Rank-dependent Expected Utility - An Outline

1993 ◽  
pp. 55-74
Author(s):  
John Quiggin
Keyword(s):  
Expected Utility ◽  
Rank Dependent Expected Utility

BETTING MARKET: Rank Dependent Expected Utility

2013 ◽  
Vol 1 (2) ◽  
pp. 147-163
Author(s):  
Loreto Llorente
Keyword(s):  
Decision Theory ◽  
Expected Utility ◽  
Utility Maximization ◽  
Betting Market ◽  
Expected Utility Maximization ◽  
Classical Decision ◽  
Rank Dependent Expected Utility ◽  
The Way

In Pelota matches, games with two mutually exclusive and exhaustive outcomes, bets on the winner are made through a middleman who receives 16% of the finally paid amount. The classical decision theory of expected utility maximization can not explain this market assuming bettors are identical. Llorente and Aizpurua (2007) explain the existence of bets in the market under Quiggin’s rank dependent expected utility (RDEU) model. They find that bettors have to be optimistic in order to explain the existence of a bet. Analyzing the way odds are fixed in the market Llorente (2006) finds that assuming equal return on bets there are inefficiencies in the market. In this paper we show that, given an assumption that bettors are rank dependent expected utility maximizers, these inefficiencies tend to disappear.

The risk averse newsvendor problem from rank-dependent expected utility approach

2014 ◽  
Vol 20 (3) ◽  
pp. 262
Author(s):  
Jiangfeng Li ◽  
Weifang Liu ◽  
Jinliang Wang ◽  
Shunming Zhang
Keyword(s):  
Expected Utility ◽  
Newsvendor Problem ◽  
Risk Averse ◽  
Utility Approach ◽  
Rank Dependent Expected Utility

A Comparison of Probabilistic Prize Promotion Schemes

2012 ◽  
Vol 40 (7) ◽  
pp. 1183-1194 ◽  
Author(s):  
Rong Chen ◽  
Jinsong Huang ◽  
Song Su ◽  
Feng He
Keyword(s):  
Optimal Design ◽  
Expected Utility ◽  
Cell Phone ◽  
Utility Model ◽  
Winning Probability ◽  
Behavioral Theories ◽  
Expected Utility Model ◽  
Rank Dependent Expected Utility

Based on the rank-dependent expected utility model (Quiggin, 1991), hypotheses are formed in this study regarding optimal prize promotion structure with reference to associated probabilistic aspects. Referencing both modeling works and related behavioral theories, we compared several design schemes. Using purchase scenarios of a low-value product (bread) and a high-value product (cell phone) we determined the optimal design among promotion schemes that differ by winning probability, prize amount, and number of prize value levels. We found that a promotion offering a combination of high-value prizes plus some low-value prizes was invariably preferred over a promotion offering only high- or low-value prizes. We also explored whether these high- or low-value prizes should be in a series of ascending value or prizes at each value level should be of the same value and whether or not some moderately valuable prizes should also be included.

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