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Constructing Multiattribute Utility Functions for Decision Analysis 

Presented by Ali Abbas, University of Illinois
January 2014, SDP Webinar - DA Fundamentals Series

A fundamental step in decision analysis is the accurate representation of the decision maker’s preferences. When the decision situation is deterministic, each decision alternative leads to a single prospect (consequence). A prospect may be characterized by one or more attributes, such as health state and wealth. A value function that ranks the prospects is sufficient to rank order the decision alternatives in deterministic decision problems. When uncertainty is present, each alternative may result in a number of possible prospects, each characterized by a number of attributes. A Von Neumann–Morgenstern utility function, defined over the domain of the attributes, is then required for each prospect we face. The best decision alternative is the one with the highest expected utility. This talk presents state of the art methods for constructing multiattribute utility functions in decision analysis and for making trade-offs in decisions with multiple attributes.

Click on the file below to hear a sample of the presentation.  

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Keywords: MUA, climate change, multi-objective multobj, preferential independence

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