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| Type | Conference Presentation |
| Scope | Discipline-based scholarship |
| Title | Controlling complex policy problems: a multi-methodological approach using system dynamics and network controllability |
| Organization Unit | |
| Authors |
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| Presentation Type | other |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | Conference on Complex Systems |
| Event Type | conference |
| Event Location | Amsterdam, Netherlands |
| Event Start Date | September 19 - 2016 |
| Event End Date | September 22 - 2016 |
| Abstract Text | System dynamics (SD), an approach to modeling and simulating complex systems, has repeatedly demonstrated its value in contributing to the understanding and solution of complex policy problems. Typical areas of system dynamics application include modeling of policy problems related to public health, energy and the environment, social welfare, sustainable development, and security. One of the main challenges in system dynamics is that, due to a high degree of interdependent model variables and nonlinear relationships, the detection of model levers, i.e. variables capable of effectively and efficiently controlling complex policy problems, is exceedingly demanding. So, notwithstanding the usefulness of system dynamics in the analysis of these problems, the solution identification process is far from straightforward and in most cases trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability to facilitate the detection of model levers. In essence, a system dynamics model can be thought of a web of interrelated causal factors that are assumed giving rise to the complex policy problem under study. Due to its web similarity, the structure of a system dynamics model can be accurately described as a directed weighted network, making it accessible to algorithmic exploration using concepts from the fields of graph theory and network science. Referring to recent research on control principles of complex networks, model levers are found first by calculating the size of the minimum driver set of the system dynamics model (network), second by computing all existing minimum driver sets, and third by ranking minimum driver set variables according to their control centrality. Variables with a high control centrality should be of primary interest to policy-makers when designing new solutions to complex policy problems. We demonstrate the proposed multimethodological approach on the basis of the World Dynamics model, a classic example from the system dynamics literature. |
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