System Dynamics: Systems Thinking and Modeling for a Complex World

Todays problems often arise as unintended consequences of yesterdays solutions. Social systems often suffer from policy resistance, the tendency for well-intentioned interventions to be defeated by the response of the system to the intervention itself. The field of system dynamics, created at MIT in the 1950s by Jay Forrester, is designed to help us learn about the structure and dynamics of the complex systems in which we are embedded, design high-leverage policies for sustained improvement, and catalyze successful implementation and change. Drawing on engineering control theory and the modern theory of nonlinear dynamical systems, system dynamics often involves the development of formal models and management flight simulators to capture complex dynamics, and to create an environment for learning and policy design. Unlike pure engineering problems if any exist, human systems present unique challenges, including long time horizons, issues that cross disciplinary boundaries, the need to develop reliable models of human behavior, and the great difficulty of experimental testing. Successful change in social systems also requires the active participation of a wide range of people in the modeling and policy design process, people who often lack technical training. In this paper I discuss requirements for the effective use of system dynamics and illustrate with a successful application to a difficult business issue

Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

Supply chains are fundamental to a wide range of systems. Many exhibit chronic instability, including oscillations, amplification as disturbances move upstream from final sales, and phase lag. Every supply chain consists of networks, including stocks and flows of material, time delays, and the decision rules that couple them. This chapter develops a dynamic model of supply chains and shows how it can be customized to various situations. The model explains the sources of oscillation, amplification, and phase lag. Simulations, field studies, and experiments demonstrate that supply chain instability and oscillations arise from the failure of decision-makers to account for the time delays in the system and the supply line of orders not yet received, and failure to understand basic principles of accumulation. Two learning activities, the Manufacturing Case and the Beer Distribution Game, provide interactive experiences that help students and executives understand the structure and dynamics of supply chains

Factorization is not violated

We show that existing proofs of factorization imply the cancellation of certain multiladder contributions that Gotsman, Levin, and Maor had suggested would invalidate the basic factorization theorem in QCD. No modifications of the original argument are necessary, although the details of the example offer useful insight into the mechanisms of factorization.Comment: 11 pages including 10 figure

How to Save a Leaky Ship: Capability Traps and the Failure of Win-Win Investments in Sustainability and Social Responsibility

Can managers enhance social responsibility while also improving profitability? Research demonstrates that there are “win-win” investments that improve both socially desirable outcomes and the bottom line, from energy and the environment to wages and workplace safety. Yet many such opportunities are not taken—money is left on the table. Here we explore this puzzle using the case of energy efficiency in a large research university, a setting that should favor implementation of win-win actions. However, despite a long time horizon, large endowment and pro-social mission, the university failed to implement many programs offering both large environmental and financial benefits. Using ethnographic field study and panel regression we develop a novel simulation model integrating energy use, maintenance, and facilities renewal. We find that the organization inadvertently fell into a capability trap in which poor performance prevented investments in win-win opportunities and the capabilities needed to realize them, perpetuating poor performance. Escaping the trap requires investments large enough and sustained long enough to cross tipping thresholds that convert the vicious cycle in to a virtuous cycle of better performance, greater investment and still better performance. We discuss how the organization is escaping from the trap and whether the results generalize to other contexts

Cyclical dynamics of airline industry earnings

Aggregate 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

A dynamic model for health screening: misperceptions, feedback and long term trends in screening mammography

Implications of widespread mammography screening remain controversial, and major health organizations in the US adopt different guidelines reflecting significant variations in actual practice. Literature suggests that implementation of routine screening over the past 30 years has incurred less benefit and more harms than is formerly believed

Transition challenges for alternative fuel vehicle and transportation systems

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 enormous size and importance of the auto industry and vehicle fleet. 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 and competition among alternative fuel vehicles, including the coevolution of the fleet, technology, driver behavior, and complementary resources. Here we focus on the generation of consumer awareness of alternatives through feedback from driving 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. Nevertheless, marketing and subsidies for alternatives to ICE must remain in place for long periods for diffusion to become self-sustaining. Expanding the model boundary to include endogenous learning, technological spillovers and spatial coevolution of fueling infrastructure adds additional feedbacks that further suppress the diffusion of alternative vehicles

Reporting guidelines for simulation-based research in social sciences

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 guidelines are discussed at the end