INTRODUCTION TO PHILOSOPHY OF SCIENCE
Book I Page 4
3.25 Univocal and Equivocal Terms
A descriptive term’s use is univocal, if no universally quantified negative categorical statement accepted as true can relate any of the predicates in the several universal affirmations functioning as semantical rules for the same subject term. Otherwise the term is equivocal.
If two semantical rules have the form “Every X is A” and “Every X is B”, and if it is also believed that “No A is B”, then the terms “A” and “B” symbolize parts of different meanings for the term “X”, and “X” is equivocal. Otherwise “A” and “B” symbolize different parts of the same meaning complex associated with the univocal term “X”.
The definitions of descriptive terms such as common nouns and verbs in a unilingual dictionary function as semantical rules. Implicitly they are universally quantified logically, and are always presumed to be true. Usually each lexical entry in a large dictionary such as the Oxford English Dictionary offers several different meanings for a descriptive term, because terms are routinely equivocal. Language economizes on words by giving them several different meanings, which the fluent listener or reader can distinguish in context. Equivocations are the raw materials for puns (and for deconstructionist escapades). There is always at least one semantical rule for the meaning complex for each univocal use of a descriptive term, because to be meaningful, the term must be part of the linguistic system of beliefs. If the use is conventional, it must be capable of a lexical entry in a unilingual dictionary, or otherwise recognized by some trade or clique as part of their argot.
A definition, i.e., a lexical entry in a unilingual dictionary, functions as a semantical rule. But the dictionary definition is only a minimal description of the meaning complex of a univocal descriptive term, and it is seldom the whole description. Univocal terms routinely have many semantical rules, when many characteristics can be predicated of a given subject in universally quantified beliefs. Thus there are multiple predicates that universally characterize crows, characteristics known to the ornithologist, and which may fill a paragraph or more in his ornithological reference book.
Descriptive terms can become partially equivocal through time, when some parts of the term’s meaning complex are unaffected by a change of defining beliefs, while other parts are simply dropped as archaic or are replaced by new parts contributed by new beliefs. In science this partial equivocation occurs when one theory is replaced by a newer one due to a test outcome, while the test design for both theories remains the same. A term common to old and new theory may on occasion remain univocal only with respect to the parts contributed by the test-design language.
3.26 Signification and Supposition
Supposition enables identifying ambiguities not due to differences in signification that make equivocations, but instead are ambiguities due to differences in relating the semantics to its ontology.
The signification of a descriptive term is its meaning, and terms with two or more alternative significations are equivocal in the sense described immediately above in Section 3.25. The signification of a univocal term has different suppositions, when it describes its ontology differently due to its having different functions in the sentences containing it.
Historically the subject term in the categorical proposition is said to be in “personal” supposition, because it references individual entities, while the predicate term is said to be in “simple” or “formal” supposition, because the predicate signifies attributes without referencing any individual entities manifesting the attributes. For this reason unlike the subject term the predicate term in the categorical proposition is not logically quantified with any syncategorematic quantifiers such as “every” or “some”. For example in “Every crow is black” the subject term “crow” is in personal supposition, while the predicate “black” is in simple supposition; so too for “No crow is black”.
The subject-term rôle in a sentence in object language has personal supposition, because it references entities.
The predicate-term rôle in a sentence in object language has simple or formal supposition, because it signifies attributes without referencing the entities manifesting the attributes.
Both personal and simple suppositions are types of “real” supposition, because they are different ways of talking about extramental nonlinguistic reality. They operate in expressions in object language and thus describe ontologies as either attributes or the referenced individuals characterized by the signified attributes.
In logical supposition the meaning of a term is considered specifically as a meaning.
Real supposition is contrasted with “logical” supposition, in which the meaning of the term is considered in the metalinguistic perspective exclusively as a meaning, i.e., only semantics is considered and not extramental ontology. For example in “Black is a component part of the meaning of crow”, the terms “crow” and “black” in this statement are in logical supposition. Similarly to say in explicit metalanguage “‘Every crow is black’ is a semantical rule” to express “Black is a component part of the meaning of crow”, is again to use both “crow” and “black” in logical supposition.
Furthermore just to use “Every crow is black” as a semantical rule in order to exhibit its meaning composition without actually saying that it is a semantical rule, is also to use the sentence in the metalinguistic perspective and in logical supposition. The difference between real and logical supposition in such use of a sentence is not exhibited syntactically, but is pragmatic and depends on a greater context revealing the intention of the writer or speaker. Whenever a universally quantified affirmation is used in the metalinguistic perspective as a semantical rule for analysis in the semantical dimension, both the subject and predicate terms are in logical supposition. Lexical entries in dictionaries are in the metalinguistic perspective and in logical supposition, because they are about language and are intended to describe meanings.
In all the above types of supposition the same univocal term has the same signification. But another type of so-called supposition proposed incorrectly in ancient times is “material supposition”, in which the term is referenced in metalanguage as a linguistic symbol in the syntactical dimension with no reference to a term’s semantics or ontology. An example is “’Crow’ is a four-letter word”. In this example “crow” does not refer either to the individual real bird or to its characteristics as in real supposition or to the universal concept of the creature as in logical supposition. Thus material supposition is not supposition properly so called, because the signification is different. It is actually an alternative meaning and thus a type of semantical equivocation. Some modern philosophers have used other vocabularies for recognizing this equivocation, such as Stanislaw Lesńiewski’s (1886-1939) “use” (semantics) vs. “mention” (syntax) and Carnap’s “material mode” (semantics) vs. “formal mode” (syntax).
3.27 Aside on Metaphor
A metaphor is a predication to a subject term that is intended to include only selected parts of the meaning complex conventionally associated with the predicate term, so the metaphorical predication is a true statement due to the exclusion of the remaining parts in the predicate’s meaning complex that would conventionally make the metaphorical predication a false statement.
In the last-gasp days of decadent neopositivism some positivist philosophers invoked the idea of metaphor to explain the semantics of theoretical terms. And a few were closet Cartesians who used it in the charade of justifying realism for theoretical terms. The theoretical term was the positivists’ favorite hobbyhorse. But both realism and the semantics of theories are unproblematic for contemporary pragmatists. In his “Posits and Reality” Quine said that all language is empirically underdetermined, and that the only difference between positing microphysical entities [like electrons] and macrophysical entities [like elephants] is that the statements describing the former are more empirically underdetermined than those describing the latter. Thus contrary to the neopositivists the pragmatists admit no qualitative dichotomy between the positivists’ so-called observation terms and their so-called theoretical terms.
As science and technology advance, concepts of microphysical entities like electrons are made less empirically underdetermined, as occurred for example with the development of the cloud chamber. While contemporary pragmatist philosophers of science recognize no need to explain so-called theoretical terms by metaphor or otherwise, metaphor is nevertheless a linguistic phenomenon often involving semantical change, and it can easily be analyzed and explained with componential semantics.
It has been said that metaphors are both (unconventionally) true and (conventionally) false. In a speaker or writer’s conventional or so-called “literal” linguistic usage the entire conventional meaning complex associated with a univocal predicate term of a universal affirmation is operative. But in a speaker or writer’s metaphorical linguistic usage only some selected component part or parts of the entire meaning complex associated with the univocal predicate term are operative, and the remaining parts of the meaning complex are intended to be excluded, i.e., suspended from consideration and ignored. If the excluded parts were included, then the metaphorical statement would indeed be false. But the speaker or writer implicitly expects the hearer or reader to recognize and suspend from consideration the excluded parts of the predicate’s conventional semantics, while the speaker or writer uses the component part that he has tacitly selected for describing the subject truly.
Consider for example the metaphorical statement “Every man is a wolf.” The selected meaning component associated with “wolf” that is intended to be predicated truly of “man” might describe the wolf’s predatory behaviors, while the animal’s quadrupedal anatomy, which is conventionally associated with “wolf”, is among the excluded meaning components for “wolf” that are not intended to be predicated truly of “man”.
A listener or reader may or may not succeed in understanding the metaphorical predication depending on his ability to select the applicable parts of the predicate’s semantics tacitly intended by the issuer of the metaphor. But there is nothing arcane or mysterious about metaphors, because they can be explained in “literal” (i.e., conventional) terms to the uncomprehending listener or reader. To explain the metaphorical predication of a descriptive term to a subject term is to list explicitly those affirmations intended to be true of that subject and that set forth just those parts of the predicate’s meaning that the issuer of the metaphor intends to be applicable.
The explanation may be further elaborated by listing separately the affirmations that are not viewed as true of the subject, but which are associated with the predicated term when it is predicated conventionally. Or these may be expressed as universal negations stating what is intended to be excluded from the predicate’s meaning complex in the particular metaphorical predication, e.g., “No man is quadrupedal.” In fact such negative statements might be given as hints by a picaresque issuer of the metaphor for the uncomprehending listener.
A semantical change occurs when the metaphorical predication becomes conventional, and this change to conventionality produces an equivocation. The equivocation consists of two “literal” meanings: the original one and a derivative meaning that is now a dead metaphor. As a dead man is no longer a man, so a dead metaphor is no longer a metaphor. A dead metaphor is a meaning from which the suspended parts in the metaphor have become conventionally excluded to produce a new “literal” meaning. Trite metaphors, when not just forgotten, metamorphose into new literals, as they eventually become conventional.
There is an alternative “interactionist” concept of metaphor that was proposed by Max Black (1909-1988), a Cambrian positivist philosopher, in his Models and Metaphors (1962). On Black’s interactionist view both the subject and predicate terms change their meanings in the metaphorical statement due to a semantical “interaction” between them. Black does not describe the process of interaction. Curiously he claims for example that the metaphorical statement “Man is a wolf” allegedly makes wolves seem more human and men seem more lupine. This is merely obscurantism; it is not logical, because the statement “Every man is a wolf” in not universally convertible; “Every man is a wolf” does not imply logically “Every wolf is a man”. The metaphorical use of “wolf” in “Every man is a wolf” therefore does not make the subject term “man” a metaphor. “Man” becomes a metaphor only if there is an independent acceptance of “Every wolf is a man”, where “man” occurs as a predicate.
3.28 Clear and Vague Meaning
Vagueness is empirical underdetermination, and can never be eliminated completely, since our concepts can never grasp reality exhaustively.
Meanings are more or less clear and vague, such that the greater the clarity, the less the vagueness. In “Verifiability” in Logic and Language (1952) Friedrich Waismann (1896-1954) called this inexhaustible residual vagueness the “open texture” of concepts.
Vagueness in the semantics of a univocal descriptive term is reduced and clarity is increased by the addition of universal affirmations and/or negations accepted as true, to the list of the term’s semantic rules with each rule having the term as a common subject.
Additional semantical rules increase clarity. The clarification is supplied by the semantics of the predicates in the added universal affirmations and/or negations. Thus if the list of universal statements believed to be true are in the form “Every X is A” and “Every X is B”, then clarification of X with respect to a descriptive predicate “C” consists in adding to the list either the statement in the form “Every X is C” or the statement in the form “No X is C”. Clarity is thereby added by amending the meaning of “X”.
Clarity is also increased by adding semantical rules that relate any of the univocal predicates in the list of semantical rules for the same subject thus increasing coherence.
If the predicate terms “A” and “B” in the semantical rules with the form “Every X is A” and “Every X is B” are related by the statements in the form “Every A is B” or “Every B is A”, then one of the statements in the expanded list can be logically derived from the others by a syllogism. Awareness of the deductive relationship and the consequent display of structure in the meaning complex associated with the term “X” clarifies the complex meaning of “X”, because the deductive relation makes it more semantically integrated thus enhancing coherence. Clarity is thereby added by exhibiting semantic structure in a deductive system. And the resulting coherence also supplies a psychological satisfaction, because people prefer to live in a coherent world. However “Every A is B” and “Every B is A” are also empirical statements that may be falsified, and if tested and not falsified they offer more than psychological satisfaction, because they are what Ernest Nagel (1901-1985) calls “correspondence rules” and are new laws as occur in heterogeneous reductions.
These additional semantical rules relating the predicates may be negative as well as affirmative. Additional universal negations offer clarification by exhibiting equivocation. Thus if two semantical rules are in the form “Every X is A” and “Every X is B”, and if it is also believed that “No A is B” or its equivalent “No B is A”, then the terms “A” and “B” symbolize parts of different meanings for the term “X”, and “X” is equivocal. Clarity is thereby added by the negation.
3.29 Semantics of Mathematical Language
The semantics for a descriptive mathematical variable intended to take measurement values is determined by its context consisting of universally quantified statements believed to be true including mathematical expressions in the theory language proposed for testing and in the test-design language presumed for testing.
Both test designs and theories often involve mathematical expressions. Thus the semantics for the descriptive variables common to a test design and a theory may be supplied wholly or in part by mathematical expressions, such that the structure of their meaning complexes is partly mathematical. The semantics-determining statements in test designs for mathematically expressed theories may include mathematical equations, measurement language describing the subject measured, the measurement procedures, the metric units and any employed apparatus.
Some of these statements may suggest what 1946 Nobel-laureate physicist Percy Bridgman (1882-1961) in his Logic of Modern Physics (1927) calls “operational definitions”, because the statements describing the measurement procedures and apparatus contribute meaning to the descriptive term that occurs in a test design. Bridgman says that a concept is a set of operations. But contrary to Bridgman and as even the positivist Carnap recognized in his Philosophical Foundations of Physics (1966), each of several operational definitions for the same term does not constitute a separate definition for the term’s concept of the measured subject thereby making the term equivocal. Likewise pragmatists say that descriptions of different measurement procedures contribute different parts to the meaning of the univocal descriptive term, unless the different procedures produce different measurement values, where the differences are greater than the estimated measurement errors in the overlapping ranges of measurement. Also contrary to Bridgman operational definitions have no special status; they are just one of many possible types of statement often found in a test design. Furthermore the semantics is not the measurement procedures as a nominalist would maintain, but rather the semantics is the concept of the measurement procedures.
3.30 Semantical State Descriptions
A semantical state description for a scientific profession is a synchronic display of the semantical composition of the various meanings of the partially equivocal descriptive terms in the several alternative theories functioning as semantical rules and addressing a single problem defined by a common test design.
The above discussions in philosophy of language have focused on descriptive terms such as words and mathematical variables, and then on statements and equations that are constructed with the terms. For computational philosophy of science there is an even larger unit of language, which is the semantical state description.
In his Meaning and Necessity Carnap had introduced a concept of semantical state description in his philosophy of semantical systems. Similarly in computational philosophy of science a state description is a semantical description but different from Carnap’s. The statements and/or equations supplying the terms for a discovery system’s input and the statements and/or equations constituting the output state description are all semantical rules. Each alternative theory or law in a state description has its distinctive semantics for its constituent descriptive terms. A term shared by several alternative theories or laws is thus partly equivocal. But the term is also partly univocal due at least to the common test-design statements and/or equations that are also semantical rules, which are operative in state descriptions.
In computational philosophy of science the state description is a synchronic and thus a static semantical display. The state description contains vocabulary actually used in the surface structure of a science both in an initial state description supplying object-language terms inputted to a discovery system, and in a terminal state description containing new object-language statements or equations output generated by a computerized discovery-system’s execution. The initial state description represents the current frontier of research for the specific problem. Both input and output state descriptions for a discovery-system execution address only one problem identified by the common test design, and thus for computational philosophers of science they represent only one scientific “profession” (See below, Section 3.47).
A discovery-system is a mechanized finite-state generative grammar that produces sentences or equations from inputted descriptive terms or variables. As a grammar it is creative in Noam Chomsky’s sense in his Syntactical Structures, because when encoded in a computer language and executed, the system produces new theories that have never previously been stated in the particular scientific profession. A mechanized discovery system is Feyerabend’s principle of theory proliferation applied with mindless abandon. But to control the size and quality of the output, the system also tests the empirical adequacy of the generated novel theories and usually rejects most of them. Associating measurement data with the inputted variables enables empirical testing, so that the system designs often employ one or another type of applied numerical methods.
For semantical analysis a state description consists of universally quantified statements and/or equations. These statements and/or equations including theories and the test design from which the inputted terms were extracted are included in the state description although not for discovery system input, because these statements and/or equations would prejudice the output. Statements and/or equations function as semantical rules in the generated output only. Thus for discovery-system input, the input is a set of descriptive terms found in the input state description and extracted from the statements and/or equations of the several currently untested theories addressing the same unsolved problem as defined by a common test design at a given point in time.
Descriptive terms extracted from the statements and/or equations constituting falsified theories might also be included to produce a cumulative state description for input, because the terms from previously falsified theories represent available information at the historical or current point in time. Descriptive terms salvaged from falsified theories have scrap value, because they may be recycled productively through the theory-developmental process. Furthermore terms and variables from tested and currently 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 comparative-static display consists of two chronologically successive state descriptions containing 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 descriptive terms and variables. Comparison of the statements and equations in two chronologically separated state descriptions containing the same test design for the same profession exhibits semantical changes resulting from the transition.
In computational philosophy of science comparative-static comparison is typically a comparison of a discovery system’s originating input and generated output state descriptions of theory statements for purposes of contrast.
3.32 Diachronic Dynamic Analysis
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 describes a process of linguistic change between the two successive state descriptions.
Such transitions in science are the result of two pragmatic 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 constructed 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 mechanized discovery systems that can explicitly proceduralize and thus mechanize a transition applied to language in the current state description of a science, in order to develop a new state description containing one or several new and empirically adequate theories.
The discovery systems created by the computational philosopher of science represent diachronic dynamic metalinguistic analyses. The systems proceduralize developmental transitions explicitly with a mechanized system design, in order to accelerate the advancement of a contemporary state of a science. Their various procedural system designs are metalinguistic logics for rational constructions of a scientific discovery process. The discovery systems are generative grammars that produce surface-structure theories as may actually be found in the object language of the applicable science. The systems generate the new theories by applying the discovery system to the vocabulary in the current state description for the science. The systems also typically include measurement data and 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.
But presently few philosophy professors have the needed competencies to contribute to computational philosophy of science, because few curricula in university philosophy departments even expose much less actually prepare students for contributing to this new and emerging area in philosophy of science for their necessarily interdisciplinary Ph.D. philosophy dissertations. Among today’s academic philosophers the mediocrities will simply ignore this new area, while the Luddites will shrilly reject it. Lethargic and/or reactionary academics that dismiss it are fated to spend their careers denying its merits and evading it, as they are inevitably marginalized, destined to die in obscurity. The exponentially growing capacities of computer hardware and the proliferation of computer-systems designs have already been enhancing the mechanized practices of basic-scientific research in many sciences.
Thus, as mentioned above, in his Extending Ourselves (2004) University of Virginia philosopher of science and cognitive scientist Paul Humphreys reports that computational science for scientific analysis has already far outstripped natural human capabilities and that it currently plays a central rôle in the development of many physical and life sciences. Neither philosophy of science nor the retarded social sciences can escape such developments. For example 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 for Economic Modeling at their Institute for New Economic Thinking” write that automatic modeling has “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 University’s Martin School. These authors have developed a mechanized general-search algorithm that they call AUTOMETRICS for determining the equation specifications for econometric models.
Artificial intelligence today is producing an institutional change in both the sciences and the humanities. In “MIT Creates a College for Artificial Intelligence, Backed by $1 Billion” The New York Times (16 October 2018) reported that the Massachusetts Institute of Technology (MIT) will create a new college with fifty new faculty positions and many fellowships for graduate students, in order to integrate artificial intelligence systems into both its humanities and its science curricula. The article quoted L. Rafael Reif, president of MIT as stating that he wanted artificial intelligence to make a university-wide impact and to be used by everyone in every discipline [presumably including philosophy of science]. And the article also quoted Melissa Nobles, dean of MIT’s School of Humanities and Sciences, as stating that the new college will enable the humanities to survive, not by running from the future, but by embracing it.
Computational philosophy of science 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”, “computational philosophy of science” or “artificial intelligence”. Our twenty-first century perspective shows that computational philosophy of science has indeed “come of age”, as Hendry and Doornik report. So, there is hope that the next generation of academic journal editors and their favorite referees, whose peer-reviewed publications now operate as a haven for Luddites, reactionaries and hacks, will stop running from the future and belatedly acknowledge the power and productivity of artificial intelligence.
3.34 An Interpretation Issue
In “A Split in Thinking among Keepers of Artificial Intelligence” The New York Times (18 July 1993) reported that scientists attending the annual meeting of the American Association of Artificial Intelligence expressed disagreement about the goals of artificial intelligence. Some maintained the traditional view that artificial-intelligence systems should be designed to simulate intuitive human intelligence, while others maintained that the phrase “artificial intelligence” is merely a metaphor that has become an impediment, and that AI systems should be designed to exceed the limitations of intuitive human intelligence.
There is also 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 identified by a test design. Like the diverse members of a profession, the system produces a diversity of new theories. But no psychological claims are made about intuitive thinking processes.
Computer discovery systems are generative grammars that generate and test theories.
On the linguistic analysis interpretation the computer discovery systems are mechanized generative grammars that construct and test theories. The AI 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. The various procedural discovery system designs are rational constructions of the discovery process. 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 a scientist’s cognitive state consisting of mental representations and the discovery system represents the scientist’s cognitive processes.
Computer discovery systems are psychological hypotheses about intuitive human problem-solving processes.
Contemporary views in cognitive psychology are illustrated in Cognitive Psychology: An Overview for Cognitive Scientists (1992) by Lawrence W. Barsalou of the University of Chicago, who writes that cognitive psychology has used internal psychological constructs (internal constructs that are rejected altogether by the behaviorist school). He says that these constructs almost always describe information-processing mechanisms, and that their plausibility rests primarily on their ability to explain behavioral data. He notes that internal psychological constructs are analogous to a computer’s information flows: neurological mechanisms and cognitive constructs in the brain are analogous to electronics and information processing in computers (P. 10).
The originator of the cognitive-psychology interpretation is Simon. In his Scientific Discovery: Computational Explorations of the Creative Processes (1987) 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 those processes. There he states that an empirical test of the systems as psychological theories of intuitive human discovery processes would involve presenting the computer programs and some human subjects with identical problems, and then comparing their behaviors. But Simon admits that his book provides nothing by way of comparison with human performance. And in discussions of particular applications involving particular historic discoveries, he also admits that in some cases the historical scientists actually performed their discoveries differently than the way the systems performed the rediscoveries.
The academic philosopher Paul Thagard, who follows Simon’s cognitive psychology interpretation, originated the name “computational philosophy of science” in his Computational Philosophy of Science (1988). Hickey admits that it is more descriptive than the name “metascience” that Hickey had proposed in his Introduction to Metascience a decade earlier. Thagard defines computational philosophy of science as “normative cognitive psychology”. His cognitive-psychology systems have successfully replicated developmental episodes in history of science, but the relation of their system designs to systematically observed human cognitive processes is still unexamined. And their outputted theories to date have not yet proposed any new contributions to the current state of any science.
In their “Processes and Constraints in Explanatory Scientific Discovery” in Proceedings of the Thirteenth Annual Meeting of the Cognitive Science Society (2008) Langley and Bridewell, who advocate Simon’s cognitive-psychology interpretation, appear to depart from the cognitive-psychology interpretation or at least to redefine it. They state that they have not aimed to “mimic” the detailed behavior of human researchers, but that instead their systems address the same tasks as scientists and carry out search through similar problem spaces. This much might also be said of the linguistic-analysis approach.
The relation between the psychological and the linguistic perspectives can be illustrated by way of analogy with man’s experience with flying. Since primitive man first saw a bird spread its wings and escape the hunter by flight, mankind has been envious of birds’ ability to fly. This envy is illustrated in ancient Greek mythology by the character Icarus, who escaped from the labyrinth of Crete with wings that he made of wax. But Icarus flew too close to the hot sun, so that he fell from the sky as the wax melted, and then drowned in the Aegean Sea. Icarus’ fatally flawed choice of materials notwithstanding, his basic design concept was a plausible one in imitation of the evidently successful flight capability of birds. Call Icarus’ design concept the “wing-flapping” technology. In fact in the 1930’s there was a company called Gray Goose Airways, which claimed to have developed a wing-flapping aircraft they called an “ornithopter”. But pity the investor who holds equity shares in Gray Goose Airways today, because his stock certificates are good only for folded-paper toy-glider airplanes. A contemporary development of the wing-flapping technology might serve well for an ornithological investigation of how birds fly, but it is not the technology used for modern flight, which has evolved quite differently.
When proposed imitation of nature fails, pragmatic innovation prevails, in order to achieve the practical aim. Therefore when asking how a computational philosophy of science should be conceived, it is necessary firstly to ask about the aim of basic science, and then to ask whether or not computational philosophy of science is adequately characterized as “normative cognitive psychology”, as Thagard would have it. Contemporary pragmatist philosophy of science views the aim of basic science as the production of a linguistic artifact having the status of an “explanation”, which includes law language that had earlier been a proposed theory and has not been falsified when tested empirically. The aim of a computational philosophy of science in turn is derivative from the aim of science: to enhance scientists’ research practices by developing and employing mechanized procedures capable of achieving the aim of basic science. The computational philosopher of science should feel at liberty to employ any technology that achieves this aim with or without any reliance upon psychology.
So, is artificial intelligence computerized psychology or computerized linguistics? There is as yet no unanimity. To date the phrase “computational philosophy of science” need not commit one to either interpretation. Which interpretation prevails in academia will likely depend on which academic department productively takes up the movement. If the psychologists develop new and useful systems that produce contributions to an empirical science, the psychologistic interpretation will prevail. If the philosophers take it up successfully, their linguistic-analysis interpretation will prevail.
For more about Simon, Langley, and Thagard and about discovery systems and computational philosophy of science readers are referred to BOOK VIII at the free web site www.philsci.com or in the e-book Twentieth-Century Philosophy of Science: A History, which is available in the web site through hyperlinks to Internet booksellers.
3.35 Ontological Dimension
Semantics is description of reality; ontology is reality as described and thus revealed by semantics.
Ontology is the aspects of mind-independent reality that are signified and thus revealed by relativized perspectivist semantics.
Ontology is the metalinguistic dimension after syntax and semantics, and it presumes both of them. It is the reality that is signified by semantics. Semantically interpreted syntax describes ontology most realistically, when the statement is warranted empirically by independently repeated nonfalsifying test outcomes. Thus in science ontology is more adequately realistic, when described by the semantics of either a scientific law or an observation report having its semantics defined by a law. The semantics of falsified theories display ontology less realistically due to the falsified theories’ demonstrated lesser empirical adequacy.