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Availability: A heuristic for judging frequency and probability
Abstract
Introduction Much recent research has been concerned with the validity and consistency of frequency and probability judgments. Little is known, however, about the psychological mechanisms by which people evaluate the frequency of classes or the likelihood of events. We propose that when faced with the difficult task of judging probability or frequency, people employ a limited number of heuristics which reduce these judgments to simpler ones. Elsewhere we have analyzed in detail one such heuristic – representativeness. By this heuristic, an event is judged probable to the extent that it represents the essential features of its parent population or generating process. … When judging the probability of an event by representativeness, one compares the essential features of the event to those of the structure from which it originates. In this manner, one estimates probability by assessing similarity or connotative distance. Alternatively, one may estimate probability by assessing availability, or associative distance. Life-long experience has taught us that instances of large classes are recalled better and faster than instances of less frequent classes, that likely occurrences are easier to imagine than unlikely ones, and that associative connections are strengthened when two events frequently co-occur. Thus, a person could estimate the numerosity of a class, the likelihood of an event, or the frequency of co-occurrences by assessing the ease with which the relevant mental operation of retrieval, construction, or association can be carried out.
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Cited by
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Lerman, K., Yan, X., & Wu, X.-Z. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147617 · Cited by 669
The Role of the Affect and Availability Heuristics in Risk Communication
Keller, C., Siegrist, M., & Gutscher, H. (2006) · Risk Analysis · doi:10.1111/j.1539-6924.2006.00773.x · Cited by 551
Lam, S. K., Ahearne, M., Hu, Y., & Schillewaert, N. (2010) · Journal of Marketing · doi:10.1509/jmkg.74.6.128 · Cited by 533
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
Rastogi, C., Zhang, Y., Wei, D., Varshney, K. R., Dhurandhar, A., & Tomsett, R. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512930 · Cited by 216
Metadata
- Authors
- Tversky, A., & Kahneman, D.
- Venue
- Judgment under Uncertainty
- Publisher
- Cambridge University Press
- Published
- 1982-04-30
- Pages
- 163-178
- OpenAlex
- W1469046834
- Cited by
- 1,255
- References
- 0
- FWCI
- 0.34
- Type
- book chapter
- Language
- en
- Open access
- closed
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