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Archive | 2007

Using System Dynamics to Model and Better Understand State Stability

Nazli Choucri; Daniel Goldsmith; Stuart E. Madnick; Dinsha Mistree; J. Bradley Morrison; Michael Siegel

The world can be complex and dangerous - the loss of state stability of countries is of increasing concern. Although every case is unique, there are important common processes. We have developed a system dynamics model of state stability based on an extensive review of the literature and debriefings of subject matter experts. We represent the nature and dynamics of the €O¬oads€U generated by insurgency activities, on the one hand, and the core features of state resilience and its €O£apacity€U to withstand these €O¬oads€U¬ on the other. The challenge is to determine when threats to stability override the resilience of the state and, more important, to anticipate conditions under which small additional changes in anti-regime activity can generate major disruptions. With these insights, we can identify appropriate and actionable mitigation factors to decrease the likelihood of radical shifts in behavior and enhance prospects for stability.


The American Journal of Medicine | 2008

Sidestepping Superstitious Learning, Ambiguity, and Other Roadblocks: A Feedback Model of Diagnostic Problem Solving

Jenny W. Rudolph; J. Bradley Morrison

A central argument of Drs. Eta S. Berner and Mark L. Graber’s review is that feedback processes are crucial to enhancing or inhibiting the quality of diagnostic problem solving over time. Our goal is to enrich the conversation about diagnostic problem solving by presenting an explicit model of the feedback processes inherent in improving diagnostic problem solving. We present a simple, generic model of the fundamental feedback processes at play in calibrating or improving diagnostic problem-solving skill over time. To amplify these key processes, this commentary draws on a 50-year evidence and theory base from the discipline of system dynamics. Using Berner and Graber’s analysis of the challenges of feedback and calibration as a starting point, we depict how feedback loops can operate in a robust or benign manner to support and improve immediate and long-term diagnostic problem solving. Drawing on insights from research on how people manage problem solving that involves dynamic feedback, we then describe how this process is likely to break down. Finally, leverage points for improving diagnostic problem solving and avoiding error are provided. To improve diagnostic problem solving, practitioners and researchers need to move away from viewing diagnosis as a “one-shot deal.” When diagnosis is perceived as a stand-alone, discrete episode of judgment, the solutions suggested to resolve error focus on reducing cognitive biases and increasing expertise and vigilance at the individual clinician level. It is not that such recommendations have no merit, but simply that they are only a small piece of a much larger repertoire of possible solutions that come into sight when we regard diagnostic problem solving as a recursive, feedback-driven process. Put differently, rather than viewing diagnosis as an event or episode, we suggest emphasizing it as an active, ongoing practice in which clinicians revise and redraft their conclusions over time.


Academic Emergency Medicine | 2011

Learning from Accident and Error: Avoiding the Hazards of Workload, Stress, and Routine Interruptions in the Emergency Department

J. Bradley Morrison; Jenny W. Rudolph

This article presents a model of how a build-up of interruptions can shift the dynamics of the emergency department (ED) from an adaptive, self-regulating system into a fragile, crisis-prone one. Drawing on case studies of organizational disasters and insights from the theory of high-reliability organizations, the authors use computer simulations to show how the accumulation of small interruptions could have disproportionately large effects in the ED. In the face of a mounting workload created by interruptions, EDs, like other organizational systems, have tipping points, thresholds beyond which a vicious cycle can lead rather quickly to the collapse of normal operating routines and in the extreme to a crisis of organizational paralysis. The authors discuss some possible implications for emergency medicine, emphasizing the potential threat from routine, non-novel demands on EDs and raising the concern that EDs are operating closer to the precipitous edge of crisis as ED crowding exacerbates the problem.


Academy of Management Proceedings | 2002

THE RIGHT SHOCK TO INITIATE CHANGE: A SENSEMAKING PERSPECTIVE.

J. Bradley Morrison

Organizational scholars and management practitioners have long been concerned with the processes and dynamics associated with changes in the beliefs and practices in organizations. The purpose of t...


Academy of Management Review | 2009

The Dynamics of Action-Oriented Problem Solving: Linking Interpretation and Choice

Jenny W. Rudolph; J. Bradley Morrison; John S. Carroll


Journal of Business Research | 2008

Putting the learning curve in context

J. Bradley Morrison


Cancer Research | 1984

A Sensitive Method for Detecting in Vivo Formation of N-Nitrosomorpholine and Its Application to Rats Given Low Doses of Morpholine and Sodium Nitrite

Stephen S. Hecht; J. Bradley Morrison


Academic Emergency Medicine | 2011

Consensus-based recommendations for research priorities related to interventions to safeguard patient safety in the crowded emergency department.

Christopher Fee; Kendall Hall; J. Bradley Morrison; Robert J. Stephens; Karen S. Cosby; Rollin J. Fairbanks; Barbara Youngberg; Gail Lenehan; Jameel Abualenain; Kevin O’Connor; Robert L. Wears


System Dynamics Review | 2012

Process improvement dynamics under constrained resources: managing the work harder versus work smarter balance

J. Bradley Morrison


System Dynamics Review | 2013

Dynamic modeling as a multidiscipline collaborative journey

J. Bradley Morrison; Jenny W. Rudolph; John S. Carroll

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John S. Carroll

Massachusetts Institute of Technology

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Nelson P. Repenning

Massachusetts Institute of Technology

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Daniel Goldsmith

Massachusetts Institute of Technology

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John D. Sterman

Massachusetts Institute of Technology

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Michael Siegel

Massachusetts Institute of Technology

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