Skip to main content

Robust Assessment of Preference Functions

  • Chapter


A framework for assessing a single decision maker’s preference function of several variables is sketched. The preference function is assumed to be additively decomposable into onedimensional preference functions. All attributes are known prior to the given analysis. The case of probability distributions can basically be dealt with in the same way as the case of certainty. However, in the first case we explore an instability phenomenon which does not exist for sure alternatives. The approach is applied to a real world problem in environmental decision making. Preferences serve as a proxy measure for unobtainable statistical data on damage cost and frequency. We describe this application along an outline of a software system developed to cope with that problem.


  • Utility Function
  • Preference Function
  • Strict Preference
  • Preference Elicitation
  • Hazard Class

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  • Chew, S.H. (1989), ”Axiomatic utility theories with the betweenness property”, Annals of Operations Research 19, 273–298.

    CrossRef  Google Scholar 

  • CPLEX Optimization Inc. (1989), ”Using the CPLEX™ linear optimizer”.

    Google Scholar 

  • Despotis, D.K., Yannacopoulos, D., and Zopounidis, C. (1990), ”A review of the UTA multicriteria method and some improvements”, Foundations of Computing and Decision Science 15, 63–76.

    Google Scholar 

  • Dyer, J.S. and Sarin, R.K. (1979), ”Measurable multiattribute value functions”, Operations Research 27, 810–822.

    CrossRef  Google Scholar 

  • Jacquet-Lagrèze, E. and Siskos, J. (1982), ”Assessing a set of additive utility functions for multicriteria decision-making, the UTA method”, European Journal of Operational Research 10, 151–164.

    CrossRef  Google Scholar 

  • Jacquet-Lagrèze, E., Meziani, R., and Slowinski, R. (1987), ”MOLP with an interactive assessment of a piecewise linear utility function”, European Journal of Operational Research 31, 350–357.

    CrossRef  Google Scholar 

  • Kämpke, T. (1992), Bestimmung von Präferenzfunktionen mittels linearer und nicht- linearer Optimierungsverfahren, in progress.

    Google Scholar 

  • Kämpke, T., Radermacher, F.J., and Wolf, P. (1993), ”Supporting preference elicitation”, Decision Support Systems, to appear.

    Google Scholar 

  • Keeney, R.L. and Raiffa, H. (1976), Decisions with multiple objectives, Wiley, New York.

    Google Scholar 

  • Wolf, P. (1992), Rechnerunterstützte Elizitierung mehrattributiver Präferenzstrukturen, Dissertation, University of Ulm.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 1993 Springer-Verlag Berlin · Heidelberg

About this chapter

Cite this chapter

Kämpke, T., Radermacher, F.J. (1993). Robust Assessment of Preference Functions. In: Diewert, W.E., Spremann, K., Stehling, F. (eds) Mathematical Modelling in Economics. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-78510-8

  • Online ISBN: 978-3-642-78508-5

  • eBook Packages: Springer Book Archive