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A Self-regulating Information Acquisition Algorithm for Preventing Choice Regret in Multi-perspective Decision Making

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In a world filled with an increasing number of choices people must carefully select the information they acquire in order to make sound decisions that they will not regret in the future. This ranges from everyday life decisions to those made by experts in the business world. The authors introduce a novel information acquisition algorithm based on the value that information has when preventing a decision maker from regretting his or her current decision. The main features of the model include the capacity to account for different risk attitudes of the decision maker as well as his or her forward-looking behavior, the ability to assess choice objects (projects or products) defined by multiple characteristics and a self-regulation mechanism for the information acquisition process, even in the absence of information acquisition costs. The main properties of the algorithm are examined numerically. Copyright Springer Fachmedien Wiesbaden 2014

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  • Francisco Santos-Arteaga & Debora Di Caprio & Madjid Tavana, 2014. "A Self-regulating Information Acquisition Algorithm for Preventing Choice Regret in Multi-perspective Decision Making," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(3), pages 165-175, June.
    • Handle: RePEc:spr:binfse:v:6:y:2014:i:3:p:165-175
    DOI: 10.1007/s12599-014-0322-8
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    1. Di Caprio, Debora & Santos-Arteaga, Francisco J. & Tavana, Madjid, 2019. "The role of anticipated emotions and the value of information in determining sequential search incentives," Operations Research Perspectives, Elsevier, vol. 6(C).

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