Revisiting Almost Second-Degree Stochastic Dominance

Tzeng, Larry Y.; Huang, Rachel J.; Shih, Pai Ta

doi:10.1287/mnsc.1120.1616

Cited by 94 publications

(44 citation statements)

References 11 publications

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“…Then, following Levy's [20] and Tzeng's [21] definitions of almost first degree stochastic dominance (AFSD) and almost second degree stochastic dominance (ASSD), we further supplement the definitions of almost second degree inverse stochastic dominance (ASISD), and almost prospect stochastic dominance (APSD).…”

Section: Stochastic Dominance and Almost Stochastic Dominancementioning

confidence: 99%

“…Although Leshno and Levy [11] have proposed the definition of almost second-degree stochastic dominance (ASSD) in 2002, Tzeng and Shih [21] as well as Huang et al [25] proved that Levy's [11] definition is incorrect and gave the correctional definition of ASSD. Next, we follow Tzeng's [21] definition and further provide the definitions of ASISD and APSD.…”

Section: Almost Stochastic Dominancementioning

confidence: 99%

“…Tzeng's ASSD was defined as follow: Definition 3 [21]. For every 0.5 < ε 2 < 1, F(x) ε 2 − almost second degree stochastic dominance G(x) (F(x) ε 2 −ASSD G(x)), if and only if,…”

Section: Almost Stochastic Dominancementioning

confidence: 99%

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A New Stochastic Dominance Degree Based on Almost Stochastic Dominance and Its Application in Decision Making

Sun

et al. 2017

Entropy

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Abstract:Traditional stochastic dominance rules are so strict and qualitative conditions that generally a stochastic dominance relation between two alternatives does not exist. To solve the problem, we firstly supplement the definitions of almost stochastic dominance (ASD). Then, we propose a new definition of stochastic dominance degree (SDD) that is based on the idea of ASD. The new definition takes both the objective mean and stakeholders' subjective preference into account, and can measure both standard and almost stochastic dominance degree. The new definition contains four kinds of SDD corresponding to different stakeholders (rational investors, risk averters, risk seekers, and prospect investors). The operator in the definition can also be changed to fit in with different circumstances. On the basis of the new SDD definition, we present a method to solve stochastic multiple criteria decision-making problem. The numerical experiment shows that the new method could produce a more accurate result according to the utility situations of stakeholders. Moreover, even when it is difficult to elicit the group utility distribution of stakeholders, or when the group utility distribution is ambiguous, the method can still rank alternatives.

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Section: Stochastic Dominance and Almost Stochastic Dominancementioning

confidence: 99%

Section: Almost Stochastic Dominancementioning

confidence: 99%

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A New Stochastic Dominance Degree Based on Almost Stochastic Dominance and Its Application in Decision Making

Sun

et al. 2017

Entropy

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“…A more fruitful route is the theory of Almost SD set out by Leshno and Levy (2002) and recently further developed by Tzeng, Huang, and Shih (2012). Almost SD places restrictions on the derivatives of the utility function with the purpose of excluding the extreme preferences that prevent exact SD from being established.…”

Section: Appendix Amentioning

confidence: 99%

Spaces for Agreement: A Theory of Time-Stochastic Dominance and an Application to Climate Change

Dietz

Matei²

2016

Journal of the Association of Environmental and Resource Econom

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Many investments involve both a long time horizon and risky returns. Making investment decisions thus requires assumptions about time and risk preferences. Such assumptions are frequently contested, particularly in the public sector, and there is no immediate prospect of universal agreement. Motivated by these observations, we develop a theory and method of finding "spaces for agreement." These are combinations of classes of discount and utility function, for which one investment dominates another (or "almost" does so), so that all those whose preferences can be represented by such combinations would agree on the option to choose. The theory combines the insights of stochastic dominance and time dominance and offers a nonparametric approach to intertemporal, risky choice. We then apply the theory to climate change and show using a popular simulation model that even tough carbon emissions targets would be chosen by almost everyone, barring those with arguably "extreme" preferences.JEL Codes: D61, H43, Q54

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“…For almost SD (ASD), Leshno and Levy (2002) propose a definition, while Tzeng et al (2013) modify it, to provide a further separate definition.…”

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confidence: 99%

Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

2018

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This paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas. KeywordsDecision sciences, economics, finance, business, computing, and big data, theoretical models, econometric and statistical models, applications. JEL Classification AbstractThis paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas.

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Revisiting Almost Second-Degree Stochastic Dominance

Cited by 94 publications

References 11 publications

A New Stochastic Dominance Degree Based on Almost Stochastic Dominance and Its Application in Decision Making

A New Stochastic Dominance Degree Based on Almost Stochastic Dominance and Its Application in Decision Making

Spaces for Agreement: A Theory of Time-Stochastic Dominance and an Application to Climate Change

Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

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