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Journal articles on the topic 'Probability weighting'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Wang, Di. "Attention-driven probability weighting." Economics Letters 203 (June 2021): 109838. http://dx.doi.org/10.1016/j.econlet.2021.109838.

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2

Prelec, Drazen. "The Probability Weighting Function." Econometrica 66, no. 3 (1998): 497. http://dx.doi.org/10.2307/2998573.

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3

Budescu, David, Ali Abbas, and Lijuan Wu. "Does probability weighting matter in probability elicitation?" Journal of Mathematical Psychology 55, no. 4 (2011): 320–27. http://dx.doi.org/10.1016/j.jmp.2011.04.002.

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4

Charupat, Narat, Richard Deaves, Travis Derouin, Marcelo Klotzle, and Peter Miu. "Emotional balance and probability weighting." Theory and Decision 75, no. 1 (2012): 17–41. http://dx.doi.org/10.1007/s11238-012-9348-x.

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5

Blavatskyy, Pavlo. "Probability weighting and L-moments." European Journal of Operational Research 255, no. 1 (2016): 103–9. http://dx.doi.org/10.1016/j.ejor.2016.05.007.

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6

Halpern, Elkan F. "Behind the Numbers: Inverse Probability Weighting." Radiology 271, no. 3 (2014): 625–28. http://dx.doi.org/10.1148/radiol.14140035.

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7

Spalt, Oliver G. "Probability Weighting and Employee Stock Options." Journal of Financial and Quantitative Analysis 48, no. 4 (2013): 1085–118. http://dx.doi.org/10.1017/s0022109013000380.

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AbstractThis paper documents that riskier firms with higher idiosyncratic volatility grant more stock options to nonexecutive employees. Standard models in the literature cannot easily explain this pattern; a model in which a risk-neutral firm and an employee with prospect theory preferences bargain over the employee's pay package can. The key feature which makes stock options attractive is probability weighting. The model fits the data on option grants well when calibrated using standard parameters from the experimental literature. The results are the first evidence that risky firms can profitably use stock options to cater to an employee demand for long-shot bets.

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8

Blau, Benjamin M., R. Jared DeLisle, and Ryan J. Whitby. "Does Probability Weighting Drive Lottery Preferences?" Journal of Behavioral Finance 21, no. 3 (2019): 233–47. http://dx.doi.org/10.1080/15427560.2019.1672167.

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9

Takahashi, Taiki. "Psychophysics of the probability weighting function." Physica A: Statistical Mechanics and its Applications 390, no. 5 (2011): 902–5. http://dx.doi.org/10.1016/j.physa.2010.10.004.

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10

Skinner, C. J., and D'arrigo. "Inverse probability weighting for clustered nonresponse." Biometrika 98, no. 4 (2011): 953–66. http://dx.doi.org/10.1093/biomet/asr058.

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11

Wu, George, and Richard Gonzalez. "Curvature of the Probability Weighting Function." Management Science 42, no. 12 (1996): 1676–90. http://dx.doi.org/10.1287/mnsc.42.12.1676.

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12

Ma, Xinwei, and Jingshen Wang. "Robust Inference Using Inverse Probability Weighting." Journal of the American Statistical Association 115, no. 532 (2019): 1851–60. http://dx.doi.org/10.1080/01621459.2019.1660173.

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13

Dombi, József, and Tamás Jónás. "Towards a general class of parametric probability weighting functions." Soft Computing 24, no. 21 (2020): 15967–77. http://dx.doi.org/10.1007/s00500-020-05335-3.

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Abstract In this study, we present a novel methodology that can be used to generate parametric probability weighting functions, which play an important role in behavioral economics, by making use of the Dombi modifier operator of continuous-valued logic. Namely, we will show that the modifier operator satisfies the requirements for a probability weighting function. Next, we will demonstrate that the application of the modifier operator can be treated as a general approach to create parametric probability weighting functions including the most important ones such as the Prelec and the Ostaszewski, Green and Myerson (Lattimore, Baker and Witte) probability weighting function families. Also, we will show that the asymptotic probability weighting function induced by the inverse of the so-called epsilon function is none other than the Prelec probability weighting function. Furthermore, we will prove that, by using the modifier operator, other probability weighting functions can be generated from the dual generator functions and from transformed generator functions. Finally, we will show how the modifier operator can be used to generate strictly convex (or concave) probability weighting functions and introduce a method for fitting a generated probability weighting function to empirical data.

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14

Murakami, Hajime, Yuki Tamari, Takashi Ideno, Shigetaka Okubo, and Kazuhisa Takemura. "Probability weighting function in experiment using graphically represented probability information." Journal of Human Environmental Studies 12, no. 1 (2014): 51–56. http://dx.doi.org/10.4189/shes.12.51.

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15

Gelman, Andrew, and Thomas C. Little. "Improving on Probability Weighting for Household Size." Public Opinion Quarterly 62, no. 3 (1998): 398. http://dx.doi.org/10.1086/297852.

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16

Seaman, Shaun R., Ian R. White, Andrew J. Copas, and Leah Li. "Combining Multiple Imputation and Inverse‐Probability Weighting." Biometrics 68, no. 1 (2011): 129–37. http://dx.doi.org/10.1111/j.1541-0420.2011.01666.x.

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17

Delquié, Philippe, and Alessandra Cillo. "Expectations, Disappointment, and Rank-Dependent Probability Weighting." Theory and Decision 60, no. 2-3 (2006): 193–206. http://dx.doi.org/10.1007/s11238-005-4571-3.

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18

McCaffrey, D. F., J. R. Lockwood, and C. M. Setodji. "Inverse probability weighting with error-prone covariates." Biometrika 100, no. 3 (2013): 671–80. http://dx.doi.org/10.1093/biomet/ast022.

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19

Polkovnichenko, Valery, and Feng Zhao. "Probability weighting functions implied in options prices." Journal of Financial Economics 107, no. 3 (2013): 580–609. http://dx.doi.org/10.1016/j.jfineco.2012.09.008.

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20

DeLisle, R. Jared, Dean Diavatopoulos, Andy Fodor, and Kevin Krieger. "Anchoring and Probability Weighting in Option Prices." Journal of Futures Markets 37, no. 6 (2017): 614–38. http://dx.doi.org/10.1002/fut.21833.

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21

Kim, Jae Kwang, and Jay J. Kim. "Nonresponse weighting adjustment using estimated response probability." Canadian Journal of Statistics 35, no. 4 (2007): 501–14. http://dx.doi.org/10.1002/cjs.5550350403.

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22

Yew-Kwang, NG. "Intergenerational Impartiality: Replacing Discounting by Probability Weighting." Journal of Agricultural and Environmental Ethics 18, no. 3 (2005): 237–57. http://dx.doi.org/10.1007/s10806-005-1491-8.

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23

Suter, Renata S., Thorsten Pachur, and Ralph Hertwig. "How Affect Shapes Risky Choice: Distorted Probability Weighting Versus Probability Neglect." Journal of Behavioral Decision Making 29, no. 4 (2015): 437–49. http://dx.doi.org/10.1002/bdm.1888.

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24

Zhou, Yunji, Roland A. Matsouaka, and Laine Thomas. "Propensity score weighting under limited overlap and model misspecification." Statistical Methods in Medical Research 29, no. 12 (2020): 3721–56. http://dx.doi.org/10.1177/0962280220940334.

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Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the propensity score distributions between treatment groups. When these practical violations occur, a small number of highly influential inverse probability weights may lead to unstable inverse probability weighting estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including inverse probability weighting trimming, overlap weights, matching weights, and entropy weights. Because overlap weights, matching weights, and entropy weights target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true propensity score, a common criticism is that these estimators may be more sensitive to misspecifications of the propensity score model. In this paper, we conduct extensive simulation studies to compare the performances of inverse probability weighting and inverse probability weighting trimming against those of overlap weights, matching weights, and entropy weights under limited overlap and misspecified propensity score models. Across the wide range of scenarios we considered, overlap weights, matching weights, and entropy weights consistently outperform inverse probability weighting in terms of bias, root mean squared error, and coverage probability.

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25

Fudenberg, Drew, and Indira Puri. "Simplicity and Probability Weighting in Choice under Risk." AEA Papers and Proceedings 112 (May 1, 2022): 421–25. http://dx.doi.org/10.1257/pandp.20221091.

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We present a speculative application of model estimates from Fudenberg and Puri (2021) to prize-linked savings in South Africa. The models used include one combining simplicity theory (Puri 2018, 2022), a preference for lotteries with fewer possible outcomes, with cumulative prospect theory. The results and those of prior literature indicate that both simplicity and probability weighting have a role to play in understanding behavior in choice under risk. We discuss the properties of these models and their implications for behavior.

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26

Nakamura, Kuninori. "A response time approach to probability weighting function." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 2EV—062–2EV—062. http://dx.doi.org/10.4992/pacjpa.82.0_2ev-062.

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27

Avagyan, Vahe, and Stijn Vansteelandt. "Stable inverse probability weighting estimation for longitudinal studies." Scandinavian Journal of Statistics 48, no. 3 (2021): 1046–67. http://dx.doi.org/10.1111/sjos.12542.

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28

Sjölander, Arvid. "Estimation of attributable fractions using inverse probability weighting." Statistical Methods in Medical Research 20, no. 4 (2010): 415–28. http://dx.doi.org/10.1177/0962280209349880.

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29

Berns, Gregory S., C. Monica Capra, Jonathan Chappelow, Sara Moore, and Charles Noussair. "Nonlinear neurobiological probability weighting functions for aversive outcomes." NeuroImage 39, no. 4 (2008): 2047–57. http://dx.doi.org/10.1016/j.neuroimage.2007.10.028.

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30

van de Kuilen, Gijs. "Subjective Probability Weighting and the Discovered Preference Hypothesis." Theory and Decision 67, no. 1 (2007): 1–22. http://dx.doi.org/10.1007/s11238-007-9080-0.

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31

al-Nowaihi, Ali, and Sanjit Dhami. "A simple derivation of Prelec's probability weighting function." Journal of Mathematical Psychology 50, no. 6 (2006): 521–24. http://dx.doi.org/10.1016/j.jmp.2006.07.006.

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32

Mattos, Fabio, Philip Garcia, and Joost M. E. Pennings. "Probability weighting and loss aversion in futures hedging." Journal of Financial Markets 11, no. 4 (2008): 433–52. http://dx.doi.org/10.1016/j.finmar.2008.04.002.

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33

Gonzalez, Richard, and George Wu. "On the Shape of the Probability Weighting Function." Cognitive Psychology 38, no. 1 (1999): 129–66. http://dx.doi.org/10.1006/cogp.1998.0710.

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34

Henderson, Vicky, David Hobson, and Alex S. L. Tse. "Probability weighting, stop-loss and the disposition effect." Journal of Economic Theory 178 (November 2018): 360–97. http://dx.doi.org/10.1016/j.jet.2018.10.002.

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35

Sheikh, Kazim. "Investigation of selection bias using inverse probability weighting." European Journal of Epidemiology 22, no. 5 (2007): 349–50. http://dx.doi.org/10.1007/s10654-007-9131-4.

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36

Teegavarapu, Ramesh S. V. "Precipitation imputation with probability space-based weighting methods." Journal of Hydrology 581 (February 2020): 124447. http://dx.doi.org/10.1016/j.jhydrol.2019.124447.

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37

Uzhga-Rebrov, Oleg, and Galina Kuleshova. "Probability Weighting in Decision-Making Tasks under Risk." Information Technology and Management Science 25 (December 9, 2022): 49–54. http://dx.doi.org/10.7250/itms-2022-0006.

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The analysis of alternative decisions and the choice of the optimal – in a given sense – decision is an integral part of people’s purposeful activity in all areas of their social life. Many formal approaches have been proposed to solve these problems. One such approach is expected utility theory, which correctly models individuals’ subjective preferences and attitudes to risk. For a very long time this theory was the leading approach for decision making under conditions of risk. However, numerous practical studies have shown its weakness: the theory did not explicitly use subjective perceptions of decision outcome probabilities in optimal decision-making processes. This research has led to the creation and development of approaches to explicitly consider the probabilities of outcomes in decision making. This paper provides a critical analysis of the descriptive properties of expected utility theory and presents various forms of probability weighting functions.

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38

Lee, Myoung-jae, and Sanghyeok Lee. "Double robustness without weighting." Statistics & Probability Letters 146 (March 2019): 175–80. http://dx.doi.org/10.1016/j.spl.2018.11.017.

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39

Berns, Gregory S., C. Monica Capra, Sara Moore, and Charles Noussair. "A shocking experiment: New evidence on probability weighting and common ratio violations." Judgment and Decision Making 2, no. 4 (2007): 234–42. http://dx.doi.org/10.1017/s1930297500000565.

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AbstractWe study whether probability weighting is observed when individuals are presented with a series of choices between lotteries consisting of real non-monetary adverse outcomes, electric shocks. Our estimation of the parameters of the probability weighting function proposed by Tversky and Kahneman (1992) are similar to those obtained in previous studies of lottery choice for negative monetary payoffs and negative hypothetical payoffs. In addition, common ratio violations in choice behavior are widespread. Our results provide evidence that probability weighting is a general phenomenon, independent of the source of disutility.

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40

Sengupta, Srijan, Xiaofeng Shao, and Yingchuan Wang. "The Dependent Random Weighting." Journal of Time Series Analysis 36, no. 3 (2014): 315–26. http://dx.doi.org/10.1111/jtsa.12109.

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41

Bernheim, B. Douglas, Rebecca Royer, and Charles Sprenger. "Robustness of Rank Independence in Risky Choice." AEA Papers and Proceedings 112 (May 1, 2022): 415–20. http://dx.doi.org/10.1257/pandp.20221090.

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Bernheim and Sprenger (2020) devise and implement a novel test of rank-dependent probability weighting both in general and as formulated in cumulative prospect theory. They reject both hypotheses decisively. Cumulative prospect theory cannot simultaneously account for the rank independence of “equalizing reductions” for three-outcome lotteries, which it construes as indicating linear probability weighting, and the relationship between equalizing reductions and probabilities, which it interprets as indicating highly nonlinear probability weighting. In the current paper, we explore the robustness of the first finding, rank independence of equalizing reductions (and hence of decision weights), with respect to alternative experimental procedures.

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42

Rostami, Mehdi, and Olli Saarela. "Normalized Augmented Inverse Probability Weighting with Neural Network Predictions." Entropy 24, no. 2 (2022): 179. http://dx.doi.org/10.3390/e24020179.

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The estimation of average treatment effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are inserted into the ATE estimators such as the augmented inverse probability weighting (AIPW) estimator. Due to the concerns regarding the non-linear or unknown relationships between confounders and the treatment and outcome, there has been interest in applying non-parametric methods such as machine learning (ML) algorithms instead. Some of the literature proposes to use two separate neural networks (NNs) where there is no regularization on the network’s parameters except the stochastic gradient descent (SGD) in the NN’s optimization. Our simulations indicate that the AIPW estimator suffers extensively if no regularization is utilized. We propose the normalization of AIPW (referred to as nAIPW) which can be helpful in some scenarios. nAIPW, provably, has the same properties as AIPW, that is, the double-robustness and orthogonality properties. Further, if the first-step algorithms converge fast enough, under regulatory conditions, nAIPW will be asymptotically normal. We also compare the performance of AIPW and nAIPW in terms of the bias and variance when small to moderate L1 regularization is imposed on the NNs.

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43

Støer, Nathalie,C, and Sven,Ove Samuelsen. "multipleNCC: Inverse Probability Weighting of Nested Case-Control Data." R Journal 8, no. 2 (2016): 5. http://dx.doi.org/10.32614/rj-2016-030.

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44

Barseghyan, Levon, Francesca Molinari, Ted O'Donoghue, and Joshua C. Teitelbaum. "Distinguishing Probability Weighting from Risk Misperceptions in Field Data." American Economic Review 103, no. 3 (2013): 580–85. http://dx.doi.org/10.1257/aer.103.3.580.

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We outline a strategy for distinguishing rank-dependent probability weighting from systematic risk misperceptions in field data. Our strategy relies on singling out a field environment with two key properties: (i) the objects of choice are money lotteries with more than two outcomes; and (ii) the ranking of outcomes differs across lotteries. We first present an abstract model of risky choice that elucidates the identification problem and our strategy. The model has numerous applications, including insurance choices and gambling. We then consider the application of insurance deductible choices and illustrate our strategy using simulated data.

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45

Hernan, Miguel A., Emilie Lanoy, Dominique Costagliola, and James M. Robins. "Comparison of Dynamic Treatment Regimes via Inverse Probability Weighting." Basic Clinical Pharmacology Toxicology 98, no. 3 (2006): 237–42. http://dx.doi.org/10.1111/j.1742-7843.2006.pto_329.x.

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46

Karp, Igor. "Inverse Probability Weighting With Time-varying Confounding and Nonpositivity." Epidemiology 23, no. 1 (2012): 178–79. http://dx.doi.org/10.1097/ede.0b013e31823ac960.

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47

Naimi, Ashley I., Stephen R. Cole, Daniel J. Westreich, and David B. Richardson. "Inverse Probability Weighting With Time-varying Confounding and Nonpositivity." Epidemiology 23, no. 1 (2012): 179. http://dx.doi.org/10.1097/ede.0b013e31823acc73.

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48

Kerr, M. "IMPROVING SENSITIVITY TO WEAK PULSATIONS WITH PHOTON PROBABILITY WEIGHTING." Astrophysical Journal 732, no. 1 (2011): 38. http://dx.doi.org/10.1088/0004-637x/732/1/38.

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49

Hota, Ashish R., and Shreyas Sundaram. "Interdependent Security Games on Networks Under Behavioral Probability Weighting." IEEE Transactions on Control of Network Systems 5, no. 1 (2018): 262–73. http://dx.doi.org/10.1109/tcns.2016.2600484.

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50

dos Santos, Lindomar Soares, Natália Destefano, and Alexandre Souto Martinez. "Decision making generalized by a cumulative probability weighting function." Physica A: Statistical Mechanics and its Applications 490 (January 2018): 250–59. http://dx.doi.org/10.1016/j.physa.2017.08.022.

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