Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...

One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth "all at once". The second, which we call (...) AE convergence, demands only that for every sentence in the theorist's language, there is a time at which the theorist settles the status of the sentence. The relative difficulties of these criteria are compared for effective and ineffective agents. We then examine in detail how the enrichment of an agent's hypothesis language makes the task of converging to the truth more difficult. In particular, we parametrize first-order languages by predicate and function symbol arity, presence or absence of identity, and quantifier prefix complexity. For nearly each choice of values of these parameters, we determine the senses in which effective and ineffective agents can converge to the complete truth on an arbitrary structure for the language. Finally, we sketch directions in which our learning theoretic setting can be generalized or made more realistic. (shrink)

The realist programme has degenerated by now to the point where it is quite beyond salvage. A token of this degeneration is that there are altogether too many realisms. It is as though by splitting into a confusing array of types and kinds, realism has hoped that some one variety might yet escape extinct. I shall survey the debate, and some of these realisms, below. Here I would just point out the obvious; that in so far as the successes of (...) science mount while realism continues to decline we must conclude that scientific success lends no support to realism. Since it is unlikely to support anti-realism, we have some reason to suspect that the philosophical debate over realism does not concern issues that can be settled by developments in the sciences, no matter how successful science may be. Further, since that success grounds a culture of acceptance for science and its entities, we have reason to believe that the existence of those entities is also not actually the issue that concerns realism. Afortiori, it is not the issue that concerns anti-realism either; nor, I might add, is anti-realism the winner in the philosophical debate that realism has lost. (shrink)

This paper develops lines of criticism directed at two currently popular versions of anti-realism: the putnam-rorty-kuhn version that is centered on an acceptance theory of truth, and the van fraassen version that is centered on empiricist strictures over warranted beliefs. the paper continues by elaborating and extending a stance, called "the natural ontological attitude", that is neither realist nor anti-realist.

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal learning theory.In (...) a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh. (shrink)

This volume of original essays assesses Nozick's analyses of knowledge and evidence and his approach to skepticism. Several of the contributors claim that Nozick has not succeeded in rebutting the skeptic; some offer fresh accounts of skepticism and its flaws; others criticize Nozick's externalist accounts of knowledge and evidence; still others welcome externalism but attempt to replace Nozick's accounts of knowledge and evidence with more plausible analyses.

Some Philosophical Prehistory of General Relativity As history, my remarks will form rather a medley. If they can claim any sort of unity (apart from a ...

Determinism is a perennial topic of philosophical discussion. Very little acquaintance with the philosophical literature is needed to reveal the Tower of ...