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전문가 모델링에서 비선형모형과 선형모형 : 렌즈모형분석
김충녕 한국지능정보시스템학회 1995 지능정보연구 Vol.1 No.2
The field of human judgment and decision making provides useful methodologies for examining the human decision making process and substantive results. One of the methodologies is a lens model analysis which can examine valid nonlinearity in the human decision making process. Using this method, valid nonlinearity in human decision behavior can be successfully detected. Two linear (statistical) models of human experts and two nonlinear models of human experts are compared in terms of predictive accuracy (predictive validity). The results indicate that nonlinear models can capture factors (valid nonlinearity) that contribute to the experts' predictive accuracy, but not factors (inconsistency) that detract from their predictive accuracy. Then, it is argued that nonlinear models can be more accurate than linear models, or as accurate as human experts, especially when human experts employ valid nonlinear strategies in decision making.
A Neural Network Approach to Compare Predictive Value of Accounting Versus Market Data
김충녕(Choong Nyoung Kim),전상경(Sang-gyung Jun),Kinsun Tam(Kinsun Tam) 한국지능정보시스템학회 2004 지능정보연구 Vol.10 No.1
This research compares the use of accounting data versus market data in the prediction of bankruptcy. Comparison is made through neural networks so that prediction accuracy is model-independent. Results of this study indicate that both market and accounting data provide useful information on corporate bankruptcies. Interestingly, using market and accounting information together can achieve substantial gain in prediction accuracy.