Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-25T06:08:47.435Z Has data issue: false hasContentIssue false

Almost stochastic dominance under inconsistent utility and loss functions

Published online by Cambridge University Press:  15 September 2017

Chunling Luo*
Affiliation:
National University of Singapore
Zhou He*
Affiliation:
National University of Singapore
Chin Hon Tan*
Affiliation:
National University of Singapore
*
* Postal address: Department of Industrial & Systems Engineering, National University of Singapore, Singapore.
* Postal address: Department of Industrial & Systems Engineering, National University of Singapore, Singapore.
* Postal address: Department of Industrial & Systems Engineering, National University of Singapore, Singapore.

Abstract

Current literature on stochastic dominance assumes utility/loss functions to be the same across random variables. However, decision models with inconsistent utility functions have been proposed in the literature. The use of inconsistent loss functions when comparing between two random variables can also be appropriate under other problem settings. In this paper we generalize almost stochastic dominance to problems with inconsistent utility/loss functions. In particular, we propose a set of conditions that is necessary and sufficient for clear preferences when the utility/loss functions are allowed to vary across different random variables.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Becker, J. L. and Sarin, R. K. (1987). Lottery dependent utility. Manag. Sci. 33, 13671382. Google Scholar
[2] Becker, J. L. and Sarin, R. K. (1989). Decision analysis using lottery-dependent utility. J. Risk Uncertainty 2, 105117. Google Scholar
[3] Carlsson, C. et al. (2006). Are pharmaceuticals potent environmental pollutants?: Part I: Environmental risk assessments of selected active pharmaceutical ingredients. Sci. Total Environ. 364, 6787. Google Scholar
[4] Daniels, R. L. and Keller, L. R. (1990). An experimental evaluation of the descriptive validity of lottery-dependent utility theory. J. Risk Uncertainty 3, 115134. Google Scholar
[5] De La Cal, J. and Cárcamo, J. (2010). Inverse stochastic dominance, majorization, and mean order statistics. J. Appl. Prob. 47, 277292. Google Scholar
[6] Ferrari, B. et al. (2004). Environmental risk assessment of six human pharmaceuticals: are the current environmental risk assessment procedures sufficient for the protection of the aquatic environment? Environ. Toxicol. Chem. 23, 13441354. Google Scholar
[7] Komori, K., Suzuki, Y., Minamiyama, M. and Harada, A. (2013). Occurrence of selected pharmaceuticals in river water in Japan and assessment of their environmental risk. Environ. Monit. Assess. 185, 45294536. Google Scholar
[8] Leshno, M. and Levy, H. (2002). Preferred by "all" and preferred by "most" decision makers: almost stochastic dominance. Manag. Sci. 48, 10741085. Google Scholar
[9] Levy, H. (1992). Stochastic dominance and expected utility: survey and analysis. Manag. Sci. 38, 555593. CrossRefGoogle Scholar
[10] Levy, H. (2016). Stochastic Dominance: Investment Decision Making Under Uncertainty, 3rd edn. Springer, Cham. Google Scholar
[11] Müller, A., Scarsini, M., Tsetlin, I. and Winkler, R. L. (2017). Between first- and second-order stochastic dominance. To appear in Manag. Sci. Available at http://dx.doi.org/10.1287/mnsc.2016.2486. Google Scholar
[12] Müller, A. and Stoyan, D. (2002). Comparison Methods for Stochastic Models and Risks. John Wiley, Chichester. Google Scholar
[13] Pal, A. et al. (2014). Emerging contaminants of public health significance as water quality indicator compounds in the urban water cycle. Environ. Internat. 71, 4662. Google Scholar
[14] Schmidt, U. (2001). Lottery dependent utility: a reexamination. Theory Decision 50, 3558. Google Scholar
[15] Shaked, M. and Shanthikumar, J. G. (2007). Stochastic Orders. Springer, New York. CrossRefGoogle Scholar
[16] Tan, C. H. (2015). Weighted almost stochastic dominance: revealing the preferences of most decision makers in the St. Petersburg paradox. Decision Anal. 12, 7480. Google Scholar
[17] Tan, C. H. and Luo, C. (2017). Clear preferences under partial distribution information. Decision Anal. 14, 6573. Google Scholar
[18] Tsetlin, I., Winkler, R. L., Huang, R. J. and Tzeng, L. Y. (2015). Generalized almost stochastic dominance. Operat. Res. 63, 363377. Google Scholar
[19] Tzeng, L. Y., Huang, R. J. and Shih, P.-T. (2013). Revisiting almost second-degree stochastic dominance. Manag. Sci. 59, 12501254. Google Scholar
[20] Yu, Y. (2009). Stochastic ordering of exponential family distributions and their mixtures. J. Appl. Prob. 46, 244254. Google Scholar