Harin, Alexander (2021): Behavioral economics. Forbidden zones. New method and models.
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Abstract
A forbidden zone theorem, hypothesis, and applied mathematical method and model are introduced in the present article. The method and model are based on the forbidden zones and hypothesis. The model is uniformly and successfully applied for different domains. The ultimate goal of the research is to solve some generic problems of behavioral economics.
Item Type: | MPRA Paper |
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Original Title: | Behavioral economics. Forbidden zones. New method and models |
Language: | English |
Keywords: | Expectation; Variation; Boundary; Utility; Prospect theory; Behavioral economics; |
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: | 106545 |
Depositing User: | Alexander Harin |
Date Deposited: | 09 Mar 2021 21:38 |
Last Modified: | 09 Mar 2021 21:38 |
References: | [1] Aczél, J., and D. R. Luce (2007). A behavioral condition for Prelec’s weighting function on the positive line without assuming W(1)=1. J. Math. Psychol. 51, 126–129. [2] Aj, A., C. Holt, and A. Smith (2008). Newsvendor "pull-to-center" effect: Adaptive learning in a laboratory experiment. Manufacturing & Service Operations Management 10(4), 590–608. [3] Aldashev, G., T. Carletti, and S. Righi (2011). Follies subdued: Informational efficiency under adaptive expectations and confirmatory bias. Journal of Economic Behavior & Organization 80(1), 110–121. [4] Arnold, L., H. Crauel, and V. Wihstutz (1983). Stabilization of linear systems by noise, SIAM J. Control Optim. 21, 451–461. [5] Atalay, A., H. Bodur, and D. Rasolofoarison (2012). Shining in the center: Central gaze cascade effect on product choice. Journal of Consumer Research 39(4), 848–866. [6] Barbu, V. (2009). The internal stabilization by noise of the linearized Navier-Stokes equation. Contr. Op. Ca. Va. 17 (1), 117–130. [7] Becherer, D. (2006). Bounded solutions to backward SDEs with jumps for utility optimization and indifference hedging. Ann. Appl. Probab. 16(4), 2027–2054. [8] Bhatia, R. and C. Davis (2000). A better bound on the variance, Am. Math. Mon. 107, 353–357. [9] Biagini, S., and M. Frittelli (2008). A unified framework for utility maximization problems: an Orlicz space approach. Ann. Appl. Probab. 18, 929–966. [10] Bowden, M. (2015). A model of information flows and confirmatory bias in financial markets. Decisions in Economics and Finance 38(2), 197–215. [11] Bracha, A., and D. Brown (2012). Affective decision making: A theory of optimism bias. Games and Economic Behavior 75(1), 67–80. [12] Butler, D., and G. Loomes (2007). Imprecision as an Account of the Preference Reversal Phenomenon. Am. Econ. Rev. 97, 277–297. [13] Cerrai, S. (2005). Stabilization by noise for a class of stochastic reaction-diffusion equations. Probab. Theory Rel. 133(2), 190–214. [14] Cheraghchi, M. (2013). Noise-resilient group testing: Limitations and constructions. Discrete Appl. Math. 161(1), 81–95. [15] Chernoff, H. (1981). The identification of an element of a large population in the presence of noise. Ann. Probab. 9, 533–536. [16] Choulli, T., and J. Ma (2017). Explicit description of HARA forward utilities and their optimal portfolios. Theory Probab. Appl. 61(1), 57–93. [17] Coeurdacier, N., and P. Gourinchas (2016). When bonds matter: Home bias in goods and assets. Journal of Monetary Economics 82(C), 119–137. [18] Correa, M., L. Gonzalez-Sabate, I. Serrano (2013). Home bias effect in the management literature. Scientometrics 95(27), 417–433. [19] Dokov, S. P., and D.P. Morton (2005). Second-Order Lower Bounds on the Expectation of a Convex Function. Math. Oper. Res. 30(3), 662–677. [20] Egozcue, M., García, L.F., Wong, W.-K., and R. Zitikis, (2011). The covariance sign of transformed random variables with applications to economics and finance. IMA J. Manag. Math. 22(3), 291–300. [21] Flandoli, F., B. Gess, M. Scheutzow (2017). Synchronization by noise. Probab. Theory Rel. 168(3–4), 511–556. [22] Gaballo, G., and A. Zetlin-Jones (2016). Bailouts, moral hazard and banks' home bias for sovereign debt. Journal of Monetary Economics 81(C), 70–85. [23] Giacomin, G., and C. Poquet (2015). Noise, interaction, nonlinear dynamics and the origin of rhythmic behaviors. Braz. J. Prob. Stat. 29(2), 460–493. [24] Greenacre, L., J. Martin, S. Patrick, and V. Jaeger (2016). Boundaries of the centrality effect during product choice. Journal of Retailing and Consumer Services 32(C), 32–38. [25] Hao, Y., H.H. Chu, K.C. Ko (2016). The 52-week high and momentum in the taiwan stock market: Anchoring or recency biases? International Review of Economics & Finance 43(C), 121–138. [26] Harin, A. (2012). Data dispersion in economics (II) – Inevitability and Consequences of Restrictions. Review of Economics & Finance 2, 24–36. [27] Harin, A. (2014). The random-lottery incentive system. Can p~1 experiments deductions be correct? 16th conference on the Foundations of Utility and Risk, Rotterdam. [28] Harin, A. (2018). Forbidden zones for the expectation. New mathematical results for behavioral and social sciences. preprint, MPRA Paper No. 86650. [29] Hey, J., and C. Orme (1994). Investigating Generalizations of Expected Utility Theory Using Experimental Data. Econometrica 62, 1291–1326. [30] Kahneman, D., and R. Thaler (2006). Anomalies: Utility Maximization and Experienced Utility, J Econ. Perspect. 20(1), 221–234. [31] Kahneman, D., and A. Tversky (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica 47, 263–291. [32] Kumar, D., and N. Goyal (2015). Behavioural biases in investment decision making – a systematic literature review. Qualitative Research in Financial Markets 7(1), 88–108. [33] Madansky, A. (1959). Bounds on the expectation of a convex function of a multivariate random variable. Ann. Math. Stat. 30, 743–746. [34] Meier, S., and C. Sprenger (2010). Present-biased preferences and credit card borrowing. American Economic Journal: Applied Economics 2(1), 193–210. [35] Menapace, L., and R. Raffaelli (2020). Unraveling hypothetical bias in discrete choice experiments. Journal of Economic Behavior & Organization 176(C), 416–430. [36] Moriguti, S. (1952). A lower bound for a probability moment of any absolutely continuous distribution with finite variance. Ann. Math. Stat. 23(2), 286–289. [37] Von Neumann, J., and O. Morgenstern (1944). Theory of games and economic behavior. Princeton: Princeton University Press. [38] Palomino, F. (2010). Psychological bias and gender wage gap. Journal of Economic Behavior & Organization 76(3), 563–573. [39] Penn, J., and W. Hu (2018). Understanding hypothetical bias: An enhanced meta-analysis. American Journal of Agricultural Economics 100(4), 1186–1206. [40] Prékopa, A. (1990). The discrete moment problem and linear programming, Discrete Appl. Math. 27(3), 235–254. [41] Prelec, D. (1998). The Probability Weighting Function. Econometrica 66, 497–527. [42] Scheutzow, M. (1985). Noise can create periodic behavior and stabilize nonlinear diffusions. Stoch. Proc. Appl. 20(2), 323–331. [43] Schoemaker, P., and J. Hershey (1992). Utility measurement: Signal, noise, and bias. Organ. Behav. Hum. Dec. 52, 397–424. [44] Shannon, C. (1949). Communication in the presence of noise. Proc. Institute of Radio Engineers 37(1), 10–21. [45] Sharma, R., and. R. Bhandari (2014). On Variance Upper Bounds for Unimodal Distributions. Commun. Stat. A-Theor. 43(21), 4503–4513. [46] Smith, J. (1971). The information capacity of amplitude- and variance constrained scalar Gaussian channels. Inform. Control 18(3), 203–219. [47] Starmer, C., and R. Sugden (1991). Does the Random-Lottery Incentive System Elicit True Preferences? An Experimental Investigation. Am. Econ. Rev. 81, 971–78. [48] Steingrimsson, R., and R. D. Luce (2007). Empirical evaluation of a model of global psychophysical judgments: IV. Forms for the weighting function. J. Math. Psychol. 51, 29–44. [49] Taylor, M. (2013). Bias and brains: Risk aversion and cognitive ability across real and hypothetical settings. Journal of Risk and Uncertainty 46(3), 299–320. [50] Tekin, B. (2014). Psychological biases and the capital structure decisions: a literature review. Theoretical and Applied Economics XXI(12), 123–142. [51] Thaler, R., (2016). Behavioral Economics: Past, Present, and Future. Am. Econ. Rev. 106(7), 1577–1600. [52] Viscusi, W., and C. Masterman (2017). Anchoring biases in international estimates of the value of a statistical life. Journal of Risk and Uncertainty 54(2), 103–128. [53] Wang, J., X. Wang, X. Zhuang, J. Yang (2017). Optimism bias, portfolio delegation, and economic welfare. Economics Letters 150(C), 111–113. [54] Wang, Y. (2018). Present bias and health. Journal of Risk and Uncertainty 57(2), 177–198. [55] Wolfowitz, J. (1975). Signalling over a Gaussian channel with feedback and autoregressive noise. J. Appl. Probability 12(4), 713–723. [56] Zahera, S., R. Bansal (2018). Do investors exhibit behavioral biases in investment decision making? A systematic review. Qualitative Research in Financial Markets 10(2), 210–251. [57] Zhang, Y., E. Siemsen (2019). A meta-analysis of newsvendor experiments: Revisiting the pull-to-center asymmetry. Production and Operations Management 28(1), 140–156. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/106545 |