D
Daniel Kahneman
Researcher at Princeton University
Publications - 274
Citations - 325486
Daniel Kahneman is an academic researcher from Princeton University. The author has contributed to research in topics: Poison control & Heuristics. The author has an hindex of 135, co-authored 270 publications receiving 307477 citations. Previous affiliations of Daniel Kahneman include Stanford University & University of California.
Papers
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Book ChapterDOI
Prospect theory: an analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
Book
Judgment Under Uncertainty: Heuristics and Biases
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The authors described three heuristics that are employed in making judgements under uncertainty: representativeness, availability of instances or scenarios, and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.
Journal ArticleDOI
Prospect theory: analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
Journal ArticleDOI
The Framing of Decisions and the Psychology of Choice
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The psychological principles that govern the perception of decision problems and the evaluation of probabilities and outcomes produce predictable shifts of preference when the same problem is framed in different ways.
Journal ArticleDOI
Advances in prospect theory: cumulative representation of uncertainty
Amos Tversky,Daniel Kahneman +1 more
TL;DR: Cumulative prospect theory as discussed by the authors applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses, and two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting function.