主観確率を含んだ累積プロスペクト理論モデルの推定精度について

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タイトル別名
  • Precision of the Cumulative Prospect Theory Model for Estimating the Subjective Probability
  • シュカン カクリツ オ フクンダ ルイセキ プロスペクト リロン モデル ノ スイテイ セイド ニ ツイテ

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<p>Measurement of subjective probability may theoretically be achieved based on the decision making tasks which require participants to choose between two gambles with known and unknown outcome probabilities. However, this approach is known to suffer from the effect of a human cognitive factor known as the ambiguity aversion. Moreover, because this approach is not based on statistical model, the estimation precision of the subjective probability cannot be evaluated. In the current study, we introduce the cumulative prospect theory model to this problem, and derive its Fisher information matrix. Using this information, we propose an adaptive presentation of the decision making tasks. Simulation studies and an empirical application confirmed that the derived Fisher information corresponds well with the empirical posterior standard deviation, and that the proposed adaptive task selection method performs much better than selecting the tasks at random. Furthermore, adaptive task selection which fixes the rewards of the two gambles was found to perform worse than the unconstrained ones. We conclude that unconstrained adaptive task selection is desirable for measurement of subjective probability under ambiguity.</p>

収録刊行物

  • 行動計量学

    行動計量学 46 (2), 53-71, 2019

    日本行動計量学会

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