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The recognition heuristic, originally termed the recognition principle, has been used as a model in the psychology of judgment and decision making and as a heuristic in artificial intelligence. The goal is to make inferences about a criterion that is not directly accessible to the decision maker, based on recognition retrieved from memory. This is possible in an environment (reference class) R where the recognition of alternatives a, b ɛ R positively correlates with their criterion values. For two alternatives, the heuristic is defined as:〔 (Full text (PDF) ).〕 The recognition heuristic is part of the “adaptive toolbox” of “fast and frugal” heuristics proposed by Gigerenzer and Goldstein. It is one of the most frugal of these, meaning it is simple or economical.〔 In their original experiment, Daniel Goldstein and Gerd Gigerenzer quizzed students in Germany and the United States on the populations of both German and American cities. Participants received pairs of city names and had to indicate which city has more inhabitants. In this and similar experiments, the recognition heuristic typically describes about 80-90% of participants’ choices, in cases where they recognize one but not the other object (see criticism of this measure below). Surprisingly, American students scored higher on German cities, while German participants scored higher on American cities, despite only recognizing a fraction of the foreign cities. This has been labeled the “Less-is-more-Effect” and mathematically formalized.〔Katsikopoulos, K. V. (2010). The less-is-more effect: Predictions and tests" ''Judgment and Decision Making'', Vol. 5, No. 4, July 2010, pp. 244-257.〕 == Domain Specificity == The recognition heuristic is posited as a domain specific strategy for inference. It is ecologically rational to rely on the recognition heuristic in domains where there is a correlation between the criterion and recognition. The higher the recognition validity α for a given criterion, the more ecologically rational it is to rely on this heuristic and the more likely people will rely on it. For each individual, α can be computed by :α = C/(C+W) where C is the number of correct inferences the recognition heuristic would make, computed across all pairs in which one alternative is recognized and the other is not, and W is the number of wrong inferences. Domains in which the recognition heuristic was successfully applied include the prediction of geographical properties (such as the size of cities, mountains, etc.),〔〔 of sports events (such as Wimbledon and soccer championships〔Serwe S, Frings C. 2006. Who will win Wimbledon? The recognition heuristic in predicting sports events. J. Behav. Decis. Mak. 19:321-32.〕〔Scheibehenne B, Bröder A. 2007. Predicting Wimbledon 2005 tennis results by mere player name recognition. Int. J. Forecast. 23:415-26.〕〔Pachur, T., Biele, G. 2007. Forecasting from ignorance: the use and usefulness of recognition in lay predictions of sports events. Acta Psychol. 125:99-116.〕) and elections. Research also shows that the recognition heuristic is relevant to marketing science. Recognition based heuristics help consumers choose which brands to buy in frequently purchased categories. A number of studies addressed the question of whether people rely on the recognition heuristic in an ecologically rational way. For instance, name recognition of Swiss cities is a valid predictor of their population (α = 0.86) but not their distance from the center of Switzerland (α = 0.51). Pohl〔Pohl R. 2006. Empirical tests or the recognition heuristic. J. Behav. Decis. Mak. 19:251-71.〕 reported that 89% of inferences accorded with the model in judgments of population, compared to only 54% in judgments of the distance. More generally, there is a positive correlation of r = 0.64 between the recognition validity and the proportion of judgments consistent with the recognition heuristic across 11 studies.〔Pachur T, Todd PM, Gigerenzer G, Schooler LJ, Goldstein DG. 2010. When is the recognition heuristic an adaptive tool? In Ecological Rationality: Intelligence in the World, ed. PM Todd, G Gigerenzer, ABC Res. Group. New York: Oxford Univ. Press.〕 Another study by Pachur suggested that the recognition heuristic is more likely a tool for exploring natural rather than induced recognition (i.e. not provoked in a laboratory setting) when inferences have to be made from memory. In one of his experiments, the results showed that there was a difference between participants in an experimental setting vs. a non-experimental setting. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Recognition heuristic」の詳細全文を読む スポンサード リンク
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