Kurt Carlson Wins Award for Best Publication in Decision Analysis

August 29, 2012

In recognition of his research on why people are unable to clearly identify decision objectives, the Informs Decision Analysis Society honored Kurt A. Carlson, associate professor of marketing at Georgetown McDonough and research director of the Georgetown Institute for Consumer Research, and his co-authors with the 2012 Decision Analysis Publication Award. The award recognizes the best article or book on “decision analysis, broadly defined,” which includes such topics as theoretical work on decision analysis methodology, descriptions of applications, and experimental studies. All works considered must have been published in the 2010 calendar year.

The winning paper was “Improving the Generation of Decision Objectives,” which was published in the September 2010 issue of Decision Analysis. In addition to Carlson, Samuel D. Bond and Ralph L. Keeney were co-authors of the research. “This work opens up a new field in descriptive decision analysis, namely the study of the ability of decision makers to express their values and objectives,” said Jason R. W. Merrick, Decision Analysis Society Committee Chair and a professor at Virginia Commonwealth University, in an announcement about the award. “It also has a great impact on prescriptive decision analysis.

Defining values and objectives is the first stage in any good decision analysis application and this work suggests new ways to approach this important sate of the structuring process.” The research suggests that when determining decision objectives, individuals do not think broadly enough about the range of relevant objectives or deeply enough to articulate every objective within the range considered. In their experiments to explore how to stimulate a more comprehensive set of objectives, the authors found that organizing objectives by category and receiving direct challenges to do better with a warning that important objectives are missing were most effective in improving the quantity of objectives generated without impacting their quality.

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