Preferred by “All” and Preferred by “Most” Decision Makers: Almost Stochastic Dominance

2002 ◽  
Vol 48 (8) ◽  
pp. 1074-1085 ◽  
Author(s):  
Moshe Leshno ◽  
Haim Levy
2020 ◽  
Author(s):  
Yi-Chieh Huang ◽  
Kamhon Kan ◽  
Larry Y. Tzeng ◽  
Kili C. Wang

Knowing how small a violation of stochastic dominance rules would be accepted by most individuals is a prerequisite to applying almost stochastic dominance criteria. Unlike previous laboratory-experimental studies, this paper estimates an acceptable violation of stochastic dominance rules with 939,690 real world data observations on a choice of deductibles in automobile theft insurance. We find that, for all policyholders in the sample who optimally chose a low deductible, the upper bound estimate of the acceptable violation ratio is 0.0014, which is close to zero. On the other hand, considering that most decision makers, such as 99% (95%) of the policyholders in the sample, optimally chose the low deductible, the upper bound estimate of the acceptable violation ratio is 0.0405 (0.0732). Our results provide reference values for the acceptable violation ratio for applying almost stochastic dominance rules. This paper was accepted by Manel Baucells, decision analysis.


2020 ◽  
Vol 17 (2) ◽  
pp. 169-184
Author(s):  
Chunling Luo ◽  
Chin Hon Tan

In this paper, we propose a new concept of almost second-degree stochastic dominance (ASSD), which we term almost risk-averse stochastic dominance (ARSD). Compared with existing ASSD conditions, ARSD can exclude extremely risk-averse utility functions. Hence, ARSD is able to reveal clear preferences of most risk-averse decision makers in practice, which are otherwise unable to be revealed. The simple closed-form of ARSD not only makes it easy to use in practice but also provides a clear insight into the preferences of decision makers and the difference in expected values and stochastic dominance violations. Moreover, we show that ARSD can be inferred based on mean and variance alone, and thus it is applicable even when distribution information is incomplete.


2005 ◽  
Vol 50 (164) ◽  
pp. 135-149
Author(s):  
Dejan Trifunovic

In order to rank investments under uncertainty, the most widely used method is mean variance analysis. Stochastic dominance is an alternative concept which ranks investments by using the whole distribution function. There exist three models: first-order stochastic dominance is used when the distribution functions do not intersect, second-order stochastic dominance is applied to situations where the distribution functions intersect only once, while third-order stochastic dominance solves the ranking problem in the case of double intersection. Almost stochastic dominance is a special model. Finally we show that the existence of arbitrage opportunities implies the existence of stochastic dominance, while the reverse does not hold.


2014 ◽  
Vol 57 (2) ◽  
pp. 377-405 ◽  
Author(s):  
Michel M. Denuit ◽  
Rachel J. Huang ◽  
Larry Y. Tzeng

2013 ◽  
Vol 121 (2) ◽  
pp. 252-256 ◽  
Author(s):  
Xu Guo ◽  
Xuehu Zhu ◽  
Wing-Keung Wong ◽  
Lixing Zhu

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