Weighted Scoring Rules and Convex Risk Measures

In Weighted Scoring Rules and Convex Risk Measures, Dr. Zachary J. Smith and Prof. J. Eric Bickel (both at the University of Texas at Austin) present a general connection between weighted proper scoring rules and investment decisions involving the minimization of a convex risk measure. Weighted scoring rules are quantitative tools for evaluating the accuracy of probabilistic forecasts relative to a baseline distribution. In their paper, the authors demonstrate that the relationship between convex risk measures and weighted scoring rules relates closely with previous economic characterizations of weighted scores based on expected utility maximization. As illustrative examples, the authors study two families of weighted scoring rules based on phi-divergences (generalizations of the Weighted Power and Weighted Pseudospherical Scoring rules) along with their corresponding risk measures. The paper will be of particular interest to the decision analysis and mathematical finance communities as well as those interested in the elicitation and evaluation of subjective probabilistic forecasts.

Portfolio optimization model with uncertain returns based on prospect theory

When investing in new stocks, it is difficult to predict returns and risks in a general way without the support of historical data. Therefore, a portfolio optimization model with an uncertain rate of return is proposed. On this basis, prospect theory is used for reference, and then the uncertain return portfolio optimization model is established from the perspective of expected utility maximization. An improved gray wolf optimization (GWO) algorithm is designed because of the complex nonsmooth and nonconcave characteristics of the model. The results show that the GWO algorithm is superior to the traditional particle swarm optimization algorithm and genetic algorithm.

Adventures of human planners in Maze Search Task

Humans make efficient plans during everydaynavigation and natural spatial search, while these tasks still remainchallenging for algorithms. Which mental computationalmodels do we have that makes this possible? We investigatethree computational principles that may be leveraged by people— approximate expected utility maximization, discountedutility, and probability weighed utility—in the context of a novelspatial Maze Search Task. These computational principles arewell studied in classic bandit tasks and monetary gambles, butthey have not been evaluated on naturalistic spatial tasks thatinvolve sequential decision making. We found that accountingfor a combined effect of these three principles explains aggregatehuman behavior better than models that include justone, or two of these principles, or any of the four behavioralheuristics. We also found substantial individual differences,revealing that humans are best explained by a diversity ofplanning strategies rather than a single best model. Our resultstake a step toward uncovering common computational qualitiesof human spatial planning that may generalize to naturalhuman behaviors in daily life.

Reasoning about social preferences with uncertain beliefs

We propose a computational model of social preference judgments that accounts for the degree of an agents’ uncertainty about the preferences of others. Underlying this model is the principle that, in the face of social uncertainty, people interpret social agents’ behavior under an assumption of expected utility maximization. We evaluate our model in two experiments which each test a different kind of social preference reasoning: predicting social choices given information about social preferences, and inferring social preferences after observing social choices. The results support our model and highlight how un- certainty influences our social judgments.

Theoretical Research on the Impact of Country Risk on Insurance Demand

Derive the optimal premium expenditure of the insured from the consumer expected utility maximization model. On this basis, the concept of country risk is introduced, and derive the optimal premium expenditure of the insured in the presence of country risk is. Then compare the optimal premium expenditure of the insured when there is country risk and when there is no country risk, and study the impact of country risk on insurance demand.

The Impact of Behavioral Finance on Lebanese Investors’ Decision Making

The inability of the traditional expected utility maximization of rational investors (within the efficient markets Framework) to explain many empirical patterns; was the main stimulus beyond the appearance for another track to resolve and analyze this inconsistency; through combining behavioral and cognitive psychological theory together where Behavioral Finance is known mainly as the irrational part that deals with investors’ Sentimental side. Furthermore it can be agreed on that behavioral finance is considered as “Subjective judgement” where ideas and decisions cannot be transmitted to other’s knowledge, as each one acts from his own point of interest; unlike conventional finance paradigm. Therefore, this paper seeks to determine the main behavioral errors or biases that are faced mainly by the Lebanese individual investor during decision making process. Results obtained by analyzing 211 questionnaires through SPSS software to develop a Structural equation model. Findings prove that Over Confidence and regret aversion are the main behavioral biases that control the Lebanese individual investors’ decision making.

Conclusion

Recognizing that an act’s risk is a consequence of the act yields a version of expected-utility maximization that does not need adjustments for risk in addition to the probabilities and utilities of possible outcomes. This treatment of an act’s risk justifies the expected-utility principle, and the mean-risk principle, for evaluation of an act. Rational attitudes to risks explain the rationality of acting in accord with the principles. They ground the separability relations that support the principles. The expected-utility principle justifies a substantive, and not just a representational, version of the decision principle of expected-utility maximization. Consequently, the principle governs a single choice and not just sets of choices. It demands more than consistency of the choices in a set. It demands that each choice follow the agent’s preferences, and these preferences explain the rationality of a choice that complies with the principle.