project makes it even later". Customer design changes are frequent, generating costly ripple effects which create delay and disruption throughout an entire organization. Projects often appear to be going smoothly until near the end, when errors made earlier are discovered, necessitating costly rework, expediting, overtime, hiring, schedule slippage, or reductions in project scope or quality. The consequences of these difficulties include poor profitability, loss of market share and reputation, increased turnover of management and work force, lower productivity, higher costs, and, all too frequently, divisive and costly litigation between customers and contractors over responsibility for overruns and delays.
This paper describes in brief the use of system dynamics modeling for management of large scale projects, including large scale engineering and construction projects. System dynamics has repeatedly been demonstrated to be an effective analytical tool in a wide variety of situations, both academic and practical, and is currently being used by a number of corporations, including Fortune 500 firms, both in the United States and worldwide. Many of the applications of system dynamics, in both academic research and consulting, involve the quantitative assessment of the costs and benefits of various programs, both retrospectively and prospectively. System dynamics models are widely used in project management, including large scale projects in shipbuilding, defense, aerospace, civil construction, and power plants.1 System dynamics models are widely used as well in management of software development.2 The models have been used to manage projects more effectively and to assess the magnitude and sources of cost and schedule overruns in the context of litigation. In addition to project management, system dynamics models are widely used in business strategy and policy assessment. For example, the US. Department of Energy has used system dynamics models of the domestic and international energy system to produce detailed forecasts and policy analysis of energy policies since 1978. Many electric utilities use system dynamics models to analyze policy options for capacity expansion, conservation, pricing, and regulatory changes.3 The following sections highlight the major issues regarding modeling of project dynamics and provide selected references to the academic and professional literature.
Cite as: J. D. Sterman (1992) System Dynamics Modeling for Project Management. MIT Sloan School of Management.
J. D. Sterman
Computer modeling of social and economic systems is only about three decades old. Yet in that time, computer models have been used to analyze everything from inventory management in corporations to the performance of national economies, from the optimal distribution of fire stations in New York City to the interplay of global population, resources, food, and pollution. Certain computer models, such as The Limits to Growth (Meadows et al. 1972), have been front page news. In the US, some have been the subject of numerous congressional hearings and have influenced the fate of legislation. Computer modeling has become an important industry, generating hundreds of millions of dollars of revenues annually. As computers have become faster, cheaper, and more widely available, computer models have become commonplace in forecasting and public policy analysis, especially in economics, energy and resources, demographics, and other crucial areas. As computers continue to proliferate, more and more policy debatesÑboth in government and the private sectorÑwill involve the results of models. Though not all of us are going to be model builders, we all are becoming model consumers, regardless of whether we know it (or like it). The ability to understand and evaluate computer models is fast becoming a prerequisite for the policymaker, legislator, lobbyist, and citizen alike. During our lives, each of us will be faced with the result of models and will have to make judgments about their relevance and validity. Most people, unfortunately, cannot make these decisions in an intelligent and informed manner, since for them computer models are black boxes: devices that operate in completely mysterious ways. Because computer models are so poorly understood by most people, it is easy for them to be misused, accidentally or intentionally. Thus there have been many cases in which computer models have been used to justify decisions already made and actions already taken, to provide a scapegoat when a forecast turned out wrong, or to lend specious authority to an argument. If these misuses are to stop and if modeling is to become a rational tool of the general public, rather than remaining the special magic of a technical priesthood, a basic understanding of models must become more widespread. This paper takes a step toward this goal by offering model consumers a peek inside the black boxes. The computer models it describes are the kinds used in foresight and policy analysis (rather than physical system models such as NASA uses to test the space shuttle). The characteristics and capabilities of the models, their advantages and disadvantages, uses and misuses are all addressed. The fundamental assumptions of the major modeling techniques are discussed, as is the appropriateness of these techniques for foresight and policy analysis. Consideration is also given to the crucial questions a model user should ask when evaluating the appropriateness and validity of a model.
Cite as: J. D. Sterman (1991) A Skeptic's Guide to Computer Models. (Book Section)