Online research in philosophy

The place of induction in the framing and test of scientific hypotheses is investigated. The meaning of 'induction' is first equated with generalization on the basis of case examination. Two kinds of induction are then distinguished: the inference of generals from particulars (first degree induction), and the generalization of generalizations (second degree induction). Induction is claimed to play a role in the framing of modest empirical generalizations and in the extension of every sort of generalizations--not however in the invention of high-level hypotheses containing theoretical predicates. It is maintained, on the other hand, that induction by enumeration is essential in the empirical test of the lowest-level consequences of scientific theories, since it occurs in the drawing of "conclusions" from the examination of empirical evidence. But it is also held that the empirical test is insufficient, and must be supplemented with theorification, or the expansion of isolated hypotheses into theories. Refutation is not viewed as a substitute for confirmation but as its complement, since the very notion of unfavorable case is meaningful only in connection with the concept of positive instance. Although the existence of an inductive method is disclaimed, it is maintained that the various patterns of plausible reasoning (inductive inference included) are worth being investigated. It is concluded that scientific research follows neither the advice of inductivism nor the injunction of deductivism, but takes a middle course in which induction is instrumental both heuristically and methodologically, although the over-all pattern of research is hypothetico-deductive.

Some desiderata for scientific confirmation are formulated in the light of a tentative scientific world view. Bayesian confirmation theories generically satisfy most of these desiderata, but one of them, "the strategy of ascent," fits best in a tempered personalist version of Bayesianism. There are both empirical and rational components, dialectically combined, in tempered personalism. The question of explanation vs. prediction is treated in a Bayesian manner, and it is found that both operations are susceptible to characteristic systematic errors. If these are eliminated, however, then explanation and prediction provide equally good evidential support for hypotheses.

In this paper, I investigate the nature of a priori biological laws in connection with the idea that laws must be empirical. I argue that the epistemic functions of a priori biological laws in biology are the same as those of empirical laws in physics. Thus, the requirement that laws be empirical is idle in connection with how laws operate in science. This result presents a choice between sticking with an unmotivated philosophical requirement and taking the functional equivalence of laws seriously and modifying our philosophical account. I favor the latter.

This article explores whether theory-ladenness makes empirical testing an inse cure foundation for objectivity. Specifically, this article uses path diagrams as visual heuristics to assist in (1) developing a parsimonious representation of the traditional empiricist view of empirical testing, (2) showing how the "New Image" view ostensibly threatens the objectivity of science, (3) proposing a unified, realist theory of empirical testing, (4) developing a representation of the unified theory, (5) exploring several potential threats to objectivity, (6) discussing the proposed theory's implications for social science, and (7) adumbrating three foundational premises for scientific realism.

In assessing the appropriateness of a scientific community's research effort, Solomon considers a number of "decision vectors," divided into the empirical and non-empirical. Value judgments get sorted as non-empirical vectors. By way of contrast, I introduce Anderson's discussion of the evidential role of value judgments. Like Anderson, I argue that value judgments are empirical in the relevant sense. I argue further that Solomon's decision matrix needs to be reconceptualized: the distinction should not be between the empirical vs. non-empirical, but between the relevant vs. irrelevant. Whether particular value judgments are relevant or not is an empirical question, to be decided on a case-by-case basis.

In this paper I distinguish various ways in which empirical claims about evolutionary and ecological models can be supported by data. I describe three basic factors bearing on confirmation of empirical claims: fit of the model to data; independent testing of various aspects of the model, and variety of evident. A brief description of the kinds of confirmation is followed by examples of each kind, drawn from a range of evolutionary and ecological theories. I conclude that the greater complexity and precision of my approach, as compared to, for instance, a Popperian approach, can facilitate detailed analysis and comparison of empirical claims.

Epistemologists and philosophers of science have often attempted to express formally the impact of a piece of evidence on the credibility of a hypothesis. In this paper we will focus on the Bayesian approach to evidential support. We will propose a new formal treatment of the notion of degree of confirmation and we will argue that it overcomes some limitations of the currently available approaches on two grounds: (i) a theoretical analysis of the confirmation relation seen as an extension of logical deduction and (ii) an empirical comparison of competing measures in an experimental inquiry concerning inductive reasoning in a probabilistic setting.

Whereas an inference (deductive as well as inductive) is usually viewed as being valid in virtue of its argument form, the present paper argues that scientific reasoning is material inference, i.e., justified in virtue of its content. A material inference is licensed by the empirical content embodied in the concepts contained in the premises and conclusion. Understanding scientific reasoning as material inference has the advantage of combining different aspects of scientific reasoning, such as confirmation, discovery, and explanation. This approach explains why these different aspects (including discovery) can be rational without conforming to formal schemes, and why scientific reasoning is local, i.e., justified only in certain domains and contingent on particular empirical facts. The notion of material inference also fruitfully interacts with accounts of conceptual change and psychological theories of concepts.

Having assigned experience this exclusive role in justification, empiricists then have a range of views concerning the character of experience, the semantics of our claims about unobservable entities, the nature of empirical confirmation, and the possibility of non-empirical warrant for some further class of claims, such as those accepted on the basis of linguistic or logical rules. Given the definitive principle of their position, empiricists can allow that we have knowledge independent of experience only where what is known is not some objective fact about the world, but something about our way of conceptualizing or describing things. Some empiricists say we have knowledge of verbal equivalences or trivialities; some argue that any non-empirical tenets are not even properly called knowledge, but should be seen as notions accepted on pragmatic rather than properly epistemic grounds. What no empiricist will allow is substantive a priori knowledge: according to empiricism we have no pure rational insight into real necessities or the inner structure of nature, but must rely on the deliverances of our senses for all of our information about external reality. Some versions of empiricism argue against the very notion of real necessities or metaphysical structure behind the phenomena; other versions take a more agnostic approach, arguing that if there is a metaphysical structure behind the phenomena it is either out of our epistemic reach, or known only to the extent that it can be grasped through experience, rather than through rational reflection.

It is argued that the relation of instance confirmation has a role to play in scientific methodology that complements, rather than competing with, a modern account of inductive support such as Bayesian confirmation theory. When an instance confirms a hypothesis, it provides inductive support, but it also provides two things that other inductive supporters normally do not: first, a connection to “empirical data” that makes science epistemically special, and second, inductive support not only for the hypothesis as a whole, but for its parts. Further, when it is conceived in the right way, instance confirmation can duck the arguments most often thought to refute it. A causal account of instantiation, thus of instance confirmation, is offered that looks to deliver on all of the foregoing promises.