In his paper on the “Methodology of Positive Economics”, Milton Friedman warned his readers that, “more than other scientists, social scientists need to be self-conscious about their methodology.” (1953, p. 34). But until quite recently, he seems either to have spoken to deaf ears or, more plausibly, to have been so successful in promoting his own views on methodology as to lead economists to be complacent about the many problems which plague their discipline. Many current textbooks, for example the one by Eugene Silberberg, present economics as a science attaining the falsificationist standards once set down by Karl Popper, despite much evidence to the contrary. Indeed, as Douglas W. Hands (1985) has recently shown, even Sir Karl did not intend economics to be subjected to such severe standards.

The problem of scientific rationality is one of the most controversial in the contemporary philosophy of science. If I am so foolhardy as to raise it again, it is because I do not feel comfortable with most present-day solutions and because I hope I may suggest a more promising, although still tentative, line of research.

In order to illustrate the particular angle from which I intend to deal with this problem, I shall make use of the words of a great cosmologist, Fred Hoyle, and I shall also refer to the current debate in cosmology. What I have to say, however, is intended to apply to science in general and not only to a single discipline or to a specific episode.

In present day ecology, competition is a subject typically generating controversies that pervade the whole field. Ecology is concerned with relations between organisms and their environment. As the environment of any organism has biotic besides physical components, relations between organisms are obviously important. Competition is one of them. According to many ecologists it crucially affects the fate of populations and species. Precisely this assumption has led to vigorous debates (cf. Lewin 1984).

I intend to show that elementary methodological analysis could easily dissolve the controversy. Methodology, indeed, has not always been brought to bear on biology as it should. This point is elaborated at the end of the paper.

Ecologists use a variety of descriptive units, beside individual organisms. Here the population - a group of individual organisms of the same species and the community - a group of populations are of special importance.

Glymour’s boostrapping account of confirmation is meant to show how it is that evidence can bear on a theory in a discriminating, noncircular way even when that theory is used to establish the inferential link between evidence and a test hypothesis. Evidence confirms a theory on his account if, “using the theory, we can deduce from the evidence an instance of the hypothesis i.e., an hypothesis comprising or instantiating the test theory, and the deduction is such that it does not guarantee that we would have gotten an instance of the hypothesis regardless of what the evidence might have been.” (1980, p. 127). Glymour goes on to argue that this strategy of inference should appear most explicitly in the developing and “un-natural” (social) sciences where novel theories are being formulated or applied to new domains (1980, p. 172).

Risk assessment is one part of the multistage process of determining the acceptability of risks. Risk assessment, as I understand it, and as I think Dale Hattis is talking about it (Hattis, 1986), is the estimation of the hazards likely to be incurred by proceeding with a given course of action—adopting a new pesticide, using a sugar substitute in processing foods, employing some method of generating energy, installing a toxic or radioactive waste disposal system in a particular location. The hazards in question are generally health hazards as distinct from economic costs, although many critics of current technology assessment (or non-assessment) programs might well argue that this is too limited a conception of hazard—that there may be social and political effects of a technology's adoption that are not encompassed in calculations of biological risk or economic cost. Risk evaluation generally refers to the procedures for deciding whether to employ a particular technology, chemical, etc. i.e. whether to accept the risks involved or not.

The semantic view of scientific theories has been defended as more adequate than the “received” view, especially with respect to biological theories (Beatty 1980, 1981; Thompson 1983). However, the semantic view has not been evaluated on its own terms. In this paper I first show how the theory of sociobiology propounded by E.O. Wilson (1975) can be understood on the semantic approach. I then discuss the criticism that Wilson’s theory is beset by the problem of unreliable generalizations. I suggest that this problem results from the use of the model-building strategy in theory. I conclude that the problem is pressing enough to impugn the semantic view as an adequate account of sociobiological theory.

According to proponents of the semantic view of theories, scientific theories function to specify a class of “models,” interpretations, or representations of their postulates.

The last twenty-five years have seen a major shift in the philosophy of science, from a focus on the logical syntax of the language of science to attempts, based often on detailed historical research, to understand the development of scientific accounts of the world. The once “Received View” of logical empiricism assumed the adequacy of an analysis of scientific knowledge in terms of interpreted logical systems. The limitations of logic as a framework for analysis of the language of science became apparent for what one might call internal and external reasons. Internally, the attempt to formalize the logical syntax of science led to the emergence to view of fundamental problems with the logical construal of laws and with the concept of confirmation.

Responses to Newcomb-like challenges to evidential decision theories such as Jeffrey’s “logic of decision” range from allegations of incoherence and irrelevance; through stonewalling - “Just one box for me, thank you.“; to arguments that maintain that when properly applied by an ideal agent such theories get the right answers and, for example, prescribe the taking of both boxes, not just one; on to conservative revisions of evidential decision theories that are held to get these supposedly right answers while remaining true to its evidential, Humean spirit; and finally to responses of radical revisionists who claim that only causal decision theories can get all Newcomb-like problem cases right. I discuss responses of the first sort in another paper - “Newcomb-like Problems for Jeffrey’s Logic of Decision.”

In this paper I intend to discuss difficulties which face Hilary Putnam’s internal realism, difficulties which involve issues in semantics. I hope to show that Putnam has not presented a viable theory of truth. Putnam wishes to avoid relativism, which he claims is incoherent. My argument against Putnam’s theory of truth will be that Putnam has avoided relativism at only a very superficial level. On a deeper analysis we will see that Putnam has in fact not avoided relativism.

Putnam has abandoned the correspondence theory of truth since he can make no sense of the claim that our words, thoughts, images, sensations, etc. correspond to external objects. Considerations of human psychology (1983a, p.viii), as well as problems concerning reference (1981; 1983a, pp.vii-xviii; 1983b), have led Putnam to adopt the view that truth must be analyzed in terms of justification.

In his 1982 Presidential Address to the PSA Ernan McMullin proposed that the Kuhnian “revolution” in the philosophy of science amounts, in part, to the growing awareness that theory-appraisal is to be modelled on decisions and value judgments rather than rule-governed inferences (McMullin 1983). This viewpoint has its attractions but needs developing before it can satisfy skeptics, such as Larry Laudan. For Laudan complains that Kuhn has provided no general model to back up his value-geared account, and that the view leads to untenable subjectivism which Kuhn has tried to avoid.

I shall try to show that neither complaint stands to criticism, by joining two ideas. The first – that of modelling theory decisions along decision theory – is not a novel one, but even its supporters have failed to see that it makes Kuhn’s subjectivity a live but respectable possibility.

The primary significance of the adoption of Black’s (1962) interaction view of metaphor by Hesse in her network model of theories (1966, 1972 and 1974) and in her network model of meanings (1984a) is the fact that it leads to a fundamental modification of the hypothetical-deductive account of scientific theorizing and a relativization of the traditional logical positivist distinction between observation language and theory language. Hesse argues that what holds for metaphorical language in ordinary language use, namely that it is affected by the contextual interaction between the various elements present in the metaphor, is also true of scientific models. They function in a similar fashion (1972, p. 254). She does not reject the distinction between literal and metaphorical language, but claims that the distinction is a pragmatic one. The distinction concerns the way in which speakers learn, use, and if necessary, define the words of their language.

Science is made possible by the introduction of theoretical objects. Why this should be so has never been made clear. Indeed, it has never been made clear how theoretical objects are rightly to be understood, or in what ways they differ from more ordinary sorts of physical objects. What follows is a sketch of a new theory. In my view, this theory becomes explicit on the so-called “Copenhagen interpretation” of quantum mechanics. But it has implicitly characterized scientific development since the revolution of the 16th and 17th centuries.

How are theoretical objects rightly to be understood? I say that they are objects whose existence is postulated by a theory is alright as far as it goes, but it doesn’t go far enough. We also need to know what theories are.

Section 2 will begin by formulating Reichenbach’s principle of common cause in a more general way than is usual but in a way that makes the idea behind it a lot clearer. The way that Salmon has pushed the principle into the services of scientific realism will be explained in terms of an example, van Fraassen objects, Salmon modifies his stand and van Fraassen rejoins - all in section 2. (See van Fraassen 1980, chapter 2).

In this episode I think van Fraassen right in claiming - against Salmon that there is no categorical imperative for common cause explanation, and I add my own examples in section 3. The first example ‘is the explanation of the correlation between the equilibrium positions of two objects on a balance in terms of their property of “mass” and the law of moments.

Probabilistic reasoning is traditionally represented by inferences of the following form (also called probabilistic explanations):

where A and B are one-place predicates in a first order language, P(A | B) is the conditional probability of observing A among individuals having property B, and q is close to one.

This argument is not logically valid, as the premises may be true while the conclusion is false. Moreover, as it stands, the premises do not even make the conclusion plausible. It may be the case that 90% of the population dies before the age of 85, and that individual j is a member of the population, but this in itself does not make it plausible that individual j will die before the age of 85.

In his recent book Scientific Explanation and the Causal Structure of the World Wesley Salmon provides a detailed explication of objective homogeneity, a concept which is central to his Statistical-Relevance (S-R) model of explanation. One of the purposes of Salmon’s explication is to refute Hempel’s thesis of the epistemic relativity of statistical explanation. According to this thesis “the concept of statistical explanation for particular events is essentially relative to a given knowledge situation” (Hempel 1965, pp. 402-403, quoted in Salmon 1984, p. 48). Salmon introduces (1984, p. 55) the concept that forms the basis for his S-R model as follows: “A reference class A is homogeneous with respect to an attribute B provided there is no set of properties Ci (1 ≤i ≤ k; k ≥ 2) in terms of which A can be relevantly partitioned.

The scheme of abstract dynamical systems will represent repetitive experimentation: There is a basic space of events X1 and the denumerable product … contains all possible sequences of events x = (x1, x2, … ). There are projections qn which give the nth member of x: qn (x) = xn. A transformation T is defined over X by the equation qn (Tx)= q n+1 (x). It removes the sequence by one step, T(x1 ,x2 ,…) = (x2 ,x3 ,…) and is known as the shift transformation. It comes as an abstraction of the dynamical transformations of classical theories. Here it represents the performance of ‘the next’ experiment.

The expected utility of an option for a decision maker is defined with respect to probability and utility functions that represent the decision maker’s beliefs and desires. Therefore, as the decision maker’s beliefs and desires change, the expected utility of an opinion may change. Some options are such that their realizations change beliefs and desires in ways that change the expected utilities of the options. If a decision is made among options that include one or more of these special options, I call it a decision made in a dynamic setting.

The rule to maximize expected utility, MEU, is insufficient for decisions in dynamic settings. It does not say how to take account of information about the way in which the realization of an option would change the expected utility of the option.