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A heuristic device is used when an entity X exists to enable understanding of, or knowledge concerning, some other entity Y. A good example is a model that, as it is never identical with what it models, is a heuristic device to enable understanding of what it models. Stories, metaphors, etc., can also be termed heuristic in this sense.
Heuristics (from Ancient Greek εὑρίσκω, heurískō, "I find, discover") is the process by which humans use mental shortcuts to arrive at decisions. Heuristics are simple strategies that humans, animals, [1] [2] [3] organizations, [4] and even machines [5] use to quickly form judgments, make decisions, and find solutions to complex problems.
The theory of attribute substitution unifies a number of separate explanations of reasoning errors in terms of cognitive heuristics. [1] In turn, the theory is subsumed by an effort-reduction framework proposed by Anuj K. Shah and Daniel M. Oppenheimer , which states that people use a variety of techniques to reduce the effort of making decisions.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity.
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:
Field theory is centered around the idea that a person's life space determines their behavior. [2] Thus, the equation was also expressed as B = f ( L ), where L is the life space. [ 4 ] In Lewin's book, he first presents the equation as B = f ( S ), where behavior is a function of the whole situation ( S ). [ 5 ]
The priority heuristic correctly predicted the majority choice in all (one-stage) gambles in Kahneman and Tversky (1979). Across four different data sets with a total of 260 problems, the heuristic predicted the majority choice better than (a) cumulative prospect theory , (b) two other modifications of expected utility theory , and (c) ten well ...
A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper ...