of a variable for the volume quantity and development of a constant coefficient for the particular gas could elaborate Gay-Lussac’s law for gasses into the combined Gay-Lussac’s law, Boyle’s law and Charles’ law. Similarly Friedman’s macroeconomic quantity theory might be elaborated into a Keynesian liquidity-preference function by the introduction of an interest rate, to account for the cyclicality manifest in an annual time series describing the calculated velocity parameter and to display the liquidity trap phenomenon.
Pat Langley’s BACON discovery system implements theory elaboration. It is named after the English philosopher Francis Bacon (1561-1626) who thought that scientific discovery can be routinized. BACON is a set of successive and increasingly sophisticated discovery systems that make quantitative laws and theories from input measurements. Langley designed and implemented BACON in 1979 as the thesis for his Ph.D. dissertation written in the Carnegie-Mellon department of psychology under the direction of Simon. A description of the system is in Simon’s Scientific Discovery: Computational Explorations of the Creative Processes (1987).
BACON uses Simon’s heuristic-search design strategy, which may be construed as a sequential application of theory elaboration. Given sets of observation measurements for two or more variables, BACON searches for functional relations among the variables. BACON has simulated the discovery of several historically significant empirical laws including Boyle’s law of gases, Kepler’s third planetary law, Galileo’s law of motion of objects on inclined planes, and Ohm’s law of electrical current.
Theory revision is a reorganization of currently existing information to create a new theory. In his Origins of Modern Science 1300-1800 Herbert Butterfield wrote that in both celestial and terrestrial physics the historic scientific revolution was brought about not by new observations or by additional evidence, but by transpositions that took place inside the minds of the scientists (P. 1). The results of theory revision may be radically different, so revision might be undertaken after repeated attempts at both theory extension and theory elaborations have failed to correct a previously falsified theory. The source for the input state description for mechanized theory revision consists of the descriptive vocabulary from the currently untested theories addressing the problem at hand. The descriptive vocabulary from previously falsified theories may also be included as inputs to make an accumulative state description, because the vocabularies in rejected theories can be productively cannibalized for their scrap value. The new theory is most likely to be called revolutionary if the revision is great, because theory revision typically produces greater change to the current language state than does theory extension or theory elaboration thus producing psychologically disorienting semantical dissolution.
Hickey’s METAMODEL discovery system synthesizes theory revisions. It constructed the Keynesian macroeconomic theory from U.S. statistical data available prior to 1936, the publication year of Keynes’ revolutionary General Theory of Employment, Interest and Money. The applicability of the METAMODEL for this theory revision was already known in retrospect by the fact that, as 1980 Nobel-laureate econometrician Lawrence Klein wrote in his Keynesian Revolution (1947), all the important parts of Keynes theory can be found in the works of one or another of his predecessors. Hickey’s METAMODEL discovery system described in his Introduction to Metascience (1976) is a mechanized generative grammar with combinatorial transition rules producing econometric models. The grammar is a finite-state generative grammar both to satisfy the collinearity restraint for the regression-estimated equations and to satisfy the formal requirements for executable multi-equation predictive models. The system tests for collinearity, statistical significance, serial correlation, goodness-of-fit properties of the equations, and for accurate out-of-sample retrodictions. Simon calls this combinatorial type of system a “generate-and-test” design.
Hickey also used his METAMODEL system in 1976 to develop a post-classical macrosociometric functionalist model of the American national society with fifty years of historical time-series data. To the shock, chagrin and dismay of academic sociologists it is not a social-psychological theory, and four sociological journals therefore rejected Hickey’s paper describing the model and its findings about the national society’s dynamics and stability characteristics. The paper is reprinted below as “Appendix I” to BOOK VIII.
The academic sociologists’ a priori ontological commitments to romanticism and social-psychological reductionism rendered the referees invincibly obdurate. Their criticisms also betrayed their Luddite mentality toward mechanized theory development. Later in the mid-1980’s Hickey integrated his macrosociometric model into a Keynesian macroeconometric model to produce an institutionalist macroeconometric model for the Indiana Department of Commerce, Division of Economic Analysis.
4.13 Examples of Successful Discovery Systems
There are several examples of successful discovery systems in actual use. John Sonquist developed his AID system for his Ph.D. dissertation in sociology at the University of Chicago. His dissertation was written in 1961, when William F. Ogburn was department chairman, which was before the romantics took over the University of Chicago sociology department. He described the system in his Multivariate Model Building: Validation of a Search Strategy (1970). The system has long been used at the University of Michigan Survey Research Center. Now modified as the CHAID system using χ2 Sonquist’s discovery system is available commercially in SAS and SPSS statistical software packages. Its principal commercial application is for list processing for market analysis and for risk analysis as well as for academic investigations in social science. It is not only the oldest mechanized discovery system but also the most widely used in practical applications to date.
Robert Litterman developed his BVAR system for his Ph.D. dissertation in economics at the University of Minnesota. He described the system in his Techniques for Forecasting Using Vector Autoregressions (1984). The economists at the Federal Reserve Bank of Minneapolis have long used his system for macroeconomic and regional economic analysis. It has also been used for regional economic analysis both by the State of Connecticut and the State of Indiana.
Having previously received an M.A. degree in economics Hickey had intended to develop his METAMODEL computerized discovery system for a Ph.D. dissertation in philosophy of science while a graduate student in the philosophy department of the University of Notre Dame, South Bend, Indiana. But the Notre Dame philosophers under the chairmanship of a Reverend Ernan McMullin were obstructionist to Hickey views, and Hickey dropped out. He then developed his computerized discovery system as a nondegree student at San Jose City College in San Jose, California.
For thirty years afterwards Hickey used his discovery system occupationally, working as a research econometrician in both business and government. For six of those years he used his system for Institutionalist macroeconometric modeling and regional econometric modeling for the State of Indiana Department of Commerce. He also used it for econometric market analysis and risk analysis for various business corporations including USX/United States Steel Corporation, BAT(UK)/Brown and Williamson Company, Pepsi/Quaker Oats Company, Altria/Kraft Foods Company, Allstate Insurance Company, and TransUnion LLC.
In 2004 TransUnion’s Analytical Services Group purchased a perpetual license to use his METAMODEL system for their consumer credit risk analyses using their proprietary TrenData aggregated quarterly time series extracted from their large national database of consumer credit files. Hickey used the models generated by the discovery system to forecast payment delinquency rates, bankruptcy filings, average balances and other consumer borrower characteristics that constitute risk exposure for lenders, especially during the contractionary phase of the business cycle. He also used the system at Quaker Oats and Kraft Foods to discover the sociological and demographic factors responsible for the secular long-term market dynamics of food products and other nondurable consumer goods.
More about discovery systems and the evolution of computational philosophy of science is in BOOK VIII below.
4.14 Scientific Criticism
