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AbstractResearch 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. Copyright © 2010 John Wiley & Sons, Ltd.

AbstractThis note introduces the special issue of the System Dynamics Review celebrating the 50th anniversary of the founding of the field. Copyright © 2007 John Wiley & Sons, Ltd.

AbstractThoughtful 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. The paper is based on the talk the author delivered at the 2002 International System Dynamics Conference upon presentation of the Jay W. Forrester Award. Copyright © 2002 John Wiley & Sons, Ltd.

AbstractThis article introduces the special issue of System Dynamics Review dedicated to the memory of Dana Meadows. It reviews her role as a leader in the fields of environmental journalism, system dynamics, and systems analysis, and summarizes the articles in the special issue. Copyright © 2002 John Wiley & Sons, Ltd.

AbstractChange is accelerating, and as the complexity of the systems in which we live grows, so do the unanticipated side effects of human actions, further increasing complexity. Many scholars call for the development of systems thinking to improve our ability to manage wisely. But how do people learn in and about complex dynamic systems? Learning is a feedback process in which our decisions alter the real world, we receive information feedback about the world and revise the decisions we make and the mental models that motivate those decisions. Unfortunately, in the world of social action various impediments slow or prevent these learning feedbacks from functioning, allowing erroneous and harmful behaviors and beliefs to persist. The barriers to learning include the dynamic complexity of the systems themselves; inadequate and ambiguous outcome feedback; systematic mispercep‐tions of feedback; inability to simulate mentally the dynamics of our cognitive maps; poor interpersonal and organizational inquiry skills; and poor scientific reasoning skills. To be successful, methods to enhance learning about complex systems must address all these impediments. Effective methods for learning in and about complex dynamic systems must include (1) tools to elicit participant knowledge, articulate and reframe perceptions, and create maps of the feedback structure of a problem from those perceptions; (2) simulation tools and management flight simulators to assess the dynamics of those maps and test new policies; and (3) methods to improve scientific reasoning skills, strengthen group process, and overcome defensive routines for individuals and teams.

AbstractRecent work has shown that several well‐known models in the system dynamics literature contain previously unsuspected regimes of deterministic chaos. Two of the most extensively analyzed are Sterman's model of the economic long wave and the production‐distribution model of the Beer Distribution Game. The significance of these theoretical developments hinges on whether the chaotic regimes lie in the realistic region of parameter space. There are also questions regarding the descriptive accuracy of models of human systems that exhibit chaos. Because of data limitations and the inability to conduct controlled experiments, empirical studies at the aggregate level are not likely to resolve these questions.An alternative approach is based on laboratory experiments in which models provide a simulated environment for the study of human decisionmaking behavior. Recently, laboratory experiments have been conducted to analyze decision‐making behavior in the longwave model and the Beer Distribution Game. This article describes these experiments and shows that the behavior of the subjects is explained well with a simple heuristic long used in system dynamics modeling and well grounded in behavioral decision theory. The parameters of the proposed decision rule are estimated econometrically for each subject. The parameters that characterize a significant minority of the subjects are shown to produce chaos. This direct experimental evidence that chaos can be produced by the decision‐xnaking behavior of real people has important implications for the formulation, analysis, and testing of models of human behavior.

AbstractThe economic crisis of the 1980s has revived interest in the economic long wave or Kondratiev cycle. Since 1975 the System Dynamics National Model has been the vehicle for the development of a dynamic, endogenous, integrated theory of the economic long wave. This paper describes the integrated theory that has now emerged from extensive analysis of the full National Model and from simple models. Simulations of the model are presented to show the wide range of empirical evidence accounted for by the model, including many of the symptoms of the present economic crisis. In particular, the theory suggests that the long wave arises from the interaction of two fundamental facets of modern industrial economies. First, firms contain inherently oscillatory structures. Second, self‐reinforcing processes amplify the instability. The relative strengths of these mechanisms and the amplification of the long wave through their interactions are discussed, as are the linkages of the longwave theory of innovation, technological progress, social innovation, and political value change.

AbstractAggregate airline industry earnings have exhibited large‐amplitude cyclical behavior since deregulation in 1978. To explore the causes of these cycles we develop a behavioral dynamic model of the airline industry with endogenous capacity expansion, demand, pricing, and other feedbacks; and model several strategies industry actors have employed in efforts to mitigate the cycle. We estimate model parameters by maximum likelihood methods during both partial model tests and full model estimation using Markov chain Monte Carlo methods to establish confidence intervals. Contrary to prior work we find that the delay in aircraft acquisition (the supply line of capacity on order) is not a very influential determinant of the profit cycle. Instead we find that aggressive use of yield management—varying prices to ensure high load factors (capacity utilization)—may have the unintended effect of increasing earnings variance by increasing the sensitivity of profit to changes in demand. Copyright © 2013 System Dynamics Society