OBEN K. BAYRAK

​When people take decisions under risk, it is not only the expected utility that is important, but also the shape of the distribution of returns: clearly the dispersion is important , but also the skewness. For given mean and dispersion, decision-makers treat positively and negatively skewed prospects differently. This paper presents a new behaviourally-inspired model for decision making under risk, incorporating both dispersion and skewness. We run a horse-race of this new model against seven other models of decision-making under risk, and show that it outperforms many in terms of goodness of fit and, perhaps more importantly, predictive ability. It can incorporate the prominent anomalies of standard theory such as the Allais paradox, the valuation gap, and preference reversals.

Although there are alternative models which can explain the Allais paradox with non-standard preferences, they do not take the emerging evidence on preference imprecision into account. The imprecision is so far incorporated into these models by adding a stochastic specification implying the errors that subjects make. However, there is also the inherent part of the preference imprecision which does not diminish with experience provided in repeated experiments and these stochastic specifications cannot explain a significant portion of the observed behavior in experiments. Moreover, evidence on imprecision suggests that subjects exhibit higher imprecision for a lottery with a higher variance. This paper presents a new model for decision under risk which takes into account the findings of the literature. Looking at the indifference curves predicted by the new model, the new model acts like a mixture of Expected Utility Theory and Rank Dependent Utility Theory depending on which part of the probability triangle the lottery is located.

Using a unique and highly detailed data set of energy consumption at the appliance-level for 390 Swedish households, seemingly unrelated regression (SUR)-based end-use specific load curves are estimated. The estimated load curves are then used to explore possible restrictions on load shifting (e.g. the office hours schedule) as well as the cost implications of different load shift patterns. The cost implications of shifting load from “expensive” to “cheap” hours, using aggregate spot price data, is computed to be very small; roughly 2-5% daily cost reduction from shifting load up to seven hours ahead, indicating small incentives for households (and suppliers) to adopt dynamic pricing of electricity. In addition, end-use-specific income elasticites are also estimated, for the first time for Sweden, using again a SUR framework. The estimated income elasticties are large and significant, varying from a high of 0.8-1.25 for heating to a low of 0.2−0.5 for lighting. Aggregate income elasticity is also high, varying from 0.5 to 0.81. Our results have important implications for Swedish energy policy, in particular for the Swedish government’s stated goal of real time pricing.