Abstract
Traditional decision theory assumes that for every two alternatives, people always make the same (deterministic) choice. In practice, people’s choices are often probabilistic, especially for similar alternatives: the same decision maker can sometimes select one of them and sometimes the other one. In many practical situations, an adequate description of this probabilistic choice can be provided by a logit model proposed by 2001 Nobelist D. McFadden. In this model, the probability of selecting an alternative a is proportional to \(\exp (\beta \cdot u(a))\), where u(a) is the alternative’s utility. Recently, however, empirical evidence appeared that shows that in some situations, we need to go beyond McFadden’s formulas. In this paper, we use natural symmetries to come up with an appropriate generalization of McFadden’s formulas.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aczél J, Dhombres J (2008) Functional equations in several variables. Camridge University Press, Cambridge, UK
Fishburn PC (1969) Utility theory for decision making. Wiley Inc., New York
Jakubczyk M (2016) Estimating the membership function of the fuzzy willingness-to-pay/accept for health via Bayesin modeling. In: Proceedings of the 6th world conference on soft computing, Berkeley, California, 22–25 May 2016
Luce D (2005) Inividual choice behavior: a theoretical analysis. Dover, New York
Luce RD, Raiffa R (1989) Games and decisions: introduction and critical survey. Dover, New York
McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142
McFadden D (2001) Economic choices. Am Econ Rev 91:351–378
Nguyen HT, Kosheleva O, Kreinovich V (2009) Decision making beyond Arrow’s ‘impossibility theorem’, with the analysis of effects of collusion and mutual attraction. Int J Intell Syst 24(1):27–47
Raiffa H (1970) Decision analysis. Addison-Wesley, Reading
Train K (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge
Acknowledgements
This work was supported by Chiang Mai University, Thailand. This work was also supported in part by the National Science Foundation grants HRD-0734825 and HRD-1242122 (Cyber-ShARE Center of Excellence) and DUE-0926721, and by an award “UTEP and Prudential Actuarial Science Academy and Pipeline Initiative” from Prudential Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Kosheleva, O., Kreinovich, V., Sriboonchitta, S. (2017). Econometric Models of Probabilistic Choice: Beyond McFadden’s Formulas. In: Kreinovich, V., Sriboonchitta, S., Huynh, VN. (eds) Robustness in Econometrics. Studies in Computational Intelligence, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-319-50742-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-50742-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50741-5
Online ISBN: 978-3-319-50742-2
eBook Packages: EngineeringEngineering (R0)