scholarly journals A rank-dependent expected utility model for strategic route choice with stated preference data

2013 ◽  
Vol 27 ◽  
pp. 117-130 ◽  
Author(s):  
Michael Razo ◽  
Song Gao
Keyword(s):  
Expected Utility ◽  
Stated Preference ◽  
Route Choice ◽  
Utility Model ◽  
Preference Data ◽  
Stated Preference Data ◽  
Expected Utility Model ◽  
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.

Advanced Travel Time Information and Drivers' Route Choice: Stated Preference Data

1999 ◽  
Vol 43 (18) ◽  
pp. 967-971
Author(s):  
Konstantinos V. Katsikopoulos ◽  
Duse-Anthony Yawa ◽  
Donald L. Fisher ◽  
Susan A. Duffy
Keyword(s):  
Travel Time ◽  
Stated Preference ◽  
Route Choice ◽  
Preference Data ◽  
Stated Preference Data ◽  
Time Information

scholarly journals A Bayesian Method for Characterizing Population Heterogeneity

Games ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 40
Author(s):  
Dale O. Stahl
Keyword(s):  
Expected Utility ◽  
Laboratory Experiments ◽  
Bayesian Method ◽  
Laboratory Data ◽  
Population Heterogeneity ◽  
Utility Model ◽  
The Subject ◽  
Expected Utility Model ◽  
Rank Dependent Expected Utility ◽  
Lottery Choices

A stylized fact from laboratory experiments is that there is much heterogeneity in human behavior. We present and demonstrate a computationally practical non-parametric Bayesian method for characterizing this heterogeneity. In addition, we define the concept of behaviorally distinguishable parameter vectors, and use the Bayesian posterior to say what proportion of the population lies in meaningful regions. These methods are then demonstrated using laboratory data on lottery choices and the rank-dependent expected utility model. In contrast to other analyses, we find that 79% of the subject population is not behaviorally distinguishable from the ordinary expected utility model.

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