The magnitude of errors in proximal multiattribute decision analysis with probabilistically dependent attributes

Article Abstract:

Kirkwood's (1992) approximate analysis method is examined in terms of its accuracy in assessing alternatives under uncertainty with multiple evaluation characteristics. This approximation procedure is based on Howard's (1971) proximal approach and uses only the first two moments of the probability distributions for the alternatives. Because of this, the Kirkwood model has the potential to considerably reduce the amount of information needed to compare alternatives in the presence of probabilistic dependence among evaluation attributes. Findings show that the approximation method is accurate under certain conditions representative of numerous decision analysis applications. These conditions are discussed.

Decision-making, Decision making, Analysis, Approximation theory, Approximation

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Estimating the impact of uncertainty on a deterministic multiattribute evaluation

Article Abstract:

A new procedure for approximating the effects of uncertainty on the results of a multiattribute evaluation is presented. The approximation procedure permits the consideration of the impact of uncertainty even without the completion of a full probabilistic multiattribute utility analysis. The approximation procedure is relatively simple and, in many cases, can be used with electronic spreadsheet programs. An illustrative application is also provided to test the procedure's accuracy.

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Risk, return, skewness and preference

Article Abstract:

The assumption that a link exists between general moment preference and expected utility theory for risk-averse investors selecting from a choice of projects is examined and proven to be incorrect. The assumption, widely believed to be true for individual decision makers faced with a choice of projects occuring in a situation that allows arbitrary returns, is disproved theoretically using an analytical method derived from the Tchebychev system of functions.

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