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vengeance. To control the size and quality of the output, the system tests the empirical adequacy of those novel theories and likely rejects most of them. Associating measurement data with the inputted variables enables empirical testing, so that the system designs are typically one or another type of applied numerical methods.

      For semantical analysis the state description consists of universally quantified statements and/or equations. The statements and/or equations from which the terms and/or variables were extracted including theories and test design are part of the state description although not for discovery system input, because they would prejudice the output. Statements and/or equations function as semantical rules in the generated output only. Thus for discovery-system input, the input state description is a listing of descriptive terms and/or variables extracted from the statements and/or equations of the several currently untested theories addressing the same unsolved problem as defined by a test design at a given point in time.

      Descriptive terms and/or variables extracted from the statements and/or equations of falsified theories can also be included to produce a cumulative state description for input, because the terms and/or variables from previously falsified theories represent available information at the historical or current point in time. Descriptive terms and/or variables salvaged from falsified theories have scrap value, because they may be recycled through the theory-developmental process. Furthermore terms and variables from tested and nonfalsified theories could also conceivably be included, just to see what new comes out. Empirical underdetermination permits scientific pluralism, and the world is full of surprises.

      3.31 Diachronic Comparative-Static Analysis

      A diachronic display consists of two chronologically successive state descriptions of theory statements for the same problem defined by the same test design and therefore addressed by the same scientific profession. State descriptions contain statements and equations that operate as semantical rules displaying the meanings of the constituent terms and variables. Comparison of the statements and equations in the two chronologically separated state descriptions containing the same test design exhibits changes in meanings through time.

      In computational philosophy of science this is a comparative-static semantical analysis, i.e., a comparison of a discovery system’s input and output state descriptions of theory statements. After the computer system is run the resulting output state description is of principal interest.

      3.32 Diachronic Dynamic Analysis

      The above discussions have described the synchronic and comparative-static diachronic perspectives. Both are static, because they apply to points in time. The dynamic diachronic metalinguistic analysis not only consists of two state descriptions representing two chronologically successive language states sharing a common subset of descriptive terms in their common test design, but also exhibits a process of linguistic change between the two successive state descriptions.

      Such transitions in science are the result of two functions in basic research, namely theory development and theory testing. A change of state description into a new one is produced whenever a new theory is proposed or whenever a theory is eliminated by a falsifying test outcome.

      3.33 Computational Philosophy of Science

      Computational philosophy of science is the development of computerized discovery systems that can proceduralize explicitly the past achievements of successful scientists, and then apply the successfully mechanized procedures to the current state description of a science to develop a new state description containing one or several new and superior theories.

      The discovery systems created by the computational philosopher of science represent diachronic dynamic metalinguistic analyses. They proceduralize a transitional process explicitly with the computerized system design, in order ultimately to accelerate the contemporary advancement of a science by mechanizing a transitional procedure. Then by applying the system to the current state description for the science they generate new theories. The discovery systems typically include empirical criteria for selecting a subset of the generated theories for output as tested and nonfalsified theories either for further predictive testing or for use as laws in explanations and test designs.

      The computer is here to stay, and in this computer age computational philosophy of science is inevitable. The exponentially growing capacities of computer hardware and proliferation of computer-systems designs have already been enhancing the technical practices of basic-scientific research in many sciences, and philosophy of science cannot escape such developments. Presently few philosophy professors have the needed competencies to contribute to computational philosophy of science. And thus few curricula in philosophy departments encourage much less actually prepare students for contributing to this new and emerging area in philosophy of science. Computational philosophy of science will achieve ascendancy in twenty-first-century philosophy of science due to those who are opportunistic enough to master both the necessary system-development skills and the requisite working competencies in an empirical science. Lethargic and/or reactionary academics that dismiss it are fated to spend their careers evading it, as they are progressively marginalized.

      In the “Introduction” to their Empirical Model Discovery and Theory Evaluation: Automatic Selection Methods in Econometrics (2014) David F. Hendry and Jurgen A. Doornik of Oxford University’s Program of Economic Modeling at their Institute for New Economic Thinking write that automatic modeling has indeed “come of age.” Hendry was head of Oxford’s Economics Department from 2001 to 2007, and is presently Director of the Economic Modeling Program at Oxford’s Martin School. And Doornik is a colleague at the Institute. These authors have developed a mechanized general-search algorithm they call AUTOMETRICS for determining the equation specifications for econometric models.

      Our twenty-first century perspective shows that notwithstanding obstructionism by latter-day Luddites, computational philosophy of science has indeed “come of age”. It is the future that has arrived, even when it is called by other names as practiced by scientists working in their special fields instead of being called “metascience” or “computational philosophy of science”.

      3.34 An Interpretation Issue

      There is ambiguity in the literature as to what a state description represents and how the discovery system’s processes are to be interpreted. The phrase “artificial intelligence” has been used in both interpretations but with slightly different meanings.

      On the linguistic analysis interpretation, which is the view taken herein, the state description represents the language state for a language community constituting a single scientific profession defined by a test design. Like the diverse members of a profession, the system produces a diversity of new theories. No psychological claims are made about intuitive thinking processes. Computational philosophy of science so interpreted is a technique for a specialized type of linguistic analysis employing mechanized generative grammars.

      The computer discovery systems are generative grammars that generate and test theories. The system inputs and outputs are both object-language state descriptions. The instructional code of the computer system is in the metalinguistic perspective, and exhibits diachronic dynamic procedures for theory development. As such the linguistic analysis interpretation is neither a separate philosophy of science nor a psychologistic agenda. It is compatible with the contemporary pragmatism and its use of generative grammars makes it closely related to computational linguistics.

      On the cognitive-psychology interpretation the state description represents the scientist’s cognitive state consisting of mental representations and the discovery system represents the scientist’s cognitive processes. The originator of the cognitive-psychology interpretation is Herbert Simon. In his Scientific Discovery: Computational Explorations of the Creative Processes and other works Simon writes that he seeks to investigate the psychology of discovery processes, and to provide an empirically tested theory of the information-processing mechanisms that are implicated in that process.

      He states that an empirical test of the systems as psychological theories of human discovery processes would involve

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