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The representativeness heuristic works by comparing an event to a prototype or stereotype that we already have in mind. For example, if we see a person who is dressed in eccentric clothes and reading a poetry book, we might be more likely to think that they are a poet than an accountant.
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 most common heuristics used are attribute substitution, the availability heuristic, the representativeness heuristic and the anchoring heuristic – these all aid in quick reasoning and work in most situations. Heuristics allow for errors, a price paid to gain efficiency.
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).
The availability heuristic (also known as the availability bias) is the tendency to overestimate the likelihood of events with greater "availability" in memory, which can be influenced by how recent the memories are or how unusual or emotionally charged they may be. [20] The availability heuristic includes or involves the following:
Base-Rate heuristic. The process that involves using common mental shortcuts that help a decision to be made based on known probabilities. For example, if an animal is heard howling in a large city, it is usually assumed to be a dog because the probability that a wolf is in a large city is very low. [25] Peak-and-end heuristic. When past ...
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]