IS

Kim, Choong Nyoung

Research Interests (5)

List of Topics (5)

Topic Weight Topic Terms
0.291 models linear heterogeneity path nonlinear forecasting unobserved alternative modeling methods different dependence paths efficient distribution
0.153 expert systems knowledge knowledge-based human intelligent experts paper problem acquisition base used expertise intelligence domain
0.145 model models process analysis paper management support used environment decision provides based develop use using
0.123 data classification statistical regression mining models neural methods using analysis techniques performance predictive networks accuracy
0.117 strategies strategy based effort paper different findings approach suggest useful choice specific attributes explain effective

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List of Coauthors (1)

Jr., Raymond McLeod 1

Article Keywords (7)

decision strategy 1 human expert 1 inductive learning 1 lens model 1
linear model 1 nonlinear model 1 predictive validity 1

Articles (1)

Expert, Linear Models, and Nonlinear Models of Expert Decision Making in Bankruptcy Prediction: A Lens Model Analysis. (Journal of Management Information Systems, 1999)
Authors: Abstract:
    Analysis of human judgment and decision making provides useful methodologies for examining the human decision process and substantive results. One such methodology is a lens model analysis. The authors used such a model to study how well a model of expert decisions can capture a valid strategy in the decision process. The study also addresses whether a model of an expert can be more accurate than the expert. The predictive accuracy (predictive validity) of two linear (statistical) models and two nonlinear models of human experts is compared. The results indicate that nonlinear models can capture factors (valid nonlinear strategy) that contribute to the experts' predictive accuracy. However, linear models cannot capture the valid nonlinear strategy as well as nonlinear models. One linear model and two nonlinear models performed as well as the overall average of a group of experts. However, all of the models were outperformed by the most accurate expert. By combining validity of decision strategy with characteristics of modeling algorithms, it is possible to explain why certain algorithms perform better than others.