Decisions with Uncertainty using Deterministic Analysis

The purpose of this article is to present a simple algorithm for the analysis of decisions with multiple objectives whose measures of effectiveness are random variables. This paper discusses the possibility of using the probability functions: uniform, normal, exponential, Cauchy, Chi-square, Erlang, Gamma, and Laplace. The algorithm, based on the concepts and methodology of Decision Theory, guides the analyst so that he can interact with the decision- maker. First, the analyst asks the decision-maker to define his objectives in the problem he is going to analyze, as well as the measures of effectiveness to evaluate its achievement. Then, he asks questions to the decision-maker to determine his type of behavior: aversion, proneness or neutrality to risk, for each measure of effectiveness. Next, for each of them, calculate its utility function. At that point, he asks the decision-maker to specify the alternatives to be analyzed with their probability function, their range, their mean, and standard deviation.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.