Advances in prospect theory: Cumulative representation of uncertainty

By Amos Tversky, Daniel Kahneman. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Implicit Quantile Networks for Distributional Reinforcement Learning (2018, background): “Stated in terms of the cumulative probability functions, αF This axiom in particular has troubled many researchers because it is consistently violated by human behavior (Tversky & Kahneman, 1992).”
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010, background): “Prospect theory, for example, employs utility models based on relation to a reference point, based on evidence that the human perceptual apparatus is attuned to evaluate differences rather than absolute magnitudes [Kahneman and Tversky, 1979, Tversky and Kahneman, 1992].”
Loss aversion and mental accounting: the favorite-longshot bias in parimutuel betting (2012, unknown): “The prospect theory value function, v(·), is defined for present purposes as a segmented power function with three parameters (Kahneman and Tversky, 1992):”
Recursive expected utility and the separation of attitudes towards risk and ambiguity: an experimental study (2009, background): “…first studied in Kahneman and Tversky (1979) extends to ambiguity as well [as reported in many studies including Hogarth and Einhorn (1990) and Tversky and Kahneman (1992)], and whether the phenomenon of reflection across gains and losses is a stable individual characteristic or is specific…”
"I Think I Can, I Think I Can": Overconfidence and Entrepreneurial Behavior (2007, background): “In addition, since most individuals are not only risk averse but also ambiguity averse (Ellsberg, 1961; Tversky and Kahneman, 1992) knowing other entrepreneurs may increase the propensity of an individual to start a business.”
Disentangling compensation and employment risks using the behavioral agency model (2007, background): “Conversely, individuals anticipating a gain to wealth become increasingly risk averse with increases in the probability of anticipated gains (see Tversky and Kahneman, 1992).”
Migration, Risk, and Uncertainty: Theoretical Perspectives (2012, background): “1”
An eye‐tracking study on information processing in risky decisions: Evidence for compensatory strategies based on automatic processes (2011, methodology): “The CPT proposes the following equations for both functions (for non-negative outcomes): vðxÞ ¼ xa (2) and pðpÞ ¼ pg=ðpg þ ð1 pÞgÞ1=g (3) Tversky and Kahneman (1992) estimated the parameters a¼ 0.88 and g ¼ 0.61.”
Prospect Theory, Indifference Curves, and Hedging Risks (2010, unknown): “Kahneman and Tversky [12] and Tversky and Kahneman [30] put forward arguments in favor of S-shaped utility (or value) functions that are, by definition, concave for gains and convex for losses.”
Divergence between individual perceptions and objective indicators of tail risks: Evidence from floodplain residents in New York City (2015, result): “…whether individual decisions are consistent with the probability-weighting function of prospect theory, with mixed results (for instance, see Tversky and Kahneman 1992; Bordalo, Gennaioli, and Shleifer 2012; Fehr-Duda and Epper 2012; Barberis 2013b; Barseghyan et al. 2013a,b; Carman and…”
A Genuine Rank-Dependent Generalization of the Von Neumann-Morgenstern Expected Utility Theorem (2002, background): “…is ample evidence that the weighting function in RDU is inverse S-shaped, concave over the first third of the unit interval and convex elsewhere (Tversky and Kahneman (1992); Camerer and Ho (1994); Wu and Gonzalez (1996); Gonzalez ansd Wu (1999); Abdellaoui (2000); Bleichrodt and Pinto (2000)).”
The Genetics of Investment Biases (2014, background): “These behaviors have been partially attributed to various psychological mechanisms: Ambiguity aversion and familiarity for lack of diversification (Ellsberg, 1961; Heath and Tversky, 1991), overconfidence and sensationseeking for excessive trading (Griffin and Tversky, 1992), loss aversion and menta”
Violations of betweenness or random errors (2006, background): “However, when Camerer and Ho fit different stochastic theories to the same dataset, a disappointment aversion theory (e.g., Gul, 1991), which is a betweenness theory, accommodates data better than cumulative prospect theory, which violates betweenness (e.g., Tversky and Kahneman, 1992).”
It is Whether You Win or Lose: The Importance of the Overall Probabilities of Winning or Losing in Risky Choice (2005, methodology): “CPT suggests that you would add value to the most extreme outcome (worst, −$65, or best, $100) given the parameters for the weighting and value functions reported by Tversky and Kahneman (1992).”
Consistency of Higher Order Risk Preferences (2012, unknown): “We also show how the evidence can be used to support (or not support) other preference models as well, such as moment preference and cumulative prospect theory (Tversky and Kahneman (1992)).”
Distributional reinforcement learning in the brain (2020, background): “While PPCs, for example, support Bayesian inference [73,77], quantile codes could support simple implementations [8] of cumulative prospect theory [78], which provides a descriptive model of human and animal behavior [79].”
The importance of faith: Tax morale and religiosity (2003, result): “This argumentation is in line with the prospect theory, which argues that people evaluate utility gains and losses not according to an absolute change but relative to a reference point (see, e.g. Kahneman and Tversky, 1979; Tversky and Kahneman, 1992).”