Harin, Alexander (2018): Forbidden zones and biases for the expectation of a random variable. Version 2.
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Abstract
A forbidden zones theorem is proven in the present article. If some non-zero lower bound exists for the variance of a random variable, whose support is located in a finite interval, then non-zero bounds or forbidden zones exist for its expectation near the boundaries of the interval. The article is motivated by the need of a theoretical support for the practical analysis of the influence of a noise that was performed for the purposes of behavioral economics, utility and prospect theories, decision and social sciences and psychology. The four main contributions of the present article are: the mathematical support, approach and model those are developed for this analysis and the successful uniform applications of the model in more than one domain. In particular, the approach supposes that subjects decide as if there were some biases of the expectations. Possible general consequences and applications of the theorem for a noise and biases of measurement data are preliminary considered.
Item Type: | MPRA Paper |
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Original Title: | Forbidden zones and biases for the expectation of a random variable. Version 2 |
Language: | English |
Keywords: | probability; variance; noise; bias; utility theory; prospect theory; behavioral economics; decision sciences; measurement; |
Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General D - Microeconomics > D8 - Information, Knowledge, and Uncertainty D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty |
Item ID: | 85607 |
Depositing User: | Alexander Harin |
Date Deposited: | 30 Mar 2018 20:09 |
Last Modified: | 04 Oct 2019 05:07 |
References: | Aczél, J., and D. R. Luce, "A behavioral condition for Prelec’s weighting function on the positive line without assuming W(1)=1", Journal of Mathematical Psychology, 51 (2007), 126–129. Barberis, N.C., 2013 “Thirty Years of Prospect Theory in Economics: A Review and Assessment,” Journal of Economic Perspective, 27 (2013), 173-196. Butler, David, and Graham Loomes, “Imprecision as an Account of the Preference Reversal Phenomenon,” American Economic Review, 97 (2007), 277-297. Chay, K., P. McEwan, and M. Urquiola, “The Central Role of Noise in Evaluating Interventions that Use Test Scores to Rank Schools”, American Economic Review, 95 (2005), 1237-1258. Dokov, S. P., Morton, D.P., 2005. Second-Order Lower Bounds on the Expectation of a Convex Function. Math. Oper. Res. 30(3), 662–677. Harin, A., 2012a, “Data dispersion in economics (I) – Possibility of restrictions,” Review of Economics & Finance, 2 (2012), 59-70. Harin, A., 2012b, “Data dispersion in economics (II) – Inevitability and Consequences of Restrictions,” Review of Economics & Finance, 2 (2012), 24-36. Harin, А. 2013, Data dispersion near the boundaries: can it partially explain the problems of decision and utility theories? Working Papers from HAL No. 00851022, 2013. Harin, A., 2014, “The random-lottery incentive system. Can p~1 experiments deductions be correct?” 16th conference on the Foundations of Utility and Risk, 2014. Harin, A., 2015. General bounds in economics and engineering at data dispersion and risk, Proceedings of the Thirteenth International Scientific School 13, 105–117, in Modeling and Analysis of Safety and Risk in Complex Systems (Saint-Petersburg: IPME RAS). Harin, А. 2017, Can forbidden zones for the expectation explain noise influence in behavioral economics and decision sciences? MPRA Paper No. 76240, 2017. Hey, J., and C. Orme, “Investigating Generalizations of Expected Utility Theory Using Experimental Data,” Econometrica, 62 (1994), 1291-1326. Kahneman, D., and Thaler, R., 2006. Anomalies: Utility Maximization and Experienced Utility, J Econ. Perspect. 20(1), 221–234. Kahneman, D., and A. Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, 47 (1979), 263-291. Moriguti, S., “A lower bound for a probability moment of any absolutely continuous distribution with finite variance,” The Annals of Mathematical Statistics 23(2), 286–289. Pinelis, I., 2011. Exact lower bounds on the exponential moments of truncated random variables, J Appl. Probab. 48(2), 547–560. Prékopa, A., 1990, The discrete moment problem and linear programming, Discrete Appl. Math. 27(3), 235–254. Prékopa, A., 1992. Inequalities on Expectations Based on the Knowledge of Multivariate Moments. Shaked M, Tong YL, eds., Stochastic Inequalities, 309–331, number 22 in Lecture Notes-Monograph Series (Institute of Mathematical Statistics). Prelec, D., “The Probability Weighting Function,” Econometrica, 66 (1998), 497-527. Schoemaker, P., and J. Hershey, “Utility measurement: Signal, noise, and bias,” Organizational Behavior and Human Decision Processes, 52 (1992), 397-424. Starmer, C., & Sugden, R. (1991). Does the Random-Lottery Incentive System Elicit True Preferences? An Experimental Investigation. American Economic Review, 81: 971–78. Steingrimsson, R., and R. D. Luce, “Empirical evaluation of a model of global psychophysical judgments: IV. Forms for the weighting function,” Journal of Mathematical Psychology, 51 (2007), 29–44. Thaler, R., 2016. Behavioral Economics: Past, Present, and Future, American Economic Review. 106(7), 1577–1600. Tversky, A. and D., Kahneman “Prospect Theory: Advances in Prospect Theory: Cumulative Representation of Uncertainty,” Journal of Risk and Uncertainty, 5 (1992), 297-323. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/85607 |