Theo Kuipers

The philosophy of science has lost its self-confidence. Structures in Science (2001) is an advanced textbook that explicates, updates and integrates the best insights of logical empiricism and its main critics. This "neo-classical approach" aims at providing heuristic patterns for research.The book introduces four ideal types of research programs (descriptive, explanatory, design and explicative) and reanimates the distinction between observational laws and proper theories without assuming a theory-free language. It explicates various patterns of explanation by subsumption and specification as well as structures in reductive and other types of interlevel research. Its threefold analysis of theory evaluation leads to new characterizations of confirmation, empirical progress, and truth approximation. What emerges are partial analogies between progress in nomological research, presented in detail in From Instrumentalism to Constructive Realism (2000) and progress in explicative and design research. Finally, special chapters are devoted to design research programs, computational philosophy of science, the structuralist approach to theories, and research ethics.The present synopsis of Structures in Science highlights the main topics, the final emphasis being on design research and research ethics.

In this article I give a naturalistic-cum-formal analysis of therelation between beauty, empirical success, and truth. The analysis is based on the onehand on a hypothetical variant of the so-called `mere-exposure effect'' which has been more orless established in experimental psychology regarding exposure-affect relationshipsin general and aesthetic appreciation in particular (Zajonc 1968; Temme 1983; Bornstein 1989;Ye 2000). On the other hand it is based on the formal theory of truthlikeness andtruth approximation as presented in my From Instrumentalism to Constructive Realism (2000).The analysis supports the findings of James McAllister in his beautifulBeauty and Revolution in Science (1996), by explaining and justifying them.First, scientists are essentially right in regarding aesthetic criteria useful for empiricalprogress and even for truth approximation, provided they conceive of them as less hard thanempirical criteria. Second, the aesthetic criteria of the time, the `aesthetic canon'', maywell be based on `aesthetic induction'' regarding nonempirical features of paradigms of successfultheories which scientists have come to appreciate as beautiful. Third, aestheticcriteria can play a crucial, schismatic role in scientific revolutions. Since they may well be wrong, they may, in the hands of aesthetic conservatives, retard empirical progress and hence truth approximation, but this does not happen in the hands of aesthetically flexible, `revolutionary'' scientists. We may find totallyopposite things beautiful: a simplemathematical principle as well as a series of unrepeatable complex contingencies. It is a matter of psychology.(Stephen Jay Gould, translated passage from (Kayzer 2000, 30)).

This paper primarily deals with theconceptual prospects for generalizing the aim ofabduction from the standard one of explainingsurprising or anomalous observations to that ofempirical progress or even truth approximation. Itturns out that the main abduction task then becomesthe instrumentalist task of theory revision aiming atan empirically more successful theory, relative to theavailable data, but not necessarily compatible withthem. The rest, that is, genuine empirical progress aswell as observational, referential and theoreticaltruth approximation, is a matter of evaluation andselection, and possibly new generation tasks forfurther improvement. The paper concludes with a surveyof possible points of departure, in AI and logic, forcomputational treatment of the instrumentalist taskguided by the `comparative evaluation matrix''.

Surprisingly enough, modified versions of the confirmation theory of Carnap and Hempel and the truth approximation theory of Popper turn out to be smoothly synthesizable. The glue between confirmation and truth approximation appears to be the instrumentalist methodology, rather than the falsificationist one.By evaluating theories separately and comparatively in terms of their successes and problems (hence even if they are already falsified), the instrumentalist methodology provides – both in theory and in practice – the straight route for short-term empirical progress in science in the spirit of Laudan. However, it is argued that such progress is also functional for all kinds of truth approximation: observational, referential, and theoretical. This sheds new light on the long-term dynamic of science and hence on the relation between the main epistemological positions, viz., instrumentalism (Toulmin, Laudan), constructive empiricism (van Fraassen), referential realism (Hacking and Cartwright), and theory realism of a non-essentialist nature (Popper), here called constructive realism.In From Instrumentalism to Constructive Realism (2000) the above story is presented in great detail. The present synopsis highlights the main ways of theory evaluation presented in that book, viz. evaluation in terms of confirmation (or falsification), empirical progress and truth approximation.

In this paper, we address the problem of truth approximation through theory change, asking whether revising our theories by newly acquired data leads us closer to the truth about a given domain. More particularly, we focus on “nomic conjunctive theories”, i.e., theories expressed as conjunctions of logically independent statements concerning the physical or, more generally, nomic possibilities and impossibilities of the domain under inquiry. We define both a comparative and a quantitative notion of the verisimilitude of such theories, and identify suitable conditions concerning the (partial) correctness of acquired data, under which revising our theories by data leads us closer to “the nomic truth”, construed as the target of scientific inquiry. We conclude by indicating some further developments, generalizations, and open issues arising from our results.

In earlier publications of the first author it was shown that intentional explanation of actions, functional explanation of biological traits and causal explanation of abnormal events share a common structure. They are called explanation by specification (of a goal, a biological function, an abnormal causal factor, respectively) as opposed to explanation by subsumption under a law. Explanation by specification is guided by a schematic train of thought, of which the argumentative steps not concerning questions were already shown to be logically valid (elementary) arguments.Independently, the second author developed a new, inferential approach to erotetic logic, the logic of questions. In this approach arguments resulting in questions, with declarative sentences and/or other questions as premises, are analyzed, and validity of such arguments is defined.

The main formal notion involved in qualitative truth approximation by the HD-method, viz. ‘more truthlike’, is shown to not only have, by its definition, an intuitively appealing ‘model foundation’, but also, at least partially, a conceptually plausible ‘consequence foundation’. Moreover, combining the relevant parts of both leads to a very appealing ‘dual foundation’, the more so since the relevant methodological notions, viz. ‘more successful’ and its ingredients provided by the HD-method, can be given a similar dual foundation. According to the resulting dual foundation of ‘naive truth approximation’, the HD-method provides successes (established true consequences) and counterexamples (established wrongly missing models) of theories. Such HD-results may support the tentative conclusion that one theory seems to remain more successful than another in the naive sense of having more successes and fewer counterexamples. If so, this provides good reasons for believing that the more successful theory is also more truthlike in the naive sense of having more correct models and more true consequences.

In this article I give a naturalistic-cum-formal analysis of the relation between beauty, empirical success, and truth. The analysis is based on the one hand on a hypothetical variant of the so-called 'mere-exposure effect' which has been more or less established in experimental psychology regarding exposure-affect relationships in general and aesthetic appreciation in particular (Zajonc 1968; Temme 1983; Bornstein 1989; (Ye 2000). On the other hand it is based on the formal theory of truthlikeness and truth approximation as presented in my From Instrumentalism to Constructive Realism (2000). The analysis supports the findings of James McAllister in his beautiful Beauty and Revolutiorl in Science (1996), by explaining and justifying them. First, scientists are essentially right in regarding aesthetic criteria useful for empirical progress and even for truth approximation, provided they conceive of them as less hard than empirical criteria. Second, the aesthetic criteria of the time, the 'aesthetic canon', may well be based on 'aesthetic induction' regarding nonempirical features of paradigms of successful theories which scientists have come to appreciate as beautiful. Third, aesthetic criteria can play a crucial, schismatic role in scientific revolutions. Since they may well be wrong, they may, in the hands of aesthetic conservatives, retard empirical progress and hence truth approximation, but this does not happen in the hands of aesthetically flexible, 'revolutionary' scientists

This paper primarily deals with the conceptual prospects for generalizing the aim of abduction from the standard one of explaining surprising or anomalous observations to that of empirical progress or even truth approximation. It turns out that the main abduction task then becomes the instrumentalist task of theory revision aiming at an empirically more successful theory, relative to the available data, but not necessarily compatible with them. The rest, that is, genuine empirical progress as well as observational, referential and theoretical truth approximation, is a matter of evaluation and selection, and possibly new generation tasks for further improvement. The paper concludes with a survey of possible points of departure, in AI and logic, for computational treatment of the instrumentalist task guided by the ‘comparative evaluation matrix’

This paper primarily deals with the conceptual prospects for generalizing the aim of abduction from the standard one of explaining surprising or anomalous observations to that of empirical progress or even truth approximation. It turns out that the main abduction task then becomes the instrumentalist task of theory revision aiming at an empirically more successful theory, relative to the available data, but not necessarily compatible with them. The rest, that is, genuine empirical progress as well as observational, referential and theoretical truth approximation, is a matter of evaluation and selection, and possibly new generation tasks for further improvement. The paper concludes with a survey of possible points of departure, in AI and logic, for computational treatment of the instrumentalist task guided by the ‘comparative evaluation matrix’

Three related intuitions are explicated in this paper. The first is the idea that there must be some kind of probabilistic version of the HD-method, a ‘Hypothetico-Probabilistic (HP-) method’, in terms of something like probabilistic consequences, instead of deductive consequences. According to the second intuition, the comparative application of this method should also be functional for some probabilistic kind of empirical progress, and according to the third intuition this should be functional for something like probabilistic truth approximation. In all three cases, the guiding idea is to explicate these intuitions by explicating the crucial notions as appropriate ‘concretizations’ of their deductive analogs, being ‘idealizations’. It turns out that the comparative version of the proposed HP-method amounts to the likelihood comparison (LC-) method applied to the cumulated evidence. This method turns out to be not only functional for probabilistic empirical progress but also for probabilistic truth approximation. The latter is based on a probabilistic threshold theorem constituting for this reason the analog of the deductive success theorem.

Straightforward theory revision, taking into account as effectively as possible the established nomic possibilities and, on their basis induced empirical laws, is conducive for (unstratified) nomic truth approximation. The question this paper asks is: is it possible to reconstruct the relevant theory revision steps, on the basis of incoming evidence, in AGM-terms? A positive answer will be given in two rounds, first for the case in which the initial theory is compatible with the established empirical laws, then for the case in which it is incompatible with at least one such a law

So far, the most prominent measure for actual truthlikeness, i.e. the likeness of a theory to the actual truth, is Ilkka Niiniluoto’s minsum definition, which is purely based on distances. A competing definition is the average distance measure proposed by Pavel Tichy and Graham Oddie. We will define three related, distance and size based, measures for actual truthlikeness and compare them with the two well-known options. However, we will start, Sect. 2, from a trio of such measures for nomic truthlikeness. The nomic truth, or the true theory, here refers to what is nomically, e.g. physically, possible. In a nomic (and factual) context there are two basic kinds of theories, viz. either based on an exclusion claim or on an inclusion claim. Two-sided theories combine these claims, with the maximal claim as extreme special case. We will base truthlikeness measures for exclusion, inclusion, two-sided, and hence maximal, nomic theories on two similarity measures, one in terms of distances between conceptual possibilities and the other in terms of sizes of sets of such possibilities. In Sect. 3 we will treat actual truthlikeness as extreme special case of nomic truthlikeness, viz. assuming that there is just one nomic possibility, the actual one. Next we will compare the resulting measures mutually and with the above mentioned measures of Niiniluoto and Tichy & Oddie. Finally, in Sect. 4, we will sum up the results and explore five questions for further research.