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recognition primed decision : ウィキペディア英語版 | recognition primed decision
Recognition-primed decision (RPD) is a model of how people make quick, effective decisions when faced with complex situations. In this model, the decision maker is assumed to generate a possible course of action, compare it to the constraints imposed by the situation, and select the first course of action that is not rejected. RPD has been described in diverse groups including ICU nurses, fireground commanders, chess players, and stock market traders. It functions well in conditions of time pressure, and in which information is partial and goals poorly defined. The limitations of RPD include the need for extensive experience among decision-makers (in order to correctly recognize the salient features of a problem and model solutions) and the problem of the failure of recognition and modeling in unusual or misidentified circumstances. It appears to be a valid model for how human decision-makers make decisions. ==Overview== The RPD model identifies a reasonable reaction as the first one that is immediately considered. RPD combines two ways of developing a decision; the first is recognizing which course of action makes sense, and the second, evaluating the course of action through imagination to see if the actions resulting from that decision make sense. However, the difference of being experienced or inexperienced plays a major factor in the decision-making processes. RPD reveals a critical difference between experts and novices when presented with recurring situations. Experienced people will generally be able to come up with quicker decision because the situation may match a prototypical situation they have encountered before. Novices, lacking this experience, must cycle through different possibilities, and tend to use the first course of action that they believe will work. The inexperienced also have the tendencies of using trial and error through their imagination.
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 primed decision」の詳細全文を読む
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