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The representativeness heuristic is simply described as assessing similarity of objects and organizing them based around the category prototype (e.g., like goes with like, and causes and effects should resemble each other). [2] This heuristic is used because it is an easy computation. [4]
The representativeness heuristic is seen when people use categories, for example when deciding whether or not a person is a criminal. An individual thing has a high representativeness for a category if it is very similar to a prototype of that category. When people categorise things on the basis of representativeness, they are using the ...
The representativeness heuristic is a special case of availability. It stipulates that abstract base-rate information plays little role in quantitative judgments about event populations. Instead, these judgments are based on the sample of more concrete exemplars that are available to the individual at the time of decision making.
The similarity heuristic is very easy to observe in the world of business, both from a marketing standpoint and from the position of the consumer. People tend to let past experience shape their world view; thus, if something presents itself as similar to a good experience had in the past, it is likely that the individual will partake in the current experience.
Gigerenzer & Gaissmaier (2011) state that sub-sets of strategy include heuristics, regression analysis, and Bayesian inference. [14]A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7).
Due to the availability heuristic, names that are more easily available are more likely to be recalled, and can thus alter judgments of probability. [31] Another example of the availability heuristic and exemplars would be seeing a shark in the ocean. Seeing a shark has a greater impact on an individual's memory than seeing a dolphin.
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The peak–end rule is an elaboration on the snapshot model of remembered utility proposed by Barbara Fredrickson and Daniel Kahneman.This model dictates that an event is not judged by the entirety of an experience, but by prototypical moments (or snapshots) as a result of the representativeness heuristic. [1]