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Expected utility analysis of infinite compound lotteries

Citation

Cullum, J and Nikolova, N and Tenekedjiev, K, Expected utility analysis of infinite compound lotteries, International Journal of General Systems, 48, (2) pp. 112-138. ISSN 0308-1079 (2018) [Refereed Article]

Copyright Statement

2018 Informa UK Limited, trading as Taylor & Francis Group

DOI: doi:10.1080/03081079.2018.1548443

Abstract

Lotteries can be used to model alternatives with uncertain outcomes. Decision theory uses compound ordinary lotteries to represent a structure of lotteries within lotteries, but can only rank the finite compound lottery structure. We expand upon this approach to introduce solutions for infinite compound ordinary lotteries (ICOL). We describe a novel procedure to simplify any ICOL as much as possible to a maximum reduced ICOL, which is not a unique representation. We limit our discussion to ICOLs of first order, which are defined as maximum reduced ICOLs with a single maximum reduced ICOL in their direct outcome. Two special cases of ICOLs of first order are discussed. These are recursive and semi-recursive ICOLs. We provide an analytical approach to find the expected utility of recursive ICOLs, and a numerical algorithm for semi-recursive ICOLs. We demonstrate our solution methods by evaluating example decision problems involving: a randomizing device with unsuccessful trials, the St. Petersburg paradox, and training with virtual reality.

Item Details

Item Type:Refereed Article
Keywords:ICOLs of first order, recursive ICOL, semi-recursive ICOL, expected utility, generalized St. Petersburg paradox
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Decision Making
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Commerce, Management, Tourism and Services
UTAS Author:Cullum, J (Ms Jane Cullum)
UTAS Author:Nikolova, N (Professor Nataliya Nikolova)
UTAS Author:Tenekedjiev, K (Professor Kiril Tenekedjiev)
ID Code:129474
Year Published:2018
Web of Science® Times Cited:1
Deposited By:Maritime and Logistics Management
Deposited On:2018-12-01
Last Modified:2020-05-06
Downloads:0

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