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Judgment Under Uncertainty: Heuristics and Biases
Amos Tversky,Daniel Kahneman +1 more
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TLDR
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.Abstract:
This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.read more
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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.
Journal ArticleDOI
Those Who Understand: Knowledge Growth in Teaching
TL;DR: In this paper, Shulman observa la historia de evaluaciones docentes, noting that the evaluación docente parecia preocuparse tanto por los conocimientos, como el siglo anterior se preoccupaba por la pedagogia.
Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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.
References
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Journal ArticleDOI
Availability: A heuristic for judging frequency and probability
Amos Tversky,Daniel Kahneman +1 more
TL;DR: A judgmental heuristic in which a person evaluates the frequency of classes or the probability of events by availability, i.e., by the ease with which relevant instances come to mind, is explored.
Journal ArticleDOI
On the psychology of prediction
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this article, the authors explore the rules that determine intuitive predictions and judgments of confidence and contrast these rules to the normative principles of statistical prediction and show that people do not appear to follow the calculus of chance or the statistical theory of prediction.
Journal ArticleDOI
Subjective Probability: A Judgment of Representativeness
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the subjective probability of an event, or a sample, is determined by the degree to which it is similar in essential characteristics to its parent population and reflects the salient features of the process by which it was generated.
Journal ArticleDOI
Belief in the law of small numbers
Amos Tversky,Daniel Kahneman +1 more
TL;DR: This paper reported that people regard a sample randomly drawn from a population as highly representative, i.e., similar to the population in all essential characteristics, and that the prevalence of the belief and its unfortunate consequences for psychological research are illustrated by the responses of 84 professional psychologists to a questionnaire concerning research decisions.