John D. Sterman

Systems dynamics modeling: tools for learning in a complex world

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models

When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes. More interestingly, the DE and mean AB dynamics differ for several metrics relevant to public health, including diffusion speed, peak load on health services infrastructure, and total disease burden. The response of the models to policies can also differ even when their base case behavior is similar. In some conditions, however, these differences in means are small compared to variability caused by stochastic events, parameter uncertainty, and model boundary. We discuss implications for the choice among model types, focusing on policy design. The results apply beyond epidemiology: from innovation adoption to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion.

Systems thinking and organizational learning: Acting locally and thinking globally in the organization of the future

Abstract To learn more rapidly and increase flexibility in a world of growing complexity and change, firms are experimenting with new modes of organization, new reward systems, and less authoritarian values — for example, reducing hierarchy, increasing local decision-making responsibility and individual incentives, and rewarding innovation. But local decision making and individual autonomy lead to management anarchy unless managers account for the interconnections and long-term side-effects of their local decisions. Laudable goals such as ‘empowering’ and ‘enabling’ individuals often prove counterproductive unless managers can act locally and think globally. Managers must become ‘systems thinkers’ as well as better learners. This paper reports on one approach to these issues: forming collaborative action research partnerships with corporations to 1) develop new tools to accelerate learning, and 2) test those tools in real organizations where managers face pressing issues. We argue that simulation is an important element of successful learning laboratories to develop systems thinking and promote organizational learning. A case study focused on improving quality and total cost performance in the insurance industry is presented to illustrate how these tools can both produce insight and focus change.

Cutting Corners and Working Overtime: Quality Erosion in the Service Industry

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 2000 to 69.4%, down 5 percentage points from 1994. We hypothesize that the characteristics of services--inseparability, intangibility, and labor intensity--interact with management practices to bias service providers toward reducing the level of service they deliver, often locking entire industries into a vicious cycle of eroding service standards. To explore this proposition we develop a formal model that integrates the structural elements of service delivery. We use econometric estimation, interviews, observations, and archival data to calibrate the model for a consumer-lending service center in a major bank in the United Kingdom. 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.

Dynamic modeling of product development processes

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 article 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.

Risk Communication on Climate: Mental Models and Mass Balance

Public confusion about the urgency of reductions in greenhouse gas emissions results from a basic misconception.

Nobody ever gets credit for fixing problems that never happened: creating and sustaining process improvement

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Expert knowledge elicitation to improve formal and mental models

Knowledge intensive processes are often driven and constrained by the mental models of experts acting as direct participants or managers. Descriptions of these relationships are not generally available from traditional data sources but are stored in the mental models of experts. Often the knowledge is not explicit but tacit, so it is diAcult to describe, examine, and use. Consequently, improvement of complex processes is plagued by false starts, failures, institutional and interpersonal conflict, and policy resistance. Modelers face diAculties in eliciting and representing the knowledge of experts so that useful models can be developed. We describe and illustrate an elicitation method that uses formal modeling and three description format transformations to help experts explicate their tacit knowledge. We use the method to elicit detailed process knowledge describing the development of a new semiconductor chip. The method improved model accuracy and credibility and provided tools for development team mental model improvement. * c 1998 John Wiley & Sons, Ltd. Syst. Dyn. Rev. 14, 309‐340, (1998) Many public and private sector systems increasingly depend on knowledge intensive processes managed and operated by interdisciplinary teams. These systems are diAcult to manage. Often formal models such as system dynamics models are used to help managers understand the sources of diAculties and design more eAective policies. Typically, the expert knowledge of the people who actually operate the system is required to structure and parameterize a useful model. To develop a useful model that is also credible in the eyes of the managers, however, modelers must elicit from these experts information about system structure and governing policies, and then use this information to develop the model. While many methods to elicit information from experts have been developed, most assist in the early phases of modeling: problem articulation, boundary selection, identification of variables, and qualitative causal mapping. These methods are often used in conceptual modeling, that is, in modeling eAorts that stop short of the development of a formal model that can be used to test hypotheses and proposed policies. The literature is comparatively silent, however, regarding methods to elicit the information required to estimate the parameters, initial conditions, and behavior relationships that must be specified precisely in formal modeling.

Overcoming the 90% Syndrome: Iteration Management in Concurrent Development Projects

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 decision-making heuristics participants use to respond to unanticipated problems and perturbations.

A behavioral model of the economic long wave

Abstract This paper presents a simple model of the economic long wave. The model is based on the System Dynamics National Model. Since 1975 the National Model has provided an increasingly rich theory of the economic long wave. The theory relates capital investment, employment and workforce participation, monetary and fiscal policy, inflation, productivity and innovation, and even political values. The model presented here focuses on capital investment. The structure of the model is shown to be consistent with the principles of bounded rationality. The behavior of the model is analyzed, and capital self-ordering is shown to be sufficient to generate long waves. The model complements the National Model by providing a representation of the dynamic hypothesis that is amenable to formal analysis and is easily extended to include other important mechanisms that may influence the nature of the long wave.