Nada Elgendy, Ahmed Elragal & Tero Päivärinta (2021) DECAS: a modern data-driven decision theory for big data and analytics, Journal of Decision Systems, DOI: https://doi.org/10.1080/12460125.2021.1894674
DECAS : a modern data-driven decision theory for big data and analytics
|Author:||Elgendy, Nada1; Elragal, Ahmed2; Päivärinta, Tero1|
1Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden
|Online Access:||PDF Full Text (PDF, 2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021121360187
|Publish Date:|| 2021-12-13
Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions.
Journal of decision systems
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
512 Business and management
This research has been partially funded by the ITEA3 project Oxilate (https://itea3.org/project/oxilate.html).
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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