Determining intervention thresholds that change output behavior patterns

Bob Walrave

doi:10.1002/sdr.1564

Abstract

AbstractThis 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

Highlights

Because so many systems and problems are characterized by dynamic complexity, the number of studies that apply system dynamics (SD) has increased (e.g., Repenning, 2001; Romme et al, 2010; Van Oorschot et al, 2013)
To develop a parsimonious approach to identify intervention thresholds, the current study proposes a custom approach for systems that show classifiable pre- and post-intervention output behavior patterns, such that there is only a need to distinguish between two behavior profiles, rather than identify them, which can be achieved by comparing a key difference in their atomic behavior
If the patterns changed significantly across different cut-off values, further investigation would be warranted, such as by choosing or constructing a different, more robust indicator variable and cut-off value. The results in this example instead illustrate that, though the intervention threshold sizes are higher for higher cut-off values, the general trend of the results remains constant, which is a sign of robustness

Summary

Introduction

Because so many systems and problems are characterized by dynamic complexity, the number of studies that apply system dynamics (SD) has increased (e.g., Repenning, 2001; Romme et al, 2010; Van Oorschot et al, 2013). Intervention studies pertain to how an issue or problem can be corrected (Forrester, 1961) and rely on model-based experimentation Such explorations, often referred to as “what-if” experiments (Morecroft, 1988), typically are conducted through ad hoc adjustments of key model parameters (e.g., Repenning, 2001; Walrave et al, 2011). The method is of value to modelers who want to go beyond ad hoc experimentation and conduct systematic analyses of intervention thresholds and how they change over time The latter question has long been subject to calls for increased attention, at least in organization science settings (e.g., Hannan and Freeman, 1984). Perhaps the best-known contributions are automated sensitivity analyses methods, such as those that rely on random univariate sampling or multivariate Monte Carlo sampling, which are

Walrave

Results

Discussion, outlook, and conclusion