Artificial Intelligence in Economics and Managment

1. Front Matter

   Pages i-x

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2. Artificial Intelligence Techniques

   1. Front Matter

      Pages 1-1

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   2. Using Machine Learning, Neural Networks and Statistics to Predict Corporate Bankruptcy: A Comparative Study

      • P. P. M. Pompe, A. J. Feelders

      Pages 3-19

   3. Prolog Business Objects in a Three-Tier Architecture

      • David G. Schwartz

      Pages 21-31

   4. The Effect of Training Data Set Size and the Complexity of the Separation Function on Neural Network Classification Capability: The Two-Group Case

      • Moshe Leshno, Yishay Spector

      Pages 33-50

   5. Imaginal Agents

      • David G. Schwartz, Dov Te’eni

      Pages 51-59

3. Financial Applications

   1. Front Matter

      Pages 61-61

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   2. Financial Product Representation and Development Using a Rule-Based System

      • Anja Lange, Juergen Seitz, Eberhard Stickel

      Pages 63-75

   3. Applications of Artificial Intelligence and Cognitive Science Techniques in Banking

      • Philippe Lenca

      Pages 77-90

4. Business Applications

   1. Front Matter

      Pages 91-91

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   2. AI-Supported Quality Function Deployment

      • Yoram Reich

      Pages 93-106

   3. Knowledge Reuse in Mass Customization of Knowledge-Intensive Services

      • Michel Benaroch

      Pages 107-127

   4. Harvest Optimization of Citrus Crop Using Genetic Algorithms

      • Nissan Levin, Jacob Zahavi

      Pages 129-138

   5. “CORPUS,” An Approach to Capitalizing Company Knowledge

      • Michel Grundstein

      Pages 139-152

5. Economic Applications

   1. Front Matter

      Pages 153-153

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   2. Fuzzy Approach in Economic Modelling of Economics of Growth

      • V. Deinichenko, G. Bikesheva, A. Borisov

      Pages 155-173

   3. Computer Based Analysis of an Economy in Transition to Steady State Equilibrium

      • Krzysztof Cichocki, Tomasz Szapiro

      Pages 175-191

   4. A Multistrategy Conceptual Analysis of Economic Data

      • Kenneth A. Kaufman, Ryszard S. Michalski

      Pages 193-203

   5. The Credible Modelling of Economic Agents with Limited Rationality

      • Bruce Edmonds, Scott Moss

      Pages 205-219

   6. Logic, Reasoning and a Programming Language for Simulating Economic and Business Processes with Artificially Intelligent Agents

      • Bruce Edmonds, Scott Moss, Steve Wallis

      Pages 221-230

In the past decades several researchers have developed statistical models for the prediction of corporate bankruptcy, e. g. Altman (1968) and Bilderbeek (1983). A model for predicting corporate bankruptcy aims to describe the relation between bankruptcy and a number of explanatory financial ratios. These ratios can be calculated from the information contained in a company's annual report. The is to obtain a method for timely prediction of bankruptcy, a so­ ultimate purpose called "early warning" system. More recently, this subject has attracted the attention of researchers in the area of machine learning, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This research is usually directed at the comparison of machine learning methods, such as induction of classification trees and neural networks, with the "standard" statistical methods of linear discriminant analysis and logistic regression. In earlier research, Feelders et al. (1994) performed a similar comparative analysis. The methods used were linear discriminant analysis, decision trees and neural networks. We used a data set which contained 139 annual reports of Dutch industrial and trading companies. The experiments showed that the estimated prediction error of both the decision tree and neural network were below the estimated error of the linear discriminant. Thus it seems that we can gain by replacing the "traditionally" used linear discriminant by a more flexible classification method to predict corporate bankruptcy. The data set used in these experiments was very small however.

• Analysis
• Prolog
• agents
• algorithms
• artificial intelligence
• classification
• complexity
• knowledge
• learning
• machine learning
• modeling
• neural network
• optimization
• programming
• programming language

• Tel-Aviv University, Israel

  Phillip Ein-Dor

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