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Risky Prospects (risky + prospect)
Selected AbstractsContingent application of the cancellation editing operation: the role of semantic relatedness between risky outcomesJOURNAL OF BEHAVIORAL DECISION MAKING, Issue 2 2004Nicolao Bonini Abstract This article presents findings on the restructuring component of the decision process. Two experiments are described employing hypothetical vacation choice dilemmas. The aim was to explore the conditions under which outcomes common to two risky prospects with the same probabilities of occurrence are or are not cancelled and how consequent decisions are influenced. The design of the options presented to participants was based on pilot work to establish appropriate contexts. The key independent variable was the semantic relatedness between outcomes of the same risky prospect. The main finding was that the participants did not cancel the outcome shared by two prospects when it was semantically related to another outcome within the same prospect. In this case, the prospect with greater risk was chosen significantly more frequently in comparison to when the common outcome was unrelated to other outcomes. An interpretation of the findings is presented in terms of contingent editing processes. Copyright © 2004 John Wiley & Sons, Ltd. [source] Non-Monotonicity of the Tversky-Kahneman Probability-Weighting Function: A Cautionary NoteEUROPEAN FINANCIAL MANAGEMENT, Issue 3 2008Jonathan Ingersoll C91; D10; D81; G19 Abstract Cumulative Prospect Theory has gained a great deal of support as an alternative to Expected Utility Theory as it accounts for a number of anomalies in the observed behavior of economic agents. Expected Utility Theory uses a utility function and subjective or objective probabilities to compare risky prospects. Cumulative Prospect Theory alters both of these aspects. The concave utility function is replaced by a loss-averse utility function and probabilities are replaced by decision weights. The latter are determined with a weighting function applied to the cumulative probability of the outcomes. Several different probability weighting functions have been suggested. The two most popular are the original proposal of Tversky and Kahneman and the compound-invariant form proposed by Prelec. This note shows that the Tversky-Kahneman probability weighting function is not increasing for all parameter values and therefore can assign negative decision weights to some outcomes. This in turn implies that Cumulative Prospect Theory could make choices not consistent with first-order stochastic dominance. [source] Contingent application of the cancellation editing operation: the role of semantic relatedness between risky outcomesJOURNAL OF BEHAVIORAL DECISION MAKING, Issue 2 2004Nicolao Bonini Abstract This article presents findings on the restructuring component of the decision process. Two experiments are described employing hypothetical vacation choice dilemmas. The aim was to explore the conditions under which outcomes common to two risky prospects with the same probabilities of occurrence are or are not cancelled and how consequent decisions are influenced. The design of the options presented to participants was based on pilot work to establish appropriate contexts. The key independent variable was the semantic relatedness between outcomes of the same risky prospect. The main finding was that the participants did not cancel the outcome shared by two prospects when it was semantically related to another outcome within the same prospect. In this case, the prospect with greater risk was chosen significantly more frequently in comparison to when the common outcome was unrelated to other outcomes. An interpretation of the findings is presented in terms of contingent editing processes. Copyright © 2004 John Wiley & Sons, Ltd. [source] Stochastic efficiency analysis with risk aversion bounds: a commentAUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 3 2010J. B. Hardaker A recent contribution by Meyer et al. (2009, p. 521) corrected an error of fact by Hardaker et al. (2004b, p. 253) about the comparison between stochastic dominance with respect to a function (SDRF) and stochastic efficiency with respect to a function (SERF). While both methods compare risky prospects for a bounded range of degrees of risk aversion, SERF, unlike SDRF, also demands an assumption that a chosen measure of risk aversion is constant over all levels of outcomes being evaluated. It is argued that it is generally reasonable to make such an assumption, especially when the form of the utility function and the bounds on the degree of risk aversion are carefully chosen. Then SERF has the advantage that it can lead to a smaller efficient set than that identified by SDRF. SERF also has advantages of ease and transparency in use. [source] | ||||||||||