Skip to main content
SpringerLink
Log in
Book cover

International Conference on Algorithmic Decision Theory

ADT 2015: Algorithmic Decision Theory pp 567–572Cite as

Measuring Intrinsic Quality of Human Decisions

  • Conference paper
  • First Online:

  • 1246 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9346))

Abstract

Decision making is an integral part of artificial intelligence.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Andrich, D.: Rasch Models for Measurement. Sage Publications, Beverly Hills (1988)

    Book  Google Scholar 

  2. Biswas, T.: Designing intelligent agents to judge intrinsic quality of human decisions. In: Proceedings of International Conference on Agents and Artificial Intelligence (ICAART) (2015)

    Google Scholar 

  3. Biswas, T., Regan, K.: Quantifying depth and complexity of thinking and knowledge. In: Proceedings of International Conference on Agents and Artificial Intelligence (ICAART) (2015)

    Google Scholar 

  4. 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 

  5. Chabris, C., Hearst, E.: Visualization, pattern recognition, and forward search: effects of playing speed and sight of the position on grandmaster chess errors. Cogn. Sci. 27, 637–648 (2003)

    Article  Google Scholar 

  6. DiFatta, G., Haworth, G.M\(^c\)C., Regan, K.: Skill rating by Bayesian inference. In: Proceedings of 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009), pp. 89–94, Nashville, TN, 30 March – 2 April 2009 (2009)

    Google Scholar 

  7. Haworth, G., Biswas, T., Regan, K.: A comparative review of skill assessment: performance, prediction and profiling. In: Proceedings of International Conference on Advances in Computer Games (ACG 2014) (2015)

    Google Scholar 

  8. Stahl II, D.O., Wilson, P.W.: Experimental evidence on players’ models of other players. J. Econ. Behav. Organ. 25(3), 309–327 (1994)

    Article  Google Scholar 

  9. Van Der Maas, H.L.J., Wagenmakers, E.J.: A psychometric analysis of chess expertise. Am. J. Psychol. 118, 29–60 (2005)

    Google Scholar 

  10. Morris, G.A., Branum-Martin, L., Harshman, N., Baker, S.D., Mazur, E., Dutta, S., Mzoughi, T., McCauley, V.: Testing the test: item response curves and test quality. Am. J. Phys. 74(5), 449–453 (2006)

    Article  Google Scholar 

  11. Rasch, G.: Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research, Copenhagen (1960)

    Google Scholar 

  12. Regan, K., Biswas, T., Zhou, J.: Human and computer preferences at chess. In: Proceedings of the 8th Multidisciplinary Workshop on Advances in Preference Handling (MPref 2014) (2014)

    Google Scholar 

  13. Regan, K., Haworth, G.M\(^c\)C.: Haworth intrinsic chess ratings. In: Proceedings of AAAI 2011, San Francisco (2011)

    Google Scholar 

  14. Regan, K.W., Macieja, B., Haworth, G.M.C.: Understanding distributions of chess performances. In: van den Herik, H.J., Plaat, A. (eds.) ACG 2011. LNCS, vol. 7168, pp. 230–243. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  15. Regan, K., Biswas, T.: Psychometric modeling of decision making via game play. In: Proceedings of IEEE Conference on Computational Intelligence in Games (2013)

    Google Scholar 

  16. Thorpe, G.L., Favia, A.: Data analysis using item response theory methodology: an introduction to selected programs and applications. Psychol. Fac. Sch. (2012). Paper 20. http://digitalcommons.library.umaine.edu/psy_facpub/20

Download references

Author information

Authors and Affiliations

  1. Department of CSE, University at Buffalo, Amherst, NY, 14260, USA

    Tamal T. Biswas

Authors
  1. Tamal T. Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tamal T. Biswas .

Editor information

Editors and Affiliations

  1. University of New South Wales, Kensington, Australia

    Toby Walsh

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Biswas, T.T. (2015). Measuring Intrinsic Quality of Human Decisions. In: Walsh, T. (eds) Algorithmic Decision Theory. ADT 2015. Lecture Notes in Computer Science(), vol 9346. Springer, Cham. https://doi.org/10.1007/978-3-319-23114-3_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23114-3_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23113-6

  • Online ISBN: 978-3-319-23114-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation