On the shape of the probability weighting function.

By Richard Gonzalez, George Wu. 1999

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Citations in the corpus

Implicit Quantile Networks for Distributional Reinforcement Learning (2018, background): “The ﬁrst one is the cumulative probability weighting parameterization proposed in cumulative prospect theory (Tversky & Kahneman, 1992; Gonzalez & Wu, 1999): .”
Violations of betweenness or random errors (2006, background): “Hey and Orme (1994, p.1301) and Gonzalez and Wu (1999) propose an additive, normally distributed error term on the utility scale.”
Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences (2012, methodology): “The estimation algorithm provides a weight for each monetary amount and each probability in the data without any assumptions of specific parametric functional forms (see Gonzalez and Wu (1999) for a similar procedure).”
Reference-Dependent Preferences: Evidence from Marathon Runners (2014, methodology): “We do so because we can reference the large literature on measurement of the prospect theory value function (e.g., Abdellaoui, Bleichrodt, and Paraschiv, 2007; Gonzalez and Wu, 1999; Tversky and Kahneman, 1992).”
A Comparison of Five Models that Predict Violations of First-Order Stochastic Dominance in Risky Decision Making (2005, background): “…Quiggin, 1985; 1993), rank and sign dependent utility (RSDU) theory (Luce, 2000; Luce and Fishburn, 1991; 1995), cumulative prospect (CPT) theory (Gonzalez and Wu, 1999; Starmer and Sugden, 1989; Tversky and Kahneman, 1992; Tversky and Wakker, 1995; Wakker and Tversky, 1993; Wu and Gonzalez,…”
Gender, Financial Risk, and Probability Weights (2006, background): “The parameters have a neat psychological interpretation (Gonzalez and Wu, 1999): γ reflects a subject’s responsiveness to changes in probability: the smaller γ , the less responsive.”
Explaining heterogeneity in utility functions by individual differences in decision modes (2006, background): “…of scope yield successively smaller increments of value) and inversely s-shaped, which is usually interpreted as risk-averse decision behavior when gambling for monetary gains and as risk-seeking behavior in gambles with loss outcomes (Abedellaoui, 2000; Gonzalez & Wu, 1999; Tversky & Fox, 1995).”
Causes of Allais common consequence paradoxes: An experimental dissection ☆ (2004, background): “…variations of the Allais paradoxes (Birnbaum, 2000, 2001b; Birnbaum & Martin, 2003; Wu & Gonzalez, 1996, 1998), as well as other data that can be fit with the inverse-S weighting function (Abdellaoui, 2000; Gonzalez & Wu, 1999; Tversky & Wakker, 1995; Quiggin, 1993; Luce, 2000; Starmer, 2000).”
Rationality on the rise: Why relative risk aversion increases with stake size (2010, background): “(Hey and Orme 1994; Gonzalez and Wu 1999; Cohen and Einav 2007), is substantive in the sense that a single preference model is unable to adequately describe behavior.”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “Fitting by one-parameter Prelec function (435) (black), two-parameter Prelec function (green), and linear-in-log-odds function (red) (187, 305).”
Parameter-Free Elicitation of Utility and Probability Weighting Functions (2000, background): “The introduction of two weighting functions in CPT seems more appropriate than duality (assumed in RDEU) for taking into account the observed tendency of individuals to treat probabilities differently when 21 Bleichrodt and Pinto (1998) and Gonzalez and Wu (1999) obtained similar results.”
Small feedback‐based decisions and their limited correspondence to description‐based decisions (2003, background): “Careful quantification of this effect shows that the average decision maker behaves as if she overweighs probabilities below 0.25 (see a recent quantification of this effect in Gonzalez & Wu, 1999).”