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)). (shrink)

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''. (shrink)