Bush, a technical advisor to Roosevelt, published in 1945 an article in Atlantic Monthly which highlighted problems in the growth of knowledge, and p… - Brian R. Gaines

Models of human reasoning are clearly relevant to a wide variety of subject areas such as sociology, economics, psychology, artificial intelligence and man-machine systems. Broadly there are two types: psychological models of what people actually do; and formal models of what logicians and philosophers feel a rational individual would, or should, do. The main problem with the former is that it is extremely difficult to monitor thought processes - the behaviourist approach is perhaps reasonable with rats but a ridiculously inadequate source of data on man - the introspectionist approach is far more successful [e.g. in analysing human chess strategy... but the data obtained is still incomplete and may not reflect the actual thought processes involved.

The postulation of a principle of causality, “to every effect there is a cause,” has been a continuing central problem for philosophy (Popper, 1972). Its role as a source of contention in modern science (Jauch, 1973) is epitomized by Einstein’s remark that, “I can’t believe that God plays dice.” Many of the arguments about the application of the principle are very relevant to systems science and to problems of system identification and machine learning, on the one hand,and to epistemology and behavioural psychology, on the other. In current system science the theory of causal deterministic systems is most well developed and generally applied, while the theory of modeling with alternative structures, e.g., stochastic automata, indeterminate automata, products of asynchronous automata, etc., has not been developed to the same degree.

Tracking the individual learning curves of the major technologies that comprise the infrastructure of information technology provides a more detailed account of the present and future state-of-the art of the technologies underlying convergence. The base technologies of digital electronics, general-purpose computer architectures, software and interaction are mature and provide solid foundations for computer science. The upper technologies of knowledge representation and acquisition, autonomy and sociality, support product innovation and provide the beginnings of foundations for knowledge science. Well's dream of a world brain making available all of human knowledge is well on its way to realization and it is in the representation, acquisition, and access and effective application of that knowledge that the commercial potential and socio-economic impact of convergence lies.