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.

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”.

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.

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).

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.

In their contributions to this volume, Bruce Buchanan (1983) and Lindley Darden (1983) have provided compelling reasons why philosophers of science concerned with the nature of scientific discovery should be aware of current work in artificial intelligence. Buchanan presents abundant examples of the mechanization of generation of empirical hypotheses in current AI programs, while Darden, at a more speculative level, discusses the use of analogy in theoretical discovery. The implication of both these studies is that the philosophy of discovery can profitably borrow notions from work in artificial intelligence, since useful analogies can be found between problems in that field and the problems facing philosophers studying scientific discovery.

I have no quarrel with this claim, except that it is too weak. I shall argue that work in AI is not only useful but essential for investigations in philosophy of science concerning discovery. The connecting link between the two fields is empirical cognitive psychology.

The year 1982 marks the twentieth anniversary of Carl G. Hempel's “Deductive-Nomological vs. Statistical Explanation,” published in Minnesota Studies in the Philosophy of Science, volume III. This work constitutes, to the best of my knowledge, the first extended and systematic philosophical treatment of statistical or probabilistic explanation, and the first serious attempt to articulate explicitly a precise model for such explanations. Because of this fact, it seems to me, we may take 1962 as the year in which the philosophical theory of scientific explanation first entered the twentieth century. By that time, virtually every branch of empirical science made indispensable use of statistical methods and most of them counted statistical generalizations among their important results.

The subject of this panel discussion is probabilistic explanation. However, I would like to broaden the scope of my remarks to include the subject of probabilistic causality as well. For, I will argue in this paper that recent attempts to develop a theory of objective probabilistic causality are vulnerable to the same criticisms that in a previous paper I have advanced against the theories of objective probabilistic explanation proposed by Salmon and Fetzer.

During the past decade or so, both Salmon and Fetzer have written widely on the subject of probabilistic explanation, - and the Statistical-Relevance and Causal-Relevance models for which they are, respectively, well known have been extensively discussed in the literature. During this same period, Patrick Suppes' (1970) monograph has been a common reference point for discussions of probabilistic causality (though as Salmon points out in a recent article , Suppea' account shares many features with earlier accounts of causality proposed by Good and Reichenbach).

Hanna (l981) and Humphreys (1981) have offered criticisms which tend to undermine the plausibility of the single-case propensity account of probabilistic explanation advanced in Fetzer (1974) and (1981a). Hanna contends that, on single-case criteria, there typically are no unique single-case propensity values to assign to the occurrence of particular explanandum-events because the propensities for these outcomes to occur are continuously evolving over time (yielding “the temporal relativity of probabilistic explanations”), while Humphreys suggests that, whether or not unique single-case probability values happen to be available at all, they are nevertheless incidental and dispensable elements of otherwise potentially acceptable explanatory accounts. Indeed, Humphreys and Railton (1978) and (l981) appear to converge in common agreement on the inessential function of single-case probability assignments within the context of probabilistic explanations.

In my paper “Aleatory Explanations” (Humphreys 1981a) I presented the rudiments of a theory of explanation which had the following features:

1. 1) Explanations of indeterministic phenomena are given by means of a specification of the contributing and counteracting causes involved;

2. 2) Probability values are not themselves explanatory and explanations need not specify probability values;

3. 3) Maximal specificity criteria are not part of the logical criteria of adequacy for explanations;

4. 4) Partial explanations which are nevertheless true can thus be given;

5. 5) Explanations of this kind show that the traditional ‘X because Y’ pattern of explanation is inadequate for indeterministic causal explanations and must be replaced by ‘X because Y, despite Z’.

In that paper, I restricted myself to explanations of events and their complements, involving only direct causes in two-state Markov chains.

The Emperor Vespasian, presented with an invention to enable columns to be transported cheaply, praised the inventor but declined to make use of the innovation, apparently fearing the risk of upheaval from the displaced workers (Gimpel 1976, p. 9). Imbued as they are with the notions of progress and efficiency, few leaders in the modern world seem to have followed Vespasian's precedent and rejected the promise of technological innovations. Yet the sheer magnitude of the possible consequences of some technologies, and the multiplicity of choices made possible by competing technological solutions to this or that problem have fostered techniques for the appraisal of benefits and costs (including risks) in proposed courses of action. What information do these techniques give us, and what are the grounds for using them to guide decision making?

One of the roles of government is to regulate new technologies and control the risks to life and health that they impose. This will be true according to any plausible theory of government or politics. There are many reasons for centralizing the control of technological risk. First, the problem can elude individual or decentralized solutions because the transaction costs for bargaining to take place are too high, either because many different actors each impose slight risks which are intolerable only in their aggregate, or else because one actor imposes significant risks which are distributed thinly over very many individuals. A second reason is that technological risks are often invisible, delayed, and exceptionally complex, so the costs of gathering enough information to make reasonable decisions can be so prohibitively high that they are incapable of being borne except at the most centralized levels of government where resources can be pooled.

The current theory of biological evolution (the “Synthetic Theory” or “Modern Synthesis”) may be traced to Theodosius Dobzhansky's Genetics and the Origin of Species, published in 1937: a synthesis of genetic knowledge and Darwin's theory of evolution by natural selection. The excitement provoked by Dobzhansky's book soon became reflected in many important contributions which incorporated into the Modern Synthesis relevant fields of biological knowledge. Notable landmarks are Ernst Mayr's Systematics and the Origin of Species (1942), Julian S. Huxley's Evolution: The Modern Synthesis (1942), George Gaylord Simpson's Tempo and Mode in Evolution (1944), and G. Ledyard Stebbins’ Variation and Evolution in Plants (1950). It seemed to many scientists that the theory of evolution was essentially complete and that all that was left was to fill in the details.

Deeply held scientific beliefs, like metaphysical presumptions, color the world in their own hues, leaving in shadow questions which might be both posable and answerable under a different light. Contemporary evolutionary theory, derived from the intellectual marriage of Darwin and Mendel, is such a world view. Whatever conceptual order this intellectual union has brought, it remains true that heritable mutations and Natural Selection are processes exploring and pruning a complex tree of possibilities. The central epistemological failure of contemporary evolutionary theory, I shall argue, is that it has supplied little insight into the expected character of the possibilities which are explored. Prune though it may, selection itself cannot generate biological order, and variation can only explore. Hidden in the shadows of current theory lies the concept that the ultimate sources of order in the living world must derive from basic properties of structural or dynamical self-organization in complex molecular systems; further that an appropriate understanding of such self-organizing properties would help limit and explain the possibilities explored in evolution.