This essay extends my “invisible disagreement” argument for Color Realism (2017) to formulate an argument for Direct Realism. It uses a variation of an “inverted qualia” thought experiment to show that successes in intersubjectively validating empirical claims about colors is proof that a nuanced version of Direct Realism is correct.

partial review of Thomas Nagel’s book, Mind and Cosmos: Why the Materialist Neo-Darwinian Conception of Nature Is Almost Certainly False is used to articulate some systems-theoretic ideas about the challenge of understanding subjective experience. The article accepts Nagel’s view that reductionist materialism fails as an approach to this challenge, but argues that seeking an explanation of mind based on emergence is more plausible than seeking one based on pan-psychism, which Nagel favors. However, the article proposes something similar to Nagel’s neutral (...) monism by positing a hierarchy of information processes that spans the domains of matter, life, and mind. As depicted in this hierarchy, subjective experience is emergent, but also continuous with informational phenomena at lower levels. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...) simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

When did the concept of model begin to be used in mathematics? This question appears at first somewhat surprising since “model” is such a standard term now in the discourse on mathematics and “modelling” such a standard activity that it seems to be well established since long. The paper shows that the term— in the intended epistemological meaning—emerged rather recently and tries to reveal in which mathematical contexts it became established. The paper discusses various layers of argumentations and reflections in (...) order to unravel and reach the pertinent kernel of the issue. The specific points of this paper are the difference in the epistemological concept to the usually discussed notions of model and the difference between conceptions implied in mathematical practices and their becoming conscious in proper reflections of mathematicians. (shrink)

According to the truth-functional analysis of conditions, to be ‘necessary for’ and ‘sufficient for’ are converse relations. From this, it follows that to be ‘necessary and sufficient for’ is a symmetric relation, that is, that if P is a necessary and sufficient condition for Q, then Q is a necessary and sufficient condition for P. This view is contrary to common sense. In this paper, I point out that it is also contrary to a widely accepted ontological view of conditions, (...) according to which if P is a necessary and sufficient condition for Q, then Q is in no sense a condition for P; it is a mere consequence of P. (shrink)

A characterization of the modelling process in science is proposed for science education, based on Mario Bunge’s ideas about the construction of models in science. Galileo’s Dialogues are analysed as a potentially fruitful starting point to implement strategies aimed at modelling in the classroom in the light of that proposal. It is argued that a modelling process for science education can be conceived as the evolution from phenomenological approaches towards more representational ones, emphasizing the role of abstraction and idealization in (...) model construction. The shift of reference of theories—from sensible objects to conceptual objects—and the black-box models construction process, which are both explicitly presented features in Galileo’s Dialogues, are indicated as highly relevant aspects for modelling in science education. (shrink)

Original research is of course what scientists are expected to do. Therefore the research project is in many ways the unit of science in the making: it is the center of the professional life of the individual scientist and his coworkers. It is also the means towards the culmination of their specific activities: the original publication they hope to contribute to the scientific literature. The scientific project should therefore be of central interest to all the students of science, particularly the (...) philosophers and sociologists of science. We shall focus on the preliminary evaluation of research projects—the specific task of referees—and will emphasize the problem of their scientificity—the chief concern of scientific gatekeepers. In the past such an examination aimed only at protecting the taxpayer from swindlers and incompetent amateurs, such as the inventors of continuous motion artifacts. In recent times a similar issue has resurfaced with regard to some of the most prestigious and most handsomely funded projects, namely work on string theory and many-worlds cosmology. Indeed, some of their faithful have claimed that these theories are so elegant, and so full of high-grade mathematics, that they should be exempted from empirical tests. This claim provoked the spirited rebuttal of the well-known cosmologists Ellis and Silk, which the present paper is intended to reinforce. Indeed, we shall try to show why empirical testability is necessary though insufficient for a piece of work to qualify as scientific. Finally, the present paper may also be regarded as an indirect contribution to the current debate over the reliability of quantitative indicators of scientific worth, such as the h-index of scientific productivity. But we shall touch only tangentially on the sociological, political, and economics of research teams: our focus will be the acquisition of new scientific knowledge. (shrink)

Explicatures are not cancellable. Theoretical considerations.

In view of the critique of the methodology of the dominant interdisciplinary re-search involving language studies as the main component, in particular clinical linguistics, Cummings (2014) proposes that “It is perhaps appropriate at this point to move the debate onto non-empirical grounds.” In Cummings (2014: 113) she starts such a debate on the grounds of the philosophy of language and pragmatics. In this article, I propose to expand that debate by including the input of the philosophy of science. I start (...) the discussion by presenting the way one may carry out language research in the paradigm of empirical sciences from the perspective outlined in Bunge (1967, 1973, 2003) and constrained by Altmann’s (1978) assumption about self-originating and self-regulatory nature of language. (shrink)

Metascientific ontology differs from philosophical ontologies in its objectives, objects and methods. By an examination of the ontological theories of Mario Bunge, we will show their main objective is a unified representation of the world as known through the sciences, that their objects of study are scientific concepts, and that their methods do not differ from those that one expects to find in any rational activity. Metascientific ontology is therefore not transcendent because it does not seek to represent non-concrete objects (...) alien to the world we inhabit and to the sciences that study it, and therefore does not need special faculties and methods to carry out its research. Metascientific ontology is a general scientific discourse on the world because it was designed by and for the sciences. (shrink)

L’ontologie métascientifique se distingue des ontologies philosophiques par ses objectifs, ses objets et ses méthodes. Par un examen des théories ontologiques de Mario Bunge, nous montrerons que leur principal objectif est l’élaboration d’une représentation unifiée du monde tel que connu via les sciences, que leurs objets d’étude sont les concepts scientifiques, et que leurs méthodes ne diffèrent pas de celles qu’on s’attend à trouver dans toute activité rationnelle. L’ontologie métascientifique n’est donc pas transcendante parce qu’elle ne cherche pas à représenter (...) des objets étrangers au monde que nous habitons et aux sciences qui l’étudient, et par conséquent elle n’a pas besoin de facultés ni de méthodes spéciales pour mener à bien ses recherches. L’ontologie métascientifique est un discours général scientifique sur le monde parce que conçue par et pour les sciences. (shrink)

This paper explores various functions of idealizations in quantum field theory. To this end it is important to first distinguish between different kinds of theories and models of or inspired by quantum field theory. Idealizations have pragmatic and cognitive functions. Analyzing a case-study from hadron physics, I demonstrate the virtues of studying highly idealized models for exploring the features of theories with an extremely rich structure such as quantum field theory and for gaining some understanding of the physical processes in (...) the system under consideration. (shrink)

In this talk, I want to present a conception that I have been working on for a number of years (Zwick, 1983, 1995) about the use of systems ideas. I start from the negative and proceed to the positive. The negative assertion is that systems ideas by themselves , unsupplemented by more specific and concrete knowledge (e.g., from the various disciplines), are insufficient for practical application, either for obtaining knowledge about the world or for solving problems. The reason for this (...) is the inherent abstractness of these ideas. The positive assertion is that this metaphysics can be a rich source of insights needed to improve the human condition and a component of a new scientific world view. (shrink)

Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics is a case in point. This theory is supposed to govern all strong interactions, but it is extremely hard to apply and test at energies where protons, neutrons and ions are the effective degrees of freedom. Instead, scientists typically use highly idealized models such as the MIT Bag Model or the Nambu Jona-Lasinio Model to account for phenomena in (...) this domain, to explain them and to gain nderstanding. Based on these models, which typically isolate a single feature of QCD and disregard many others, scientists attempt to get a better understanding of the physics of strong interactions. But does this practice make sense? Is it justified to use these models for the purposes at hand? Interestingly, these models do not even provide an accurate description of the mass spectrum of protons, neutrons and pions and their lowest lying excitations well - despite several adjustable parameters. And yet, the models are heavily used. I'll argue that a qualitative story, which establishes an explanatory link between the fundamental theory and a model, plays an important role in model acceptance in these cases. (shrink)

Systems theory offers a language in which one might formulate a metaphysics (or more specifically an ontology) of problems. This proposal is based upon a conception of systems theory shared by vonBertalanffy, Wiener, Boulding, Rapoport, Ashby, Klir, and others,and expressed succinctly by Bunge, who considered game theory, information theory, feedback control theory, and the like to be attempts to construct an "exact and scientific metaphysics." Our prevailing conceptions of "problems" are concretized yet also fragmented, and in fact dissolved, by the (...) standard reductionist model of science, which cannot provide a general framework for analysis. The idea of a "systems theory," however, suggests the possibility of an abstract and coherent account of the origin and essence of problems. Such an account would constitute a secular theodicy. (shrink)