著者
犬童 健良
出版者
関東学園大学
雑誌
関東学園大学経済学紀要 (ISSN:13451588)
巻号頁・発行日
vol.39, pp.53-80, 2014-03-31

Maurice Allais (Econometrica, 21, 503-546, 1953) proposed a choice problem designed to question comparison of paired gambles, in order to show that the Expected Utility Theory cannot predict choices by real people. Kahneman and Tversky (Econometrica, 47, 263-291, 1979) proposed Prospect Theory with a solution for this puzzle by using a probability weighting function. However, the shape of the weighting function of the decision-maker may differ between choice problems. Regret Theory, proposed by Bell (Operations Research, 30, 961-981, 1982) and Loomes and Sugden (The Economic Journal, 92, 805-824, 1982), used the notion of (anticipated) regret and rejoice to shed light on the role of cognitive attention on the difference in possible outcomes by not only the actual choice but also the counterfactual choice. In this paper, the Allais paradox is extended slightly, by adding a question on the evaluation of attention strength for each possible joint outcome (called a "cell" in this paper) on a pair of gambles, for each gamble to be chosen. The experimental results showed a persistent tendency for greater attention to regret than rejoice outcomes. Counterfactual outcomes draw more attention than the outcome of the gamble actually chosen. Further, regret cells, that is, joint outcomes of negatively valued differences, draw more attention from the participants who choose riskier options than participants who choose safer options, on average. In addition, by analyzing the monotonicity of the function from attention to choice, a consistent pattern that can explain the experimental results is derived. The monotonicity is argued to mean that the preference is stable for self-conscious decision makers. For the analysis of monotonicity, extraction of logical implications in attentional rank comparison, and so on, Prolog programs have been developed and used.

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この先生の研究いつも面白いので好き。 これはアレのパラドックスにかんする心理実験なんだけど、その個々の結果を集合的な意思決定として集計していて面白い。しかもそれにPrologを使っていて凄い。 http://t.co/GiG3tqsu8X

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