Neuroeconomics: Yet Another Field Where Rough Sets Can Be Useful?

  • Janusz Kacprzyk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5306)


We deal with neuroeconomics which may be viewed as a new emerging field of research at the crossroads of economics, or decision making, and brain research. Neuroeconomics is basically about neural mechanisms involved in decision making and their economic relations and connotations. We briefly review first the traditional formal approach to decision making, then discuss some experiments of real life decision making processes and point our when and where the results prescribed by the traditional formal models are not confirmed. We deal with both decision analytic and game theoretic type models. Then, we discuss results of brain investigations which indicate which parts of the brain are activated while performing some decision making related courses of action and provide some explanation about possible causes of discrepancies between the results of formal models and experiments. We point out the role of brain segmentation techniques to determine the activation of particular parts of the brain, and point out that the use of some rough sets approaches to brain segmentation, notably by Hassanien, Ślȩzak and their collaborators, can provide useful and effective tool.


Anterior Cingulate Cortex Loss Aversion Ultimatum Game Ambiguity Aversion Trust Game 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Camerer, C.F.: Psychology and economics. Strategizing in the brain 300, 1673–1675 (2003)Google Scholar
  2. 2.
    Camerer, C.F., Loewenstein, G., Prelec, D.: Neuroeconomics: How Neuroscience Can Inform Economics. Journal of Economic Literature XLIII, 9–64 (2005)CrossRefGoogle Scholar
  3. 3.
    Glimcher, P.W.: Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics. MIT Press, Cambridge (2003)Google Scholar
  4. 4.
    Gŭth W., Schmittberger, R., Schwarze, B.: An Experimental Analysis of Ultimatum Bargaining. Journal of Economic Behavior and Organization 3(4), 367–388 (1982)CrossRefGoogle Scholar
  5. 5.
    Hardy-Vallée, B.: Decision-Making: A Neuroeconomic Perspective. Philosophy Compass 2(6), 939–953 (2007)CrossRefGoogle Scholar
  6. 6.
    Hassanien, A.E.: Fuzzy-rough hybrid scheme for breast cancer detection. Image and Computer Vision 25(2), 172–183 (2007)CrossRefGoogle Scholar
  7. 7.
    Hassanien, A.E., Ślȩzak, D.: Rough Neural Intelligent Approach for Image Classification: A Case of Patients with Suspected Breast Cancer. International Journal of Hybrid Intelligent Systems 3/4, 205–218 (2006)MATHGoogle Scholar
  8. 8.
    Kahneman, D.: A Perspective on Judgment and Choice: Mapping Bounded Rationality. American Psychologist 58(9), 697–720 (2003)CrossRefGoogle Scholar
  9. 9.
    Kahneman, D., Slovic, P., Tversky, A. (eds.): Judgment under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge (1982)Google Scholar
  10. 10.
    Kahneman, D., Wakker, P.P., Sarin, R.: Back to Bentham? Explorations of Experienced Utility. The Quarterly Journal of Economics 112(2), 375–397 (1997)CrossRefGoogle Scholar
  11. 11.
    Kenning, P., Plassmann, H.: NeuroEconomics: An Overview from an Economic Prespective. Brain Reserach Nulletin 67, 343–354 (2005)Google Scholar
  12. 12.
    McCabe, K.: Neuroeconomics. In: Nadel, L. (ed.) Encyclopedia of Cognitive Science, pp. 294–298. Wiley, New York (2005)Google Scholar
  13. 13.
    Montague, R., King-Casas, B., Cohen, J.D.: Imaging Valuation Models in Human Choice. Annual Review of Neuroscience 29, 417–448 (2006)CrossRefGoogle Scholar
  14. 14.
    Montague, R., Berns, G.S.: Neural Economics and the Biological Substrates of Valuation. Neuron 36(2), 265–284 (2002)CrossRefGoogle Scholar
  15. 15.
    Naqvi, N., Shiv, B., Bechara, A.: The Role of Emotion in Decision Making: A Cognitive Neuroscience Perspective. Current Directions in Psychological Science 15(5), 260–264 (2006)CrossRefGoogle Scholar
  16. 16.
    Poundstone, W.: Prisoner’s Dilemma. Doubleday, New York (1992)Google Scholar
  17. 17.
    Ramirez, L., Durdle, N.G., Raso, V.J.: Medical image registration in computational intelligence framework: a review. In: Proceedings of IEEE–CCECE 2003: Canadian Conference on Electrical and Computer Engineering, vol. 2, pp. 1021–1024 (2003)Google Scholar
  18. 18.
    Robbins, L.: An Essay on the Nature and Significance of Economic Science. Macmillan, London (1932)Google Scholar
  19. 19.
    Ross, D.: Economic Theory and Cognitive Science: Microexplanation. MIT Press, Cambridge (2005)Google Scholar
  20. 20.
    Rubinstein, A.: Comments on Behavioral Economics. In: Blundell, Newey, W.K., Persson, T. (eds.) Advances in Economic Theory (2005 World Congress of the Econometric Society), vol. II, pp. 246–254. Cambridge University Press, Cambridge (2006)Google Scholar
  21. 21.
    Samuelson, L.: Economic Theory and Experimental Economics. Journal of Economic Literature 43, 65–107 (2005)CrossRefGoogle Scholar
  22. 22.
    Sutton, R.S., Barto, A.G. (eds.): Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning. MIT Press, Cambridge (1998)Google Scholar
  23. 23.
    Tversky, A., Kahneman, D.: Loss Aversion in Riskless Choice: A Reference- Dependent Model. The Quarterly Journal of Economics 106(4), 1039–1061 (1991)CrossRefGoogle Scholar
  24. 24.
    Widz, S., Revett, K., Ślȩzak, D.: A Hybrid Approach to MR Imaging Segmentation Using Unsupervised Clustering and Approximate Reducts. In: Ślȩzak, D., Yao, J.T., Peters, J.F., Ziarko, W., Huo, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 372–382. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  25. 25.
    Widz, S., Revett, K., Ślȩzak, D.: A Rough Set-Based Magnetic Resonance Imaging Partial Volume Detection System. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 756–761. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  26. 26.
    Widz, S., Slezak, D.: Approximation Degrees in Decision Reduct-Based MRI Segmentation. In: FBIT 2007: Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies, pp. 431–436. IEEE Computer Society Press, Los Alamitos (2007)CrossRefGoogle Scholar
  27. 27.
    Zak, P.J.: Neuroeconomics. Philosophical Transactions of the Royal Society of London, Series B, Biological Science 359(1451), 1737–1748 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Janusz Kacprzyk
    • 1
  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland

Personalised recommendations