Skip to main content
SpringerLink
Log in

Handling Risk Attitudes for Preference Learning and Intelligent Decision Support

  • Conference paper
  • First Online:
Modeling Decisions for Artificial Intelligence (MDAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9321))

Abstract

Intelligent decision support should allow integrating human knowledge with efficient algorithms for making interpretable and useful recommendations on real world decision problems. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled for understanding and solving the inherent conflict of decision making. Here, risk attitudes are represented by means of fuzzy-linguistic structures, and an interactive methodology is proposed for learning preferences from a group of decision makers (DMs). The methodology is built on a multi-criteria framework allowing imprecise observations/measurements, where DMs reveal their attitudes in linguistic form and receive from the system their associated type, characterized by a preference order of the alternatives, together with the amount of consensus and dissention existing among the group. Following on the system’s feedback, DMs can negotiate on a common attitude while searching for a satisfactory decision.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Cacioppo, J.T., Berntson, G.G.: The affect system, architecture and operating characteristics - current directions. Psycological Sci. 8, 133–137 (1999)

    Google Scholar 

  2. Chiclana, F., Tapia-Garcia, J.M., del Moral, M.J., Herrera-Viedma, E.: A statistical comparative study of different similarity measures of consensus in group decision making. Inf. Sci. 221, 110–123 (2013)

    Article  Google Scholar 

  3. Cook, W.D., Kress, M.: A data envelopment model for aggregating preference rankings. Manag. Sci. 36, 1302–1310 (1990)

    Article  MATH  Google Scholar 

  4. Emond, E.J., Mason, D.W.: A new rank correlation coefficient with application to the consensus ranking problem. J. Multi-Criteria Decis. Anal. 11, 17–28 (2002)

    Article  MATH  Google Scholar 

  5. Fodor, J., Roubens, M.: Fuzzy Preference Modelling and Multicriteria Decision Support. Kluwer Academic Publishers, Dordrecht (1994)

    Book  MATH  Google Scholar 

  6. Franco, C.A.: On the analytic hierarchy process and decision support based on fuzzy-linguistic preference structures. Knowl.-Based Syst. 70, 203–211 (2014)

    Article  Google Scholar 

  7. Franco, C., Hougaard, J.L., Nielsen K.: Ranking alternatives based on imprecise multi-criteria data and pairwise overlap dominance relations. MSAP Working Papers Series (2014)

    Google Scholar 

  8. Grabisch, M., Greco, S., Pirlot, M.: Bipolar and bivariate models in multi-criteria decision analysis: descriptive and constructive approaches. Int. J. Intel. Syst. 23, 930–969 (2008)

    Article  MATH  Google Scholar 

  9. Herrera, F., Alonso, S., Chiclana, F., Herrera-Viedma, E.: Computing with words in decision making: foundations, trends and prospects. Fuzzy Optim. Decis. Making 8, 337–364 (2009)

    Article  MathSciNet  Google Scholar 

  10. Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A model of consensus in group decision making under linguistic assessments. Fuzzy Sets Syst. 78, 73–87 (1996)

    Article  MathSciNet  Google Scholar 

  11. Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8, 746–752 (2000)

    Article  Google Scholar 

  12. Herrera-Viedma, E., Cabrerizo, F.J., Kacprzyk, J., Pedrycz, W.: A review of soft consensus models in a fuzzy environment. Inf. Fusion 17, 4–13 (2014)

    Article  Google Scholar 

  13. Hougaard, J.L., Nielsen, K.: Weighted overlap dominance - a procedure for interactive selection on multidimensional interval data. Appl. Math. Model. 35, 3958–3969 (2011)

    Article  MATH  Google Scholar 

  14. Kacprzyk, J., Zadrozny, S.: Computing with words in decision making through individual and collective linguistic choice rules. Int. J. Uncertainty, Fuzziness and Knowl.-Based Syst. 9, 89–102 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kaplan, K.: On the ambivalence-indifference problem in attitude theory and measurement: a suggested modification of the semantic differential technique. Psychol. Bull. 77, 361–372 (1972)

    Article  Google Scholar 

  16. Martínez, L., Ruan, D., Herrera, F.: Computing with words in decision support systems: An overview on models and applications. Int. J. Comput. Intell. Syst. 3, 382–395 (2010)

    Article  Google Scholar 

  17. Montero, J., Gómez, D., Bustince, H.: On the relevance of some families of fuzzy sets. Fuzzy Sets Syst. 158, 2429–2442 (2007)

    Article  MATH  Google Scholar 

  18. Moraga, C., Trillas, E.: A Computing with Words path from fuzzy logic to natural language. In: Proceedings IFSA-EUSFLAT 2009, pp. 687–692 (2009)

    Google Scholar 

  19. Osgood, Ch., Suci, G., Tannenbaum, P.: The Measurement of Meaning. University of Illinois Press, Urbana (1958)

    Google Scholar 

  20. Roy, B.: The outranking approach and the foundation of ELECTRE methods. Theor. Decis. 31, 49–73 (1991)

    Article  Google Scholar 

  21. Tastle, W.J., Wierman, M.J., Dumdum, U.R.: Ranking ordinal scales using the consensus measure. Issues Inf. Syst. 6, 96–102 (2005)

    Google Scholar 

  22. Le Téno, J.F., Mareschal, B.: An interval version of PROMETHEE for the comparison of building products’ design with ill-defined data on environmental quality. Eur. J. Oper. Res. 109, 522–529 (1998)

    Article  MATH  Google Scholar 

  23. Trillas, E.: On a model for the meaning of predicates - A naïve approach to the genesis of fuzzy sets. Stud. Fuzziness Soft Comput. 243, 175–205 (2009)

    Article  Google Scholar 

  24. Trillas, E., Guadarrama, S.: Fuzzy representations need a careful design. Int. J. General Syst. 39, 329–346 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  25. Trillas, E., Moraga, C.: Reasons for a careful design of fuzzy sets. In: Proceedings EUSFLAT 2013, pp. 140–145 (2013)

    Google Scholar 

  26. Wierman, M.J., Tastle, W.J.: Consensus and dissention: theory and properties. In: Proceedings NAFIPS 2005, pp. 75–79 (2005)

    Google Scholar 

  27. Wu, J., Chiclana, F.: Visual information feedback mechanism and attitudinal prioritisation method for group decision making with triangular fuzzy complementary preference relations. Inf. Sci. 279, 716–734 (2014)

    Article  MathSciNet  Google Scholar 

  28. Yager, R.R.: Fuzzy modeling for intelligent decision making under uncertainty. IEEE Trans. Syst. Man Cybern. 30, 60–70 (2000)

    Article  Google Scholar 

  29. Yager, R.R.: An approach to ordinal decision making. Int. J. Approximate Reasoning 12, 237–261 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  30. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Inf. Sci. 8, 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  31. Zadeh, L.A.: Fuzzy logic and approximate reasoning. Synthese 30, 407–428 (1975)

    Article  MATH  Google Scholar 

  32. Zadeh, L.A.: From computing with numbers to computing with words. From manipulation of measurements to manipulation of perceptions. IEEE Trans. Circ. Syst. 45, 105–119 (1999)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IFRO, Faculty of Science, Copenhagen University, Frederiksberg, Denmark

    Camilo Franco, Jens Leth Hougaard & Kurt Nielsen

Authors
  1. Camilo Franco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jens Leth Hougaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kurt Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Camilo Franco .

Editor information

Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenc Torra

  2. Toho Gakuen, Tokyo, Japan

    Torra Narukawa

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Franco, C., Hougaard, J.L., Nielsen, K. (2015). Handling Risk Attitudes for Preference Learning and Intelligent Decision Support. In: Torra, V., Narukawa, T. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2015. Lecture Notes in Computer Science(), vol 9321. Springer, Cham. https://doi.org/10.1007/978-3-319-23240-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23240-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23239-3

  • Online ISBN: 978-3-319-23240-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation