Operations Research
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


OPERATIONS RESEARCH
Vol. 55, No. 2, March-April 2007, pp. 226-233
DOI: 10.1287/opre.1060.0343
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Tsetlin, I.
Right arrow Articles by Winkler, R. L.
Right arrow Search for Related Content

Decision Making with Multiattribute Performance Targets: The Impact of Changes in Performance and Target Distributions

Ilia Tsetlin, Robert L. Winkler

INSEAD, 1 Ayer Rajah Avenue, Singapore 138676
Fuqua School of Business, Duke University, Durham, North Carolina 27708-0120

ilia.tsetlin{at}insead.edu
rwinkler{at}duke.edu

In many situations, performance on several attributes is important. Moreover, a decision maker’s utility may depend not on the absolute level of performance on each attribute, but rather on whether that level of performance meets a target, in which case the decision maker is said to be target oriented. For example, typical attributes in new product development include cost, quality, and features, and the corresponding targets might be the best performance on these attributes by competing products. Targets and performance levels typically are uncertain and often are dependent. We develop a model that allows for uncertain dependent targets and uncertain dependent performance levels, and we study implications for decision making in this general multiattribute target-oriented setting. We consider the impact on expected utility of modifying key characteristics of performance (or target) distributions: location, spread, and degree of dependence. In particular, we show that explicit consideration of dependence is important, and we establish when increasing or decreasing dependence is beneficial. We illustrate the results numerically with a normal model and discuss some extensions and implications.

Subject classifications: decision analysis; multiattribute performance targets; utility/preference; multiattribute utility; target-oriented utility.
History: Received July 2005; revision received January 2006; accepted February 2006.







HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 2007 by INFORMS.