This book is concerned with the problem of applying the theory of verisimilitude to cognitive problems of a quantitative nature. Attention is mostly focused on hypotheses concerned with (physical or other) systems whose state can be represented with an element of a multidimensional state space, but hypotheses concerned with quantitative laws are also considered. The book provides a systematic introduction to the main contemporary forms of the theory of verisimilitude, including both proposed definitions of quantitative degrees of verisimilitude and proposed (...) definitions of the relation `closer to the truth than'. It shows why the quantitative measures that have been proposed earlier produce unacceptable results in the multidimensional case and, using the tools of geometric measure theory, works out alternatives for them in detail. In addition, the standard structuralist theory of science and the way in which the problems of approximation are dealt with in it are presented and evaluated critically. (shrink)

I discuss the behavior of the Akaike Information Criterion in the limit when the sample size grows. I show the falsity of the claim made recently by Stanley Mulaik in Philosophy of Science that AIC would not distinguish between saturated and other correct factor analytic models in this limit. I explain the meaning and demonstrate the validity of the familiar, more moderate criticism that AIC is not a consistent estimator of the number of parameters of the smallest correct model. I (...) also give a short explanation why this feature of AIC is compatible with the motives of using it. (shrink)

The philosophical significance of the procedure of applying Akaike Information Criterion (AIC) to curve-fitting problems is evaluated. The theoretical justification for using AIC (the so-called Akaike's theorem) is presented in a rigorous way, and its range of validity is assessed by presenting both instances in which it is valid and counter-examples in which it is invalid. The philosophical relevance of the justification that this result gives for making one particular choice between simple and complicated hypotheses is emphasized. In addition, recent (...) claims that the methods based on Akaike's theorem are relevant to other philosophical problems associated with the notion of simplicity are presented and evaluated. (shrink)

J. P. Z. Bonilla's methodological approach to truthlikeness is evaluated critically. On a more general level, various senses in which the theory of truthlikeness could be seen as a theory concerned with methodology are distinguished, and it is argued that providing speical sciences with methodological tools is unrealistic as an aim of the theory of verisimilitude. Rather, when developing this theory, one should rest contnet with the more modest aim of conceptual analysis, or of providing explications for the relational concept (...) of being closer to the truth. In addition, some remarks will be made on the difficulties which the similarity approach to truthlikeness has in realizing this aim and which are caused by the important role that Hintikka's constituents have in it. (shrink)

A qualitative theory of truthlikeness, based on a family of quantitative measures, is developed for hypotheses that are concerned with the values of a finite number of real-valued quantities. Representing hypotheses by subsets of $R^{n}$ , I first show that a straightforward application of the basic ideas of the similarity approach to truthlikeness does not work out for hypotheses with zero n-dimensional Lebesgue measure. However, it is easy to give a counterpart for the average measure preferred by Pavel Tichý and (...) Graham Oddie in terms of Hausdorff measures. The task of finding analogies of the min-sum-measure preferred by Ilkka Niiniluoto is more interesting. I present and discuss analogies of two different types for this measure. (shrink)

I discuss the philosophical significance of the statistical model selection criteria, in particular their relevance for philosophical of underdetermination. I present an easily comprehensible account of their simplest possible application and contrast it with their application to curve-fitting problems. I embed philosophers' earlier discussion concerning the situations in which the criteria yield implausible results into a more general framework. Among other things, I discuss a difficulty which is related to the so-called subfamily problem, and I show that it has analogies (...) in all legitimate applications of the model selection criteria, and that an analogy of Goodman's new riddle of induction can be formulated in only some of their applications. (shrink)

Two Bayesian approaches to choosing between statistical models are contrasted. One of these is an approach which Bayesian statisticians regularly use for motivating the use of AIC, BIC, and other similar model selection criteria, and the other one is a new approach which has recently been proposed by Bandyopadhayay, Boik, and Basu. The latter approach is criticized, and the basic ideas of the former approach are presented in a way that makes them accessible to a philosophical audience. It is also (...) pointed out that the former approach establishes a new, philosophically interesting connection between the notions of simplicity and informativeness. (shrink)

Two Bayesian approaches to choosing between statistical models are contrasted. One of these is an approach which Bayesian statisticians regularly use for motivating the use of AIC, BIC, and other similar model selection criteria, and the other one is a new approach which has recently been proposed by Bandyopadhayay, Boik, and Basu. The latter approach is criticized, and the basic ideas of the former approach are presented in a way that makes them accessible to a philosophical audience. It is also (...) pointed out that the former approach establishes a new, philosophically interesting connection between the notions of simplicity and informativeness. (shrink)

I discuss the behavior of the Akaike Information Criterion in the limit when the sample size grows. I show the falsity of the claim made recently by Stanley Mulaik in Philosophy of Science that AIC would not distinguish between saturated and other correct factor analytic models in this limit. I explain the meaning and demonstrate the validity of the familiar, more moderate criticism that AIC is not a consistent estimator of the number of parameters of the smallest correct model. I (...) also give a short explanation why this feature of AIC is compatible with the motives for using it. (shrink)

The nature of the distinction between rational andnon-rational accounts of the development of science isanalyzed. These two kinds of accounts differ mostlyin the status which they give to methodologicalprinciples. It is shown that there are severaldimensions with respect to which the status of suchprinciples can resemble more or less the kind ofstatus that a paradigmatic rational account would givethem. It is concluded that, under the most plausibledefinitions of a rational account, the extent to whicha philosophical account of scientific change isrational (...) is a matter of degree, and that there is nosuch clear distinction between the two kinds ofaccounts which some supporters of rational and ofnon-rational accounts have assumed to exist. (shrink)

Reviews the book "The Vienna Circle. Studies in the Origins, Development, and Influence of Logical Empiricism," by Friedrich Stadler.

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