SummaryThis paper addresses the question of what scientific realism implies and what it does not when it is articulated so as to provide the best defense against plausible philosophical alternatives. A summary is presented of “abductive” arguments for scientific realism, and of the epistemological and semantic conceptions upon which they depend. Taking these arguments to be the best current defense of realism, it is inquired what, in the sense just mentioned, realism implies and what it does not. It is concluded (...) that realism implies the strong rejection of epistemological foundationalism, a non‐Humean conception of causation and of explanation, and a causal rather than conceptual account of the unity of natural definitions. It is denied that realism implies bivalence or the existence of one true theory, one preferred vocabulary or one distinctly privileged science. It is further denied that realism implies that there are no unrecognized conventional aspects to scientific theorizing and it is denied that realism implies that scientists routinely do good experimental metaphysics. (shrink)

The fact that there exist in nature thoroughly deterministic systems whose future behavior cannot be predicted, no matter how advanced or fined-tune our cognitive and technical abilities turn out to be, has been well established over the last decades or so, essentially in the light of two different theoretical frameworks, namely chaos theory and (some deterministic interpretation of) quantum mechanics. The prime objective of this paper is to show that there actually exists an alternative strategy to ground the divorce between (...) determinism and predictability, a way that is older than—and conceptually independent from—chaos theory and quantum mechanics, and which has not received much attention in the recent philosophical literature about determinism. This forgotten strategy—embedded in the doctrine called “emergent evolutionism”—is nonetheless far from being a mere historical curiosity that should only draw the attention of philosophers out of their concern for comprehensiveness. It has been indeed recently revived in the works of respected scientists. (shrink)

The inference from determinism to predictability, though intuitively plausible, needs to be qualified in an important respect. We need to distinguish between two different kinds of predictability. On the one hand, determinism implies external predictability , that is, the possibility for an external observer, not part of the universe, to predict, in principle, all future states of the universe. Yet, on the other hand, embedded predictability as the possibility for an embedded subsystem in the universe to make such predictions, does (...) not obtain in a deterministic universe. By revitalizing an older result—the paradox of predictability —we demonstrate that, even in a deterministic universe, there are fundamental, non-epistemic limitations on the ability of one subsystem embedded in the universe to predict the future behaviour of other subsystems embedded in the same universe. As an explanation, we put forward the hypothesis that these limitations arise because the predictions themselves are physical events which are part of the law-like causal chain of events in the deterministic universe. While the limitations on embedded predictability cannot in any direct way show evidence of free human agency, we conjecture that, even in a deterministic universe, human agents have a take-it-or-leave-it control over revealed predictions of their future behaviour. (shrink)

The use of multiple means of determination to “triangulate” on the existence and character of a common phenomenon, object, or result has had a long tradition in science but has seldom been a matter of primary focus. As with many traditions, it is traceable to Aristotle, who valued having multiple explanations of a phenomenon, and it may also be involved in his distinction between special objects of sense and common sensibles. It is implicit though not emphasized in the distinction between (...) primary and secondary qualities from Galileo onward. It is arguably one of several conceptions involved in Whewell’s method of the “consilience of inductions” (Laudan 1971) and is to be found in several places in Peirce. (From M. Brewer and B. Collins, eds., (1981); Scientific Inquiry in the Social Sciences (a festschrift for Donald T. Campbell), San Francisco: Jossey-Bass, pp. 123–162.). (shrink)

In previous technical work ([1] and [2]) on which his present paper [3] draws, Benioff has presented results conforming with the following argument-scheme:First, if we construe Quantum Mechanics as making claims to the effect that infinite outcome sequences (generated by repeated measurements on a system for a given observable in a given state) be random; and second, if a strong definition of “random” is adopted in this construal, then certain models of Zermelo-Fraenkel set theory (ZF) cannot be “carriers for the (...) mathematics of physics”.How interesting is this? One can approach the matter on two levels, the level of the specific technical results, and the level of more general implications of this pattern of argument for understanding connections between mathematics and physics. The second more general level should be our primary focus, but it will pay at the outset to look briefly at the technical level. (shrink)

Philosophical discourse traditionally distinguishes between ontology and epistemology and generally enforces this distinction by keeping the two subject areas separated. However, the relationship between the two areas is of central importance to physics and philosophy of physics. For instance, many measurement-related problems force us to consider both our knowledge of the states and observables of a system and its states and observables independent of such knowledge. This applies to quantum systems in particular. This contribution presents an example showing the importance (...) of distinguishing between ontic and epistemic levels of description even for classical systems. Corresponding conceptions of ontic and epistemic states and their evolution are introduced and discussed with respect to aspects of stability and information flow. These aspects show why the ontic/epistemic distinction is particularly important for systems exhibiting deterministic chaos. Moreover, this distinction provides some understanding of the relationships between determinism, causation, predictability, randomness, and stochasticity. (shrink)

After a brief review of ontic and epistemic descriptions, and of subjective, logical and statistical interpretations of probability, we summarize the traditional axiomatization of calculus of probability in terms of Boolean algebras and its set-theoretical realization in terms of Kolmogorov probability spaces. Since the axioms of mathematical probability theory say nothing about the conceptual meaning of “randomness” one considers probability as property of the generating conditions of a process so that one can relate randomness with predictability (or retrodictability). In the (...) measure-theoretical codification of stochastic processes genuine chance processes can be defined rigorously as so-called regular processes which do not allow a long-term prediction. We stress that stochastic processes are equivalence classes of individual point functions so that they do not refer to individual processes but only to an ensemble of statistically equivalent individual processes. Less popular but conceptually more important than statistical descriptions are individual descriptions which refer to individual chaotic processes. First, we review the individual description based on the generalized harmonic analysis by Norbert Wiener. It allows the definition of individual purely chaotic processes which can be interpreted as trajectories of regular statistical stochastic processes. Another individual description refers to algorithmic procedures which connect the intrinsic randomness of a finite sequence with the complexity of the shortest program necessary to produce the sequence. Finally, we ask why there can be laws of chance. We argue that random events fulfill the laws of chance if and only if they can be reduced to (possibly hidden) deterministic events. This mathematical result may elucidate the fact that not all nonpredictable events can be grasped by the methods of mathematical probability theory. (shrink)

In “Well-Being and Pluralism” (2021), Polly Mitchell and Anna Alexandrova defend conceptual pluralism about well-being. Conceptual pluralism about well-being holds that there are multiple, irreducible concepts of well-being that are employed in different contexts, all equally legitimate as concepts of well-being. Moreover, “Conceptual pluralism about well-being entails that there is no single essence which characterises all and only instances of well-being.” (Mitchell and Alexandrova, 2021, p. 2422) Conceptual monism about well-being, on the other hand, holds, at a minimum, that there (...) is some essence that unites all legitimate uses of ‘well- being’. In this paper, I will argue that there is a version of conceptual monism about well-being, the network theory of well-being defended by Michael Bishop in his book The Good Life (2015), that can secure all of the benefits of Mitchell and Alexandrova’s conceptual pluralism, namely accommodating the wide range of uses of “well-being” in the sciences of well-being and in ordinary language. Mitchell and Alexandrova’s argument for pluralism depends on the inability of a monistic theory of well-being to account for this diversity of conceptions of well-being and the diversity of instruments used to measure well-being in the sciences. Bishop’s network theory, like Mitchell and Alexandrova’s pluralism, is designed to accommodate the scientific study of well-being. Because of this, it avoids the pitfalls of traditional, monistic conceptions of well-being despite its monistic credentials. I conclude with two novel arguments for the network theory. (shrink)

There is a persistent confusion about determinism and predictability. In spite of the opinions of some eminent philosophers, it is possible to understand that the two concepts are completely unrelated. In few words we can say that determinism is ontic and has to do with how Nature behaves, while predictability is epistemic and is related to what the human beings are able to compute. An analysis of the Lyapunov exponents and the Kolmogorov-Sinai entropy shows how deterministic chaos, although with an (...) epistemic character, is non subjective at all. This should clarify the role and content of stochastic models in the description of the physical world. (shrink)