Scientific Realism, Adaptationism and the Problem of the Criterion

Scientific Realism (SR) has three crucial aspects: 1) the centrality of the concept of truth, 2) the idea that success is a reliable indicator of truth, and 3) the idea that the Inference to the Best Explanation is a reliable inference rule. It will be outlined how some realists try to overcome the difficulties which arise in justifying such crucial aspects relying on an adaptationist view of evolutionism, and why such attempts are inadequate. Finally, we will briefly sketch some of the main difficulties the realist has to face in defending those crucial aspects, and how such difficulties are deeply related: they derive from the inability of SR to satisfyingly avoid the sceptical challenge of the criterion of truth. Indeed, SR seems not to be able to fill the so-called ‘epistemic gap’ (Sankey 2008). In fact, the epistemic gap cannot be filled in no way other than obtaining a criterion of truth, but such a criterion cannot be obtained if the epistemic gap obtains

Scientific discovery reloaded

The way scientific discovery has been conceptualized has changed drastically in the last few decades: its relation to logic, inference, methods, and evolution has been deeply reloaded. The ‘philosophical matrix’ moulded by logical empiricism and analytical tradition has been challenged by the ‘friends of discovery’, who opened up the way to a rational investigation of discovery. This has produced not only new theories of discovery (like the deductive, cognitive, and evolutionary), but also new ways of practicing it in a rational and more systematic way. Ampliative rules, methods, heuristic procedures and even a logic of discovery have been investigated, extracted, reconstructed and refined. The outcome is a ‘scientific discovery revolution’: not only a new way of looking at discovery, but also a construction of tools that can guide us to discover something new. This is a very important contribution of philosophy of science to science, as it puts the former in a position not only to interpret what scientists do, but also to provide and improve tools that they can employ in their activity

What Can Artificial Intelligence Do for Scientific Realism?

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling

Towards a Model of Argument Strength for Bipolar Argumentation Graphs

UID/FIL/00183/2013Bipolar argument graphs represent the structure of complex pro and contra arguments for one or more standpoints. In this article, ampliative and exclusionary principles of evaluating argument strength in bipolar acyclic argumentation graphs are laid out and compared to each other. Argument chains, linked arguments, link attackers and supporters, and convergent arguments are discussed. The strength of conductive arguments is also addressed but it is argued that more work on this type of argument is needed to properly distinguish argument strength from more general value-based components of such argu- ments. The overall conclusion of the article is that there is no justifiably unique solution to the problem of argument strength outside of a particular epistemological framework.publishersversionpublishe

Abduction as a Mode of Inference in Science Education

The central argument of this article is that abduction as a “mode of inference” is a key element in the nature of scientists’ science and should consequently be introduced in school science. Abduction generally understood as generation and selection of hypotheses permits to articulate the classical scientific contexts of discovery and justification and provides educational insights into scientific methodology, this being a particularly important issue in science teaching. However, abductive reasoning has been marginally treated in the philosophy of science until relatively recently; accordingly, we deem it important to perform an “archaeology” of the concept that considers C. S. Peirce’s seminal contributions. We also choose to review contemporary treatments in order to recognise useful classifications to support more meaningful ways of teaching science and the nature of science. An elucidation of the participation of abductive inferences in knowledge construction seems necessary for us to derive conceptual input for the understanding and design of explanations in school science. Some prospective examples of “school scientific abduction” are discussed in the article through the lens of the results of our theoretical analysis.Fil: Aduriz Bravo, Agustin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Formación e Investigación en Enseñanza de las Ciencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sans Pinillos, Alger. Universita degli Studi di Pavia; Itali

Is data the plural of anecdote? Inductive arguments in composition

College writing classes are the ideal site for teaching argument. Writing students develop arguments with a frequency and insistence not present in other disciplines. Typically, however, when their curricula include reasoning instruction, composition courses over-emphasize deductive syllogisms and en-thymemes. Inductive logic, the recognition of a pattern within a data set or an ampliative inference, is more useful in composition, and an effective composition curriculum makes ample room for the study and prac-tice of inductive arguments

Learning from Experience: A Philosophical Perspective

This work examines philosophical solutions to David Hume’s problem of induction—a skeptical attack on our ability to learn from experience. I explore the logical, ontological, and epistemic difficulties behind the everyday assumption that the future will resemble the past. While historical solutions by philosophers such as Bertrand Russell and Karl Popper have been unsuccessful at tackling these complications, combining recent work on natural kinds and naturalistic epistemology has promise. Ultimately, I expand on work done by Howard Sankey, Hilary Kornblith, and Brian Ellis to create an account of nature and epistemology that explains why objects in nature have predictable behavior. I find Sankey\u27s solution incomplete, but I fix the major I identify and show why the work by Sankey builds into a powerful solution to Hume\u27s problem

Scientific realism, adaptationism and the problem of the criterion

Scientific Realism (SR) has three crucial aspects: 1) the centrality of the concept of truth, 2) the idea that success is a reliable indicator of truth, and 3) the idea that the Inference to the Best Explanation is a reliable inference rule. It will be outlined how some realists try to overcome the difficulties which arise in justifying such crucial aspects relying on an adaptationist view of evolutionism, and why such attempts are inadequate. Finally, we will briefly sketch some of the main difficulties the realist has to face in defending those crucial aspects, and how such difficulties are deeply related: they derive from the inability of SR to satisfyingly avoid the sceptical challenge of the criterion of truth. Indeed, SR seems not to be able to fill the so-called ‘epistemic gap’ (Sankey 2008). In fact, the epistemic gap cannot be filled in no way other than obtaining a criterion of truth, but such a criterion cannot be obtained if the epistemic gap obtains

The Role of Inversion in the Genesis, Development and the Structure of Scientific Knowledge

The main thrust of the argument of this thesis is to show the possibility of articulating a method of construction or of synthesis--as against the most common method of analysis or division--which has always been (so we shall argue) a necessary component of scientific theorization. This method will be shown to be based on a fundamental synthetic logical relation of thought, that we shall call inversion--to be understood as a species of logical opposition, and as one of the basic monadic logical operators. Thus the major objective of this thesis is to This thesis can be viewed as a response to Larry Laudan's challenge, which is based on the claim that ``the case has yet to be made that the rules governing the techniques whereby theories are invented (if any such rules there be) are the sorts of things that philosophers should claim any interest in or competence at.'' The challenge itself would be to show that the logic of discovery (if at all formulatable) performs the epistemological role of the justification of scientific theories. We propose to meet this challenge head on: a) by suggesting precisely how such a logic would be formulated; b) by demonstrating its epistemological relevance (in the context of justification) and c) by showing that a) and b) can be carried out without sacrificing the fallibilist view of scientific knowledge. OBJECTIVES: We have set three successive objectives: one general, one specific, and one sub-specific, each one related to the other in that very order. (A) The general objective is to indicate the clear possibility of renovating the traditional analytico-synthetic epistemology. By realizing this objective, we attempt to widen the scope of scientific reason or rationality, which for some time now has perniciously been dominated by pure analytic reason alone. In order to achieve this end we need to show specifically that there exists the possibility of articulating a synthetic (constructive) logic/reason, which has been considered by most mainstream thinkers either as not articulatable, or simply non-existent. (B) The second (specific) task is to respond to the challenge of Larry Laudan by demonstrating the possibility of an epistemologically significant generativism. In this context we will argue that this generativism, which is our suggested alternative, and the simplified structuralist and semantic view of scientific theories, mutually reinforce each other to form a single coherent foundation for the renovated analytico-synthetic methodological framework. (C) The third (sub-specific) objective, accordingly, is to show the possibility of articulating a synthetic logic that could guide us in understanding the process of theorization. This is realized by proposing the foundations for developing a logic of inversion, which represents the pattern of synthetic reason in the process of constructing scientific definitions