The applicability of Nagel's concept of theory reduction, and related concepts of reduction, to the reduction of genetics to molecular biology is examined using the lactose operon in Escherichia coli as an example. Geneticists have produced the complete nucleotide sequence of two of the genes which compose this operon. If any example of reduction in genetics should fit Nagel's analysis, the lactose operon should. Nevertheless, Nagel's formal conditions of theory reduction are inapplicable in this case. Instead, it is argued that (...) genetics has been partially reduced to molecular biology in the sense of token-token reduction. (shrink)

The burden of the present essay is to argue in favor of a proposition which is obviously true: that hypothesis choice in science is largely a rational procedure. This proposition needs arguing for because there is no philosophical theory, generally accepted as adequate, which explains why science is, or explains how science can be, rational. The main obstacles to an acceptable philosophical theory on this matter are the problems of induction . These problems seem to tell us that no amount (...) of evidence can in any way raise the probability or increase the rational believability of any hypothesis not implied by the evidence. The approach taken here is to give up on solving the problems of induction and to attempt to explain scientific rationality on some other basis. There are three main elements of this theory: rational hypothesis choice is explicated as rational acceptance of hypotheses, rather than belief; pragmatic criteria are employed to limit--always tentatively--the hypotheses under consideration; choice among hypotheses is to be made in accordance with rules that have the following property: if the true hypothesis is under consideration, then the probability of choosing the true hypothesis is greater than 0.5. Such rules correspond to what are called hypothesis testing situations in statistics. The theory is illustrated and developed through a number of examples. (shrink)

Reasoning almost always occurs in the face of incomplete information. Such reasoning is nonmonotonic in the sense that conclusions drawn may later be withdrawn when additional information is obtained. There is an active literature on the problem of modeling such nonmonotonic reasoning, yet no category of method-let alone a single method-has been broadly accepted as the right approach. This paper introduces a new method, called sweeping presumptions, for modeling nonmonotonic reasoning. The main goal of the paper is to provide an (...) example-driven, nontechnical introduction to the method of sweeping presumptions, and thereby to make it plausible that sweeping presumptions can usefully be applied to the problems of nonmonotonic reasoning. The paper discusses a representative sample of examples that have appeared in the literature on nonmonotonic reasoning, and discusses them from the point of view of sweeping presumptions. (shrink)

This article presents a new proposal for understanding the establishment and maintenance of cooperation: the cooperation afforder with framing hypothesis, producing what can be called cooperation from afforder-framing . Three key moves are present. First, a special variety of the Stag Hunt game, the Cooperation Afforder game, will reliably produce mutualistic cooperation through an evolutionary process. Second, cognitive framing is a credible candidate mechanism to meet the special conditions and requirements of the Cooperation Afforder game. Third, once mutualistic cooperation is (...) established in this way, it will plausibly lead to broader forms of cooperation, even to limited forms of altruism. (shrink)

Confirmation functions are generally thought of as probability functions. The well known difficulties associated with the probabilistic confirmation functions proposed to date indicate that functions other than probability functions should be investigated for the purpose of developing an adequate basis for confirmation theory. This paper deals with one such function, the likelihood function. First, it is argued here that likelihood is not a probability function. Second, a proof is given that, in the limit, likelihood can be used to determine which (...) of two observationally distinct hypotheses is true. Finally, a demonstration is given that, in the presence of a finite amount of experimental information, likelihood can serve as a good estimator of truth. (shrink)