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Controlling complex policy problems: A multimethodological approach using system dynamics and network controllability


Schoenenberger, Lukas; Tanase, Radu (2017). Controlling complex policy problems: A multimethodological approach using system dynamics and network controllability. Journal of Simulation, 12(2):162-170.

Abstract

Notwithstanding the usefulness of system dynamics in analysing complex policy problems, policy design is far from straightforward and in many instances trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability, an emerging eld in network science, to facilitate the detection of e ective leverage points in system dynamics models and thus to support the design of in uential policies. We illustrate our approach by analysing a classic system dynamics model: the World Dynamics model. We show that it is enough to control only 53% of the variables to steer the entire system to an arbitrary nal state. We further rank all variables according to their importance in controlling the system and we validate our approach by showing that high ranked variables have a signi cantly larger impact on the system behaviour compared to low ranked variables.

Abstract

Notwithstanding the usefulness of system dynamics in analysing complex policy problems, policy design is far from straightforward and in many instances trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability, an emerging eld in network science, to facilitate the detection of e ective leverage points in system dynamics models and thus to support the design of in uential policies. We illustrate our approach by analysing a classic system dynamics model: the World Dynamics model. We show that it is enough to control only 53% of the variables to steer the entire system to an arbitrary nal state. We further rank all variables according to their importance in controlling the system and we validate our approach by showing that high ranked variables have a signi cantly larger impact on the system behaviour compared to low ranked variables.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Modeling and Simulation
Social Sciences & Humanities > Management Science and Operations Research
Physical Sciences > Industrial and Manufacturing Engineering
Language:English
Date:18 August 2017
Deposited On:28 Mar 2019 13:08
Last Modified:06 Jul 2021 18:30
Publisher:Taylor & Francis
ISSN:1747-7778
OA Status:Green
Publisher DOI:https://doi.org/10.1080/17477778.2017.1387335
Other Identification Number:merlin-id:15564

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