專家區間估計中過度自信現象之研究

Abstract

決策或風險分析中關於不確定性的評估往往需要專家意見的投入，然而過度自信的專家判斷卻可能影響決策的品質。本研究探討專家機率區間估計中過度自信差異的來源，同時，為了修正二元性校準衡量的不穩定問題，我們運用專家主觀機率之期望絕對離差與經由實現值所觀測到的絕對離差來定義新的連續型校準指標，並以線性混合模型來分析與詮釋資料。我們發現專家之間的變異小於問題之間或者真實值所造成的隨機變異。因此，實務上使用專家機率區間判斷時，運用種子問題來篩選較具專業知識或校準程度較高的專家可得到的效果恐將有限。

Expert judgment has been widely applied in the field of decision making and risk assessment. However, when overconfidence reveals in the judgment, it might serious affect decision quality. Thus, this study aims at discussing different sources of overconfidence in an expert's probability interval estimation. To revise the instability which is caused by using binary variables as calibration measuring in previous research, we define a continuous variable as new calibration measurement by applying the ratio between EAD (Expected Absolute Deviation) of experts' subjective probability and MAD (Mean Absolute Deviation) of realization; further, analyze and interpret the data by a simpler linear mixed model. Under the new calibration measurement, we discover that the variance among experts is less than the random variance among questions or realizations. This result has overthrown the analytic outcome of binary calibration measurement. Thus, to use expert judgment in practice, the effect may be limited by adopting seed questions to select a more professional expert or the one in higher calibration level.