David A. Fischer

A comparative study of uncertainty methods for legal reasoning

This paper is based on the premise that legal reasoning involves an evaluation of facts, principles, and legal precedent that are inexact, and uncertainty‐based methods represent a useful approach for modeling this type of reasoning. By applying three different uncertainty‐based methods to the same legal reasoning problem, a comparative study can be constructed. The application involves modeling legal reasoning for the assessment of potential liability due to defective product design. The three methods used for this study include: a Bayesian belief network, a fuzzy logic system, and an artificial neural network. A common knowledge base is used to implement the three solutions and provide an unbiased framework for evaluation. The problem framework and the construction of the common knowledgebase are described. The theoretical background for Bayesian belief networks, fuzzy logic inference, and multilayer perceptron with backpropagation are discussed. The design, implementation, and results with each of these systems are provided. The fuzzy logic system outperformed the other systems by reproducing the opinion of a skilled attorney in 99 of 100 cases, but the fuzzy logic system required more effort to construct the rulebase. The neural network method also reproduced the experts opinions very well, but required less effort to develop. ©1999 John Wiley & Sons, Inc.

A Fuzzy Model of Legal Reasoning for Concurrent Engineering

The role of Concurrent Engineering is expanding to recognize enterprise issues in the product realization process. This work continues that trend by advocating proactive consideration of legal liability for defective design in preliminary design. To accomplish this, a fuzzy logic model of legal reasoning is demonstrated. The model evaluates the potential liability of a manufacturer for not adopting a pro posed alternative design. As such, it functions as a heuristic-based progressive model within Prasads [24] Concurrent Engineering para digm. This model is based on the use of interpretive charts by the designer to quantitatively assess six variables on a zero to ten scale as inputs to the model. This allows evaluation of legal concepts by the fuzzy model, and it permits determination of a quantitative measure of potential legal liability for defective design. The system demonstrated here is validated using 100 cases, with acceptable responses at the 99% level. Additionally, use of this system is illustrated with four example cases for product design. Conclusions are drawn regarding poten tial use within the product realization process.