Dissertation: Deeper Insights from Less Data – Developing a Network-Based Approach to Key Driver Identification in Scenario Analysis

Deeper Insights from Less Data – Developing a Network-Based Approach to Key Driver Identification in Scenario Analysis

QM – Quantitative Methoden in Forschung und Praxis, volume 48

Hamburg , 244 pages

ISBN 978-3-339-10722-0 (print)
ISBN 978-3-339-10723-7 (eBook)

about this book

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When Shell managed to escape the downswing of the oil crisis in the 1970’s with the help of scenario analysis, this method attracted worldwide attention. At the beginning of a scenario project, one usually employs impact analysis in order to identify so-called key drivers – drivers of extraordinary importance for the future of the object of investigation. However, traditional impact analysis requires vast amounts of data, and therefore constitutes a rather expensive exercise that only prosperous companies and institutions can afford. Furthermore, the traditional approach analyzes the data it collects by means of simple addition, which ignores such available and relevant information as areas of influence.

This dissertation aims to alleviate the mismatch between excessive data collection effort on the one hand, and the mathematically superficial analysis of the obtained data on the other hand. For this purpose, impact analysis is set into the context of network analysis – a transfer that has not been made thus far, even though both methods share numerous conceptual parallels, and notwithstanding the fact that network analysis has developed mathematically much more substantial approaches over its considerably longer history than impact analysis has. On this basis, a new measure of centrality – named Generalized Area Centrality (GAC) – is proposed and implemented by means of Monte Carlo simulations. A real scenario project for the city of Münster provides empirical evidence that this approach is able to reduce data collection efforts by 50%, while identifying more valid key drivers than traditional impact analysis does. In this way, the dissertation contributes to the practical applicability and “democratization” of scenario analysis. From a scientific perspective, the innovative centrality measure developed here can be applied far beyond the realms of scenario analysis; and thus it enriches network analysis as an overall discipline.

Ihr Werk im Verlag Dr. Kovač

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