Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. th -order stochastic dominance. The basic idea is that a decision maker exhibiting (N + M) th -order stochastic dominance preference will allocate the statecontingent lotteries in such a way as not to group the two "bad" lotteries in the same state, where "bad" is defined via i th -order stochastic dominance. In this way, we can extend and generalize existing results about risk attitudes. This lottery preference includes behavior exhibiting higher order risk effects, such as precautionary effects and tempering effects. Terms of use: Documents in EconStor mayJEL Code: D81.

We develop a continuum of stochastic dominance rules, covering preferences from first- to second-order stochastic dominance. The motivation for such a continuum is that while decision makers have a preference for “more is better,” they are mostly risk averse but cannot assert that they would dislike any risk. For example, situations with targets, aspiration levels, and local convexities in induced utility functions in sequential decision problems may lead to preferences for some risks. We relate our continuum of stochastic dominance rules to utility classes, the corresponding integral conditions, and probability transfers and discuss the usefulness of these interpretations. Several examples involving, e.g., finite-crossing cumulative distribution functions, location-scale families, and induced utility, illustrate the implementation of the framework developed here. Finally, we extend our results to a combined order including convex (risk-taking) stochastic dominance. This paper was accepted by Manel Baucells, decision analysis.

Almost stochastic dominance allows small violations of stochastic dominance rules to avoid situations where most decision makers prefer one alternative to another but stochastic dominance cannot rank them. While the idea behind almost stochastic dominance is quite promising, it has not caught on in practice. Implementation issues and inconsistencies between integral conditions and their associated utility classes contribute to this situation. We develop generalized almost second-degree stochastic dominance and almost second-degree risk in terms of the appropriate utility classes and their corresponding integral conditions, and extend these concepts to higher degrees. We address implementation issues and show that generalized almost stochastic dominance inherits the appealing properties of stochastic dominance. Finally, we define convex generalized almost stochastic dominance to deal with risk-prone preferences. Generalized almost stochastic dominance could be useful in decision analysis, empirical research (e.g., in finance), and theoretical analyses of applied situations.

The analysis of a risky project should take into account not only uncertainties about the return from that project ("project risk"), but also uncertainties associated with other ongoing projects and with exogenous factors that can impact final wealth ("background risk"). The presence of background risk can change the optimal course of action with respect to a project, and ignoring such risk might lead to a poor decision. Most work on background risk assumes that project risk and background risk are independent and are additive in their impact on wealth. However, independence is often unrealistic, and background risk operates in a multiplicative manner in many situations. We relax the independence assumption and consider a model with both additive and multiplicative background risk. The optimal decisions in the correlated setting can be very different than those that would appear optimal if the correlation were ignored. The impact of correlation differs in the additive and multiplicative cases, with positive correlation being beneficial in some cases and negative correlation in others. The analytical and numerical results indicate that in analyzing decision-making problems, it is very important to understand the direction and degree of dependence between project risk and background risks.decision analysis, background risk, correlated risks