Skip to main content
SpringerLink
Log in

Automated Preference Elicitation for Decision Making

  • Chapter
Decision Making and Imperfection

Part of the book series: Studies in Computational Intelligence ((SCI,volume 474))

Abstract

In the contemporary complex world decisions are made by an imperfect participant devoting limited deliberation resources to any decision-making task. A normative decision-making (DM) theory should provide support systems allowing such a participant to make rational decisions in spite of the limited resources. Efficiency of the support systems depends on the interfaces enabling a participant to benefit from the support while exploiting the gradually accumulating knowledge about DM environment and respecting incomplete, possibly changing, participant’s DM preferences. The insufficiently elaborated preference elicitation makes even the best DM supports of a limited use. This chapter proposes a methodology of automatic eliciting of a quantitative DM preference description, discusses the options made and sketches open research problems. The proposed elicitation serves to fully probabilistic design, which includes a standard Bayesian decision making.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abramowitz, M., Stegun, I.: Handbook of Mathematical Functions. Dover Publications, New York (1972)

    Google Scholar 

  2. Barndorff-Nielsen, O.: Information and exponential families in statistical theory, New York (1978)

    Google Scholar 

  3. Berec, L., Kárný, M.: Identification of reality in Bayesian context. In: Warwick, K., Kárný, M. (eds.) Computer-Intensive Methods in Control and Signal Processing: Curse of Dimensionality, Birkhäuser, Boston, pp. 181–193 (1997)

    Google Scholar 

  4. Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1985)

    MATH  Google Scholar 

  5. Bernardo, J.M.: Expected information as expected utility. The Annals of Statistics 7(3), 686–690 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bohlin, T.: Interactive System Identification: Prospects and Pitfalls. Springer, New York (1991)

    Book  MATH  Google Scholar 

  7. Boutilier, C.: A POMDP formulation of preference elicitation problems. In: Biundo, S. (ed.) AAAI-2002 Proc. of the Fifth European Conference on Planning, pp. 239–246. AAAI Press, Durham (2002)

    Google Scholar 

  8. Boutilier, C., Brafman, R., Geib, C., Poole, D.: A constraint-based approach to preference elicitation and decision making. In: Proceedings of AAAI Spring Symposium on Qualitative Decision Theory, Stanford, CA, pp. 19–28 (1997)

    Google Scholar 

  9. Bowong, S., Dimi, J., Kamgang, J., Mbang, J., Tewa, J.: Survey of recent results of multi-compartments intra-host models of malaria and HIV. Revue ARIMA 9, 85–107 (2008)

    MathSciNet  Google Scholar 

  10. Chajewska, U., Koller, D.: Utilities as random variables: Density estimation and structure discovery. In: Proceedings of UAI 2000, pp. 63–71 (2000)

    Google Scholar 

  11. Cooke, N.: Varieties of knowledge elicitation techniques. International Journal of Human-Computer Studies 41, 801–849 (1994)

    Article  MATH  Google Scholar 

  12. Feldbaum, A.: Theory of dual control. Autom. Remote Control 21(9) (1960)

    Google Scholar 

  13. Gajos, K., Weld, D.: Preference elicitation for interface optimization. In: Proceedings of UIST 2005 (2005)

    Google Scholar 

  14. Garthwaite, P., Kadane, J., O´Hagan, A.: Statistical methods for eliciting probability distributions. J. of the American Statistical Association 100(470), 680–700 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  15. Guy, T.V., Kárný, M., Wolpert, D.H. (eds.): Decision Making with Imperfect Decision Makers. ISRL, vol. 28. Springer, Heidelberg (2012)

    MATH  Google Scholar 

  16. Viappiani, H.P., Boutilier, S.Z., Learning, C.: complex concepts using crowdsourcing: A Bayesian approach. In: Proceedings of the Second Conference on Algorithmic Decision Theory (ADT 2011), Piscataway, NJ (2011)

    Google Scholar 

  17. Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, New York (1994)

    MATH  Google Scholar 

  18. Horst, R., Tuy, H.: Global Optimization, p. 727. Springer (1996)

    Google Scholar 

  19. Jacobs, O., Patchell, J.: Caution and probing in stochastic control. Int. J. of Control 16, 189–199 (1972)

    Article  MATH  Google Scholar 

  20. Jaynes, E.: Information theory and statistical mechanics. Physical Review Series II 106(4), 620–630 (1957)

    MathSciNet  MATH  Google Scholar 

  21. Jimison, H., Fagan, L., Shachter, R., Shortliffe, E.: Patient-specific explanation in models of chronic disease. AI in Medicine 4, 191–205 (1992)

    Google Scholar 

  22. Jorgensen, S.B., Hangos, K.M.: Qualitative models as unifying modelling tool for grey box modelling. Int. J. of Adaptive Control and Signal Processing 9(6), 461–562 (1995)

    Article  MathSciNet  Google Scholar 

  23. Kárný, M.: Towards fully probabilistic control design. Automatica 32(12), 1719–1722 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  24. Kárný, M., Andrýsek, J., Bodini, A., Guy, T., Kracík, J., Nedoma, P., Ruggeri, F.: Fully probabilistic knowledge expression and incorporation. Tech. Rep. 8-10MI, CNR IMATI, Milan (2008)

    Google Scholar 

  25. Kárný, M., Andrýsek, J., Bodini, A., Guy, T.V., Kracík, J., Ruggeri, F.: How to exploit external model of data for parameter estimation? Int. J. of Adaptive Control and Signal Processing 20(1), 41–50 (2006)

    Article  MATH  Google Scholar 

  26. Kárný, M., Böhm, J., Guy, T.V., Jirsa, L., Nagy, I., Nedoma, P., Tesař, L.: Optimized Bayesian Dynamic Advising: Theory and Algorithms. Springer, London (2006)

    Google Scholar 

  27. Kárný, M., Guy, T.: Preference elicitation in fully probabilistic design of decision strategies. In: Proc. of the 49th IEEE Conference on Decision and Control. IEEE (2010)

    Google Scholar 

  28. Kárný, M., Guy, T.V.: On Support of Imperfect Bayesian Participants. In: Guy, T.V., Kárný, M., Wolpert, D.H. (eds.) Decision Making with Imperfect Decision Makers. ISRL, vol. 28, pp. 29–56. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  29. Kárný, M., Guy, T.V.: Fully probabilistic control design. Systems & Control Letters 55(4), 259–265 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  30. Kárný, M., Halousková, A., Böhm, J., Kulhavý, R., Nedoma, P.: Design of linear quadratic adaptive control: Theory and algorithms for practice. Kybernetika 21(Supplement to Nos. 3, 4 ,5, 6) (1985)

    Google Scholar 

  31. Kárný, M., Kroupa, T.: Axiomatisation of fully probabilistic design. Information Sciences 186(1), 105–113 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  32. Kárný, M., Kulhavý, R.: Structure determination of regression-type models for adaptive prediction and control. In: Spall, J. (ed.) Bayesian Analysis of Time Series and Dynamic Models. ch.12, Marcel Dekker, New York (1988)

    Google Scholar 

  33. Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. JohnWiley and Sons Inc. (1976)

    Google Scholar 

  34. Koopman, R.: On distributions admitting a sufficient statistic. Tran. of American Mathematical Society 39, 399 (1936)

    Article  MathSciNet  Google Scholar 

  35. Kuhn, H., Tucker, A.: Nonlinear programming. In: Proc. of the 2nd Berkeley Symposium, pp. 481–492. University of California Press, Berkeley (1951)

    Google Scholar 

  36. Kulhavý, R.: Can we preserve the structure of recursive Bayesian estimation in a limited-dimensional implementation? In: Helmke, U., Mennicken, R., Saurer, J. (eds.) Systems and Networks: Mathematical Theory and Applications, vol. I, pp. 251–272. Akademie Verlag, Berlin (1994)

    Google Scholar 

  37. Kulhavý, R., Zarrop, M.B.: On a general concept of forgetting. Int. J. of Control 58(4), 905–924 (1993)

    Article  MATH  Google Scholar 

  38. Kullback, S., Leibler, R.: On information and sufficiency. Annals of Mathematical Statistics 22, 79–87 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  39. Kumar, P.: A survey on some results in stochastic adaptive control. SIAM J. Control and Applications 23, 399–409 (1985)

    Google Scholar 

  40. Lainiotis, D.: Partitioned estimation algorithms, i: Nonlinear estimation. Information Sciences 7, 203–235 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  41. Linden, G., Hanks, S., Lesh, N.: Interactive assessment of user preference models: The automated travel assistant. In: Proceedings of User Modelling 1997 (1997)

    Google Scholar 

  42. Loeve, M.: Probability Theory. van Nostrand, Princeton (1962), Russian translation, Moscow

    Google Scholar 

  43. Nagy, I., Suzdaleva, E., Kárný, M., Mlynářová, T.: Bayesian estimation of dynamic finite mixtures. Int. Journal of Adaptive Control and Signal Processing 25(9), 765–787 (2011)

    Article  MATH  Google Scholar 

  44. Nelsen, R.: An Introduction to Copulas. Springer, New York (1999)

    Book  MATH  Google Scholar 

  45. O’Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J., Rakow, T.: Uncertain judgement: eliciting experts’ probabilities. John Wiley & Sons (2006)

    Google Scholar 

  46. Osborne, M., Rubinstein, A.: A course in game theory. MIT Press (1994)

    Google Scholar 

  47. Peterka, V.: Bayesian system identification. In: Eykhoff, P. (ed.) Trends and Progress in System Identification, pp. 239–304. Pergamon Press, Oxford (1981)

    Google Scholar 

  48. Rao, M.: Measure Theory and Integration. John Wiley, New York (1987)

    MATH  Google Scholar 

  49. Savage, L.: Foundations of Statistics. Wiley, New York (1954)

    MATH  Google Scholar 

  50. Shi, R., MacGregor, J.: Modelling of dynamic systems using latent variable and subspace methods. J. of Chemometrics 14(5-6), 423–439 (2000)

    Article  Google Scholar 

  51. Shore, J., Johnson, R.: Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Tran. on Information Theory 26(1), 26–37 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  52. Si, J., Barto, A., Powell, W., Wunsch, D. (eds.): Handbook of Learning and Approximate Dynamic Programming. Wiley-IEEE Press, Danvers (2004)

    Google Scholar 

  53. Šindelář, J., Vajda, I., Kárný, M.: Stochastic control optimal in the Kullback sense. Kybernetika 44(1), 53–60 (2008)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod vodáLarenskou věží 4, 182 08, Prague 8, Czech Republic

    Miroslav Kárný

Authors
  1. Miroslav Kárný
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miroslav Kárný .

Editor information

Editors and Affiliations

  1. Theory and Automation, Academy of Sciences, Institute of Information, Pod vodarenskou vezi 4, Prague, 182 08, Czech Republic

    Tatiana V. Guy

  2. and Automation, Academy of Sciences, Institute of Information Theory, Pod vodarenskou vezi 4, Prague, Czech Republic

    Miroslav Karny

  3. Los Alamos National Laboratory, MS B256, Los Alamos, 87545, USA

    David Wolpert

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Kárný, M. (2013). Automated Preference Elicitation for Decision Making. In: Guy, T., Karny, M., Wolpert, D. (eds) Decision Making and Imperfection. Studies in Computational Intelligence, vol 474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36406-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36406-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36405-1

  • Online ISBN: 978-3-642-36406-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation