The thesis undertakes an analysis of the modelling methods used in the Soft Systems Methodology (SSM) developed by Peter Checkland and Brian Wilson. The analysis is undertaken using formal logic and work drawn from modern Anglo-American analytical philosophy especially work in the area of philosophical logic, the theory of meaning, epistemology and the philosophy of science.\ud \ud The ability of SSM models to represent causation is found to be deficient and improved modelling techniques suitable for cause and effect analysis are developed. The notional status of SSM models is explained in terms of Wittgenstein's language game theory. Modal predicate logic is used to solve the problem of mapping notional models on to the real world.\ud \ud The thesis presents a method for extending SSM modelling in to a system for the design of a knowledge based system. This six stage method comprises: systems analysis, using SSM models; language creation, using logico-linguistic models; knowledge elicitation, using empirical models; knowledge representation, using modal predicate logic; codification, using Prolog; and verification using a type of non-monotonic logic. The resulting system is constructed in such a way that built in inductive hypotheses can be falsified, as in Karl Popper's philosophy of science, by particular facts. As the system can learn what is false it has some artificial intelligence capability. A variant of the method can be used for the design of other types of information system such as a relational database
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.