Nothing Special   »   [go: up one dir, main page]

Skip to main content

Decision-Making over Compact Preference Structures

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14506))

  • 552 Accesses

Abstract

We consider a scenario where a user must make a set of correlated decisions and we propose a computational cognitive model of the deliberation process. We assume the user compactly expresses her preferences via soft constraints and we study how a psychology-based model of human decision-making, namely Multi-Alternative Decision Field Theory (MDFT), can be applied in this context. We design and study sequential and synchronous procedures which combine local decision-making on each variable, with constraint propagation, as well as a one-shot approach. Our experimental results, which focus on tree-shaped Fuzzy Constraint Satisfaction Problems, suggest that decomposing the decision process along the preference structure allows to find solutions of high quality in terms of preferences, maintains MDFT’s ability to replicate behavioral effects and is more efficient in terms of computational cost.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

Notes

  1. 1.

    System Specifications: 2.3 GHz 18-Core Intel Xeon W, 256 GB 2666 MHz DDR4.

References

  1. Bartak, R., Morris, R.A., Venable, K.B.: An Introduction to Constraint-Based Temporal Reasoning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers (2011)

    Google Scholar 

  2. Busemeyer, J.R., Diederich, A.: Survey of decision field theory. Math. Soc. Sci. 43(3), 345–370 (2002)

    Article  MathSciNet  Google Scholar 

  3. Busemeyer, J.R., Townsend, J.T.: Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychol. Rev. 100(3), 432 (1993)

    Article  Google Scholar 

  4. Busemeyer, J.R., Gluth, S., Rieskamp, J., Turner, B.M.: Cognitive and neural bases of multi-attribute, multi-alternative, value-based decisions. Trends Cogn. Sci. 23(3), 251–263 (2019)

    Article  Google Scholar 

  5. Gao, J., Lee, J.D.: Extending the decision field theory to model operators’ reliance on automation in supervisory control situations. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 36(5), 943–959 (2006)

    Article  Google Scholar 

  6. Hotaling, J.M., Busemeyer, J.R., Li, J.: Theoretical developments in decision field theory: comment on Tsetsos, Usher, and Chater (2010). Psychol. Rev. (2010)

    Google Scholar 

  7. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction, 1st edn. Cambridge University Press, New York (2010)

    Book  Google Scholar 

  8. Kornhauser, L.A., Sager, L.G.: Unpacking the court. Yale Law J. 96, 82–117 (1986)

    Article  Google Scholar 

  9. Lang, J., Xia, L.: Sequential composition of voting rules in multi-issue domains. Math. Soc. Sci. 57, 304–324 (2009)

    Article  MathSciNet  Google Scholar 

  10. Lee, S., Son, Y.-J., Jin, J.: Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network. Inf. Sci. 178(10), 2297–2314 (2008)

    Article  MathSciNet  Google Scholar 

  11. Meseguer, P., Rossi, F., Schiex, T.: Soft constraints. In: Rossi, F., Van Beek, P., Walsh, T. (eds.) Handbook of Constraint Programming. Elsevier (2005)

    Google Scholar 

  12. Phillips-Wren, G.: Ai tools in decision making support systems: a review. Int. J. Artif. Intell. Tools 21, 04 (2012)

    Article  Google Scholar 

  13. Dalla Pozza, G., Pini, M.S., Rossi, F., Venable, K.B.: Multi-agent soft constraint aggregation via sequential voting. In: Proceedings of IJCAI 2011, pp. 172–177 (2011)

    Google Scholar 

  14. Roe, R., Busemeyer, J.R., Townsend, J.T.: Multi-alternative decision field theory: a dynamic connectionist model of decision-making. Psychol. Rev. 108, 370–392 (2001)

    Article  Google Scholar 

  15. Rossi, F., Venable, K.B., Walsh, T.: A Short Introduction to Preferences: Between Artificial Intelligence and Social Choice. Morgan and Claypool (2011)

    Google Scholar 

  16. Sachan, S., Yang, J.-B., Xu, D.-L., Benavides, D.E., Li, Y.: An explainable AI decision-support-system to automate loan underwriting. Expert Syst. Appl. 144, 113100 (2020)

    Article  Google Scholar 

  17. Tenenbaum, J.B., Gershman, S.J., Malmaud, J.: Structured representations of utility in combinatorial domains. Am. Psychol. Assoc. 4, 67–86 (2017)

    Google Scholar 

  18. Schiex, T.: Possibilistic constraint satisfaction problems or “how to handle soft constraints?” In: Proceedings of UAI 1992, pp. 268–275 (1992)

    Google Scholar 

  19. Shaikh, F., et al.: Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics. Curr. Probl. Diagn. Radiol. 50(2), 262–267 (2021)

    Article  MathSciNet  Google Scholar 

  20. Tsetsos, K., Usher, M., Chater, N.: Preference reversal in multiattribute choice. Psychol. Rev. 117, 1275–1293 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kristen Brent Venable .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martin, A., Venable, K.B. (2024). Decision-Making over Compact Preference Structures. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14506. Springer, Cham. https://doi.org/10.1007/978-3-031-53966-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53966-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53965-7

  • Online ISBN: 978-3-031-53966-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics