Reichenbach, induction, and discovery

Conclusion I have applied a fairly general, learning theoretic perspective to some questions raised by Reichenbach's positions on induction and discovery. This is appropriate in an examination of the significance of Reichenbach's work, since the learning-theoretic perspective is to some degree part of Reichenbach's reliabilist legacy. I have argued that Reichenbach's positivism and his infatuation with probabilities are both irrelevant to his views on induction, which are principally grounded in the notion of limiting reliability. I have suggested that limiting reliability is still a formidable basis for the formulation of methodological norms, particularly when reliability cannot possibly be had in the short run, so that refined judgments about evidential support must depend upon measure-theoretic choices having nothing to do in the short run with the truth of the hypothesis under investigation. To illustrate the generality of Reichenbach's program, I showed how it can be applied to methods that aim to solve arbitrary assessment and discovery problems in various senses. In this generalized Reichenbachian setting, we can characterize the intrinsic complexity of reliable inductive inference in terms of topological complexity. Finally, I let Reichenbach's theory of induction have the last say about hypothetico-deductive method.     

Grounds and limits: Reichenbach and foundationalist epistemology

From 1929 onwards, C. I. Lewis defended the foundationalist claim that judgements of the form ‘x is probable’ only make sense if one assumes there to be a ground y that is certain (where x and y may be beliefs, propositions, or events). Without this assumption, Lewis argues, the probability of x could not be anything other than zero. Hans Reichenbach repeatedly contested Lewis’s idea, calling it “a remnant of rationalism”. The last move in this debate was a challenge by Lewis, defying Reichenbach to produce a regress of probability values that yields a number other than zero. Reichenbach never took up the challenge, but we will meet it on his behalf, as it were. By presenting a series converging to a limit, we demonstrate that x can have a definite and computable probability, even if its justification consists of an infinite number of steps. Next we show the invalidity of a recent riposte of foundationalists that this limit of the series can be the ground of justification. Finally we discuss the question where justification can come from if not from a ground.     

J. M. Keynes's position on the general applicability of mathematical,logical and statistical methods in economics and social science

The author finds no support for the claim that J. M. Keynes had severe reservations, in general, as opposed to particular, concerning the application of mathematical, logical and statistical methods in economics. These misinterpretations rest on the omission of important source material as well as a severe misconstrual ofThe Treatise on Probability (1921).The author acknowledges substantial help from Jaakko Hintikka, but is necessarily responsible for any shortcomings.     

Reichenbach on the relative a priori and the context of discovery/justification distinction

Hans Reichenbach introduced two seemingly separate sets of distinctions in his epistemology at different times. One is between the axioms of coordination and the axioms of connections. The other distinction is between the context of discovery and the context of justification. The status and nature of each of these distinctions have been subject-matter of an ongoing debate among philosophers of science. Thus, there is a significant amount of works considering both distinctions separately. However, the relevance of Reichenbach’s two distinctions to each other does not seem to have enjoyed the same amount of interest so far. This is what I would like to consider in this paper. In other words, I am concerned with the question: what kind of relationship is there between his two distinctions, if there is any?