Advertisement

Estimating Utility-Functions for Negotiating Agents: Using Conjoint Analysis as an Alternative Approach to Expected Utility Measurement

  • Marc Becker
  • Hans Czap
  • Malte Poppensieker
  • Alexander Stotz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3550)

Abstract

Utility-based software agents are especially suited to represent human principals in recurring automatic negotiation applications. In order to work efficiently, utility-based agents need to obtain models of the relevant part of the principal’s preference structure – represented by utility functions. So far agent theory usually applies expected utility measurement. It has, as we will show, certain shortcomings in real life applications. As an alternative, we suggest an approach based on con-joint analysis, which is a well-understood procedure widely used in marketing research and psychology, but gets only small recognition in agent theory. It offers a user-friendly way to derive quantitative utility values for multi-attribute alternatives from the principal’s preferences. In this paper, we introduce the technique in detail along with some extensions and improvements suited for agent applications. Additionally a learning algorithm is derived, keeping track of changes of the principal’s preference structure and adjusting measurement errors.

Keywords

Utility Function Multiagent System Attribute Level Conjoint Analysis Utility Measurement 
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.

References

  1. 1.
    Addelman, S.: Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments. Technometrics 4(1), 21–46 (1962)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Allais, M.: Le Comportement de l’Homme Rationnel devant le Risque: critique des Postulats et Axiomes de l’Ecole Américaine. Econometrica 21, 503–546 (1953)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Backhaus, K., Erichson, B., Plinke, W., Weiber, R.: Multivariate Analysemethoden – Eine anwendungsorientierte Einführung, Berlin (2000)Google Scholar
  4. 4.
    Brembeck, W., Howell, W.: Persuasion – A Means of Social Influence, 2nd edn., Englewood Cliffs, NJ (1976)Google Scholar
  5. 5.
    Caroll, J.D.: Individual Differences and Multidimensional Scaling. In: Sheppard, R.N., Romney, A.K., Nerlove, S.B. (hrsg.) Multidimensional Scaling: Theory and Applications in the Behavioral Sciences, vol. 1, pp. 105–155 (1972)Google Scholar
  6. 6.
    Chattin, P., Wittink, D.R.: Further Beyond Conjoint Measurement: Towards a Comparison of Methods. In: Perrault, W.D. (ed.) Advances in Consumer Research, Chicago (1977)Google Scholar
  7. 7.
    Czap, H., Becker, M.: Multi-Agent Systems and Microeconomic Theory: A Negotiation Approach to solve Scheduling Problems in High Dynamic Environments. In: Proceedings of 36th Annual Hawaii International Conference on System Sciences (CD-Rom), Hawaii (2003)Google Scholar
  8. 8.
    Gingerenzer, G., Selten, R. (eds.): Bounded Rationality: The Adaptive Toolbox, Cambridge (2001)Google Scholar
  9. 9.
    Green, P.E., Krieger, A.M., Wind, Y.: Thirty Years of Conjoint Analysis: Reflections and Prospects. Inter-faces 31(3), 56–73 (2001)Google Scholar
  10. 10.
    Jacroux, M.: A Note on the Determination and Construction of Minimal Orthogonal Main-Effect Plans. Technometrics 34(1), 92–96 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Kessler, M., Poppensieker, M., Porten, M., Stotz, A., Zub, D.: Lernende Agenten & conjoint-analytische Verfahren - Entwicklung einer Conjoint-Analyse-Software zur Verwendung in FIPA-konformen Multiagentensystemen, Studienprojekt am Lehrstuhl für Wirtschaftsinformatik I der Universität Trier, Trier (2004)Google Scholar
  12. 12.
    Klein, M.: Die Conjoint-Analyse: Eine Einführung in das Verfahren mit einem Ausblick auf mögliche sozialwissenschaftliche Anwendungen. ZA-Information 50, 7–45 (2002)Google Scholar
  13. 13.
    Kruskal, J.B.: Analysis of Factorial Experiments by Estimating Monotone Transformation of Data. Journal of the Royal Statistical Society, Series B 27, 251–263 (1965)MathSciNetGoogle Scholar
  14. 14.
    Kruskal, J.B.: Nonmetric Multidimensional Scaling: A Numerical Approach. Psychometrika 29(2), 1–27 (1964)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Laux, H.: Entscheidungstheorie, Berlin (2003)Google Scholar
  16. 16.
    Luce, R.D., Raiffa, H.: Games and Decisions – Introduction and Critical Survey, New York (1957)Google Scholar
  17. 17.
    Luce, R.D., Tukey, J.W.: Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement. Journal of Mathematical Psychology 1, 1–27 (1964)zbMATHCrossRefGoogle Scholar
  18. 18.
    Oskamp, S.: Attitudes and Opinions, 2nd edn., Englewood Cliffs, NJ (1991)Google Scholar
  19. 19.
    Raghavarao, D.: Constructions and Combinatorial Problems in Design of Experiments, New York (1971)Google Scholar
  20. 20.
    Schiaffino, S., Amandi, A.: User – interface agent interaction: personalization issues. International Journal of Human-Computer Studies 60, 129–148 (2004)CrossRefGoogle Scholar
  21. 21.
    Sen, S., Weiss, G.: Learning in Multiagent Systems. In: Weiss, G. (ed.) Multiagent Systems – A Modern Approach to Distributed Artificial Intelligence, Cambridge (1999)Google Scholar
  22. 22.
    Srinivasan, V., Shocker, A.D.: Estimating the Weight for Multiple Attributes in a Composite Criterion Using Pairwise Judgements. Psychometrika 38, 473–493 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior, Princeton (1942)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Marc Becker
    • 1
  • Hans Czap
    • 1
  • Malte Poppensieker
    • 1
  • Alexander Stotz
    • 1
  1. 1.Department of Business Information Systems IUniversity of TrierTrierGermany

Personalised recommendations