Let me say at the outset that the following exercise is an attempt to make some sense out of what I have seen my colleagues (and indeed at times myself) doing in science - more particularly in theoretical high-energy physics. I do not claim that my conclusions are final or permanent. In fact, it appears to me that Professor MacKinnon's new book, Scientific Explanation and Atomic Physics, may; present a suitable framework within which to discuss the issues raised here. In any event, since most of this paper consists of a story to be told, it may be of interest to philosophers of science.

Today I shall use developments In the field of theoretical high-energy physics to examine three issues of interest to the philosophy of science. First will be the use of highly abstract, formal models, in the generation and evolution of theories.

Given the central importance of the Copernican theory to the birth of modern science, it is somewhat surprising to learn that there were remarkably few committed Copernicans prior to 1600. In a recent study, Westman (1980) finds only ten, of whom fewer than five were major scientific figures. The vast majority of scientists in this period continued to accept the Earth-centered astronomy of Ptolemy or later switched to the geoheliocentric system of Tychp Brahe. This widespread reluctance to adopt the heliocentric theory suggests the following questions about the Copernican Revolution: When and why did it become rational to reject the Ptolemaic theory as false? When and why did it become rational to accept the Copernican theory as true? Or, to put these questions in the more familiar language of the epistemologist: When and why did the beliefs that the Ptolemaic theory is false and that the Copernican theory is true become justified beliefs?

It is now generally recognized that Quantum Field Theory (QFT), after many vicissitudes and a period of almost total neglect, has emerged during the last ten years as a triumphant framework in which to do elementary particle physics.

If philosophers are prepared to use current physical theory as a guide to resolving metaphysical questions (and that is a big “If”), then QFT is what philosophers should all be studying. But the subject is notoriously.recondite, and philosophers are busy people, so the objective of the present paper is to cut through all technicalities and try to tell in as straightforward a way as possible just what QFT has to say about “reality”.

The history of science provides two prima facie reasons for believing that there is a “logic” to scientific discovery. The first is the phenomenon of multiple or simultaneous discoveries. The second is the fact that internalist historians of science (who eschew non-cognitive explanations) routinely explain how a discovery was made and why scientist X took an important step which scientist Y did not!

However, some philosophers have argued that purported historical explanations of discovery are all sleight-of-hand tricks—what we really have is a confused mixture of rational reconstructions of the justification steps in the process combined with a description, not an explanation, of the creative leaps. Others would claim that historians do indeed help us to understand scientific discoveries, but the mode of understanding involved is not that provided by the Hempelian covering law model. Rather we empathize with the creative leaps of past geniuses.

Though Imre Lakatos's ideas have been of first importance to recent History and Philosophy of Science, one of his central theses has been ill-received by philosophers and historians alike. I refer to his thesis that historiography of science is (and should be) so laden with normative philosophical doctrines about scientific rationality that “history of science without philosophy of science is blind.” (1971, p.91). In defending this ‘norm-ladenness thesis’ (as I shall call it), Lakatos employs a notion of “rational reconstruction” and “internal history” that has been indicted by Thomas Kuhn as “not history at all but philosophy fabricating examples.” (1971, p. 143). Kuhn's charge, echoed in varying degrees by Holton (1974, p. 68), Laudan (1977, p. 170), McMullin (1970, p.33), Suppe (1977, p. 669), and others, has such wide currency that I shall call it ‘the Received Critique’ of Lakatos's norm-ladenness thesis.

Explanations sometimes involve a certain asymmetry. For example, given Ohm's Law and the voltage of a battery, one can calculate the current in an electric circuit if one knows the resistance, or one can calculate the resistance if one knows the current. Both of these calculations are (roughly) deductive-nomological arguments, but only the calculation of the current from the resistance is an explanation. Why should only one of these calculations be an explanation?

One plausible diagnosis of such explanatory asymmetries is that they arise from the asymmetry of causal relations (see, for example, Salmon 1978 or Beauchamp and Rosenberg 1981, p. 313). The direction of explanation is the direction of causation. The resistance of the wire is a causal condition influencing the amount of the current, while the amount of current does not (as a first approximation) influence the resistance.

An argument of Donald Davidson's (1970, 1973) has convinced many people that there is something suspect about the commonsense, belief-desire-action explanations that most of us apply to human behavior. Alexander Rosenberg (1980, 1983) has been convinced by Davidson's argument, and uses its conclusion (together with some ideas about sociobiology) to explain what he regards as the persistent failures of the social sciences. In this paper I shall not comment directly upon Davidson's argument. Instead, I will discuss an unacceptable implication of Rosenberg's use of it. Whether this reflects on Davidson's argument I leave for others to decide.

This is how Rosenberg uses the conclusion of Davidson's argument to explain the (alleged) failures of economics.

In The Cement of the Universe J. L. Mackie offered—with supplementation—a necessary-condition analysis of singular causal claims: “It seems that if any X is both necessary in the circumstances for and causally prior to Y, we shall say that X caused Y; also, wherever we are prepared to say that X caused Y we are prepared to say that X was necessary in the circumstances for and causally prior to Y” (p. 51). As Mackie interprets ‘necessary’, this is a counter-factual analysis.

Although there are difficulties with treating necessity (supplemented with the indicated qualifications) as sufficient for our being justified in saying that X caused Y (argued by Berofsky 1977 and Earman 1976, among others), I want to challenge the second half of the analysis, because refuting it seems to shake more deeply my sympathies for necessary-condition analyses.

Mechanized methods in science have attracted much attention among philosophers since Bacon. Of the many facets of scientific activity discovery has seemed most inscrutable. Until recently, Peirce and Hanson were the only ones to claim publicly that there can be rational methods for discovering hypotheses as well as for testing them. Their arguments, and more recent ones, are based on historical examples and analysis, and thus lack the convincingness of an existence proof. In this paper I take an empirical look at the question of whether there are rational methods of discovery and claim that computer programs provide a laboratory for experimentation on this question. Recent work in artificial intelligence, or AI, has produced programs capable of serious intellectual work in science. Results from AI will be used to show that there exist mechanized procedures for discovering hypotheses and that these methods often lead to plausible hypotheses (but do not guarantee always finding the correct hypothesis).

There is an ancient principle for which a number of writers on time and causation feel a certain allegiance. Bertrand Russell puts the idea with his characteristic flare for style: “… it seems strange—too strange to be accepted, in spite of bare logical possibility—that the cause, after existing placidly for some time, should suddenly explode into the effect, when it might just as well have done so at an earlier time, or have gone unchanged without producing its effect.” (Russell 1912, pp. 178-179). The maxim has a habit in the history of thought of lying unnoticed but once spotted leading its viewer to less than cautious theses. With maxim in hand, David Hume (1739, p. 76) concludes that simultaneous causation is impossible, Myles Brand (1980) concludes that every causal relation is one of simultaneity, Russell.(1912) declares that the very notion of causation is incoherent.

This paper will summarize positions explained and defended more fully in my book, Space-Perception and The Philosophy of Science (Heelan 1982). The philosophical genre of this paper is that of a hermeneutical phenomenology, it addresses questions from the point of view of such writers as, E. Husserl, M. Heidegger, M. Merleau-Ponty, P. Ricoeur, and H-G. Gadamer (see references). Those who are not familiar with this kind of writing may well find the exposition too brief to be persuasive; those who are, may wonder at the audacity of applying such methods of analysis to the natural sciences. It is unlikely that either group will be entirely pleased with the content of what I have to say.

My principal theses are the following: (1) reality is the content of World, and this comprises whatever is or can be given directly in a public way within perception;

As a historian and philosopher of science, I am interested in the process of the construction of new scientific theories. The hypothesis to be explored in this paper is that analogies may play an important role in the generation of new ideas that are built into new theories.

In order to make clear the locus of my discussion, I would like to present an over-simplified view of the levels of organization of scientific knowledge. The three levels are the following: data, empirical generalizations, and explanatory theories. At the lowest level of generality are the data, particular statements about, particular facts. For example, if we consider data from genetics, in a cross between a yellow pea plant and a green pea plant, all the peas of the first generation of hybrids are yellow; if the hybrids are self-fertilized, the second hybrid generation has a ratio of three yellow peas to one green pea.