Determining intervention thresholds that change output behavior patterns

This paper details a semi-automated method that can calculate intervention thresholds—that is, the minimum required intervention sizes, over a given timeframe, that result in a desired change in a system's output behavior pattern. The method exploits key differences in atomic behavior profiles that exist between classifiable pre- and post-intervention behavior patterns. An automated process of systematic adjustment of the intervention variable, while monitoring the key difference, identifies the intervention thresholds. The results, in turn, can be studied and presented in intervention threshold graphs in combination with final runtime graphs. Overall, this method allows modelers to move beyond ad hoc experimentation and develop a better understanding of intervention dynamics. This article presents an application of the method to the well-known World 3 model, which helps demonstrate both the procedure and its benefits. © 2017 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society

[1]  Kishore Sengupta,et al.  ANATOMY OF A DECISION TRAP IN COMPLEX NEW PRODUCT DEVELOPMENT PROJECTS , 2013 .

[2]  Erik Pruyt,et al.  On generating and exploring the behavior space of complex models , 2015 .

[3]  L. Codispoti.  The limits to growth , 1997, Nature.

[4]  Gustavo Stubrich.  The Fifth Discipline: The Art and Practice of the Learning Organization , 1993 .

[5]  J. Forrester.  Industrial Dynamics , 1997 .

[6]  Kim E. van Oorschot,et al.  How to Counteract the Suppression of Exploration in Publicly Traded Corporations , 2015 .

[7]  Maurizio Zollo,et al.  Dynamic capabilities, deliberate learning and environmental dynamism: a simulation model , 2010 .

[8]  J Swanson,et al.  Business Dynamics—Systems Thinking and Modeling for a Complex World , 2002, J. Oper. Res. Soc..

[9]  Kishore Sengupta,et al.  Escalation, de-escalation, or reformulation : effective interventions in delayed NPD projects , 2011 .

[10]  Bob Walrave,et al.  Getting Trapped in the Suppression of Exploration: A Simulation Model , 2011 .

[11]  Andrew Ford,et al.  Estimating the Impact of Efficiency Standards on the Uncertainty of the Northwest Electric System , 1990, Oper. Res..

[12]  P. Senge.  The fifth discipline : the art and practice of the learning organization/ Peter M. Senge , 1991 .

[13]  Marcella Corduas,et al.  Time series clustering and classification by the autoregressive metric , 2008, Comput. Stat. Data Anal..

[14]  N. Repenning.  Understanding fire fighting in new product development , 2001 .

[15]  Hazhir Rahmandad,et al.  Pattern Recognition for Model Testing, Calibration, and Behavior Analysis , 2015 .

[16]  Yaman Barlas,et al.  Automated parameter specification in dynamic feedback models based on behavior pattern features , 2011 .

[17]  Dennis L. Meadows,et al.  Limits to growth : the 30-year update , 2004 .

[18]  Nelson P. Repenning.  Understanding fire fighting in new product development , 2001 .

[19]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[20]  Jan H. Kwakkel,et al.  Using System Dynamics for Grand Challenges: The ESDMA Approach , 2015 .

[21]  Rogelio Oliva,et al.  Model calibration as a testing strategy for system dynamics models , 2003, Eur. J. Oper. Res..

[22]  John D. W. Morecroft,et al.  System dynamics and microworlds for policymakers , 1988 .

[23]  N. Repenning,et al.  Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse , 2002 .

[24]  J. I. Mills,et al.  Limits to growth: The 30‐year update , 2006 .

[25]  M. Hannan,et al.  Structural Inertia and Organizational Change , 1984 .

[26]  Jan H. Kwakkel,et al.  Dynamic scenario discovery under deep uncertainty: The future of copper , 2013 .

[27]  Larisa Angstenberger.  Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering , 2001 .

[28]  Hazhir Rahmandad,et al.  Analytical Methods for Dynamic Modelers , 2015 .