Sources of Indeterminacy in von Neumann-Morgenstern Utility Functions

Utility functions are an important component of normative decision analysis. They also serve to characterize the nature of people's risk-taking attitudes. In this paper we examine various factors that make it difficult to speak of the utility function for a given person. Similarly we show that it is questionable to pool data across studies (for descriptive purposes) that differ in the elicitation methods employed. The following five sources of indeterminacy are specifically discussed. First, the certainty equivalence method generally yields more risk-seeking preferences than the probability equivalence method. Second, the probability and outcome levels used in reference lotteries induce systematic bias. Third, combining gain and loss domains yields different utility measures than separate examinations of the two domains. Fourth, whether a risk is assumed or transferred away exerts a significant influence on people's preferences in ways counter to expected utility theory. Finally, context or framing differences strongly affect choice in a non-normative manner. The above five factors are first discussed as essential choices to be made by the decision scientist in constructing Von Neumann-Morgenstern utility functions. Next, each is examined separately in view of existing literature, and demonstrated via experiments. The emerging picture is that basic preferences under uncertainty exhibit serious incompatibilities with traditional expected utility theory. An important implication of this paper is to commence development of a systematic theory of utility encoding which incorporates the many information processing effects that influence people's expressed risk preferences

Early identification of problem interactions: A tool-supported approach

The principle of "divide and conquer" suggests that complex software problems should be decomposed into simpler problems, and those problems should be solved before considering how they can be composed. The eventual composition may fail if solutions to simpler problems interact in unexpected ways. However, early identification of concrete scenarios where interactions happen remains an outstanding issue. In this paper, we propose that logical abduction can be used to efficiently identify all possible failure scenarios when the composition cannot be achieved fully. We present an tool-supported framework that (i) provides a simple diagramming editor for drawing problem diagrams and describing them using the Event Calculus, (ii) structures the Event Calculus formulae of individual problem diagrams for the abduction procedure, (iii) communicates with an off-the-shelf abductive reasoner in the background and relates the results of the abduction procedure to the problem diagrams. With this tool, it becomes possible to highlight at an early stage, problem diagrams that will interact when composed together. The proposed theory and the tool framework are illustrated with an interaction problem from the smart home application