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Predictive analytics and disused railways requalification: insights from a Post Factum Analysis perspective

Ciomek, Krzysztof, Ferretti, Valentina and Kadzinski, Milosz (2018) Predictive analytics and disused railways requalification: insights from a Post Factum Analysis perspective. Decision Support Systems, 105. pp. 34-51. ISSN 0167-9236

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Identification Number: 10.1016/j.dss.2017.10.010

Abstract

Strategic decision making problems in the public policy domain typically involve the comparison of competing options by different stakeholders. This paper considers a real case study oriented toward ranking potential actions for the regeneration of disused railways in Italy. The study involves multiple con icting criteria such as an expected duration of construction works, costs, a number of potential users, and new green areas. Within this context, we demonstrate that Post Factum Analysis (PFA) coupled with Decision Aiding supports the development of robust recommendations. The role of PFA is to highlight how the actions' performances need to be modified so that the recommendation is changed in a desired way. In particular, it highlights the minimal improvements that would warrant the feasibility of some currently impossible outcome (e.g., achieving a better position in the ranking) or the maximal deteriorations that alternatives can afford to maintain some target result (e.g., not losing their advantage over some other options). The use of a focus group with both experts and participants in the decision making process provided insights on how PFA can support: (i) the creation of arguments in favour or against the respective options under analysis, (ii) understanding of the results' sensitivity with respect to possible changes in the alternatives' performances, (iii) a better informed discussion about the results among the participants in the process, and (iv) the development of new/better alternatives.

Item Type: Article
Official URL: https://www.journals.elsevier.com/decision-support...
Additional Information: © 2017 The Authors
Divisions: Management
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Sets: Departments > Management
Date Deposited: 01 Dec 2017 14:23
Last Modified: 24 Apr 2019 23:00
Projects: IP2015 029674 - 0296/IP2/2016/74
Funders: Polish Ministry of Science and Higher Education
URI: http://eprints.lse.ac.uk/id/eprint/85922

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