Stochastic dominance constraints allow a decision-maker to manage risk in an optimization setting by requiring their decision to yield a random outcome which stochastically dominates a reference random outcome. We present new integer and linear programming formulations for optimization under first and second-order stochastic dominance constraints, respectively. These formulations are more compa...

Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers have thus extended DCOPs to Stochastic DCOPs (SDCOPs), where rewards are sampled from known proba...

This article reviews recent findings that violate a broad class of descriptive theories of decision making. A new study compared 1,224 participants tested via the Internet and 124 undergraduates tested in the laboratory. Both samples confirmed systematic violations of stochastic dominance and cumulative independence; new tests also found violations of coalescing. The Internet sample was older, ...

Statistical inferences under second order stochastic dominance for two sample case has a long and rich history. But the k(≥ 2) sample case has not been well studied. In this article we consider k(≥ 2) sample test for the equality of distribution functions against second order stochastic dominance alternative. A test statistic is constructed with isotonic regression estimates of stop-loss transf...

We derive empirical tests for stochastic dominance that allow for diversification between choice alternatives. The tests can be computed using straightforward linear programming. Bootstrapping techniques and asymptotic distribution theory can approximate the sampling properties of the test results and allow for statistical inference. Our results could provide a stimulus to the further prolifera...

Almost stochastic dominance (ASD) as proposed by Leshno and Levy (2002) has been widely applied in decision theory and in practice. It can help to identify the preferred distribution for the majority of decision makers when a small violation in stochastic dominance is involved. The purpose of this paper is to experimentally define the preference parameters in the set of most decision makers. By...

In this journal, Best, Best and Yoder (2000) recently demonstrated that portfolios of US value stocks dominate portfolios of US growth stocks in terms of second-order stochastic dominance (SSD). However, we cannot conclude from this finding that the market is SSD inefficient, because market portfolio efficiency generally does not require growth portfolios to be efficient. Furthermore, stochasti...

In this paper, we extend Fishburn’s convex stochastic dominance theorem to include any distribution function. This paper also considers risk takers as well as risk averters, and discusses third order stochastic dominance. We apply separation and representation theorems to obtain a concise alternative proof of the theorem. Our results are used to extend a theorem of Bawa et.al. on comparison bet...

Stochastic dominance is a partial order on risky assets (“gambles”) that is based on the uniform preference, of all decision-makers (in an appropriate class), for one gamble over another. We modify this, first, by taking into account the status quo (given by the current wealth) and the possibility of rejecting gambles, and second, by comparing rejections that are substantive (that is, uniform o...