Ioannis G. Ntagkas

This paper examines the idea of "objective phenomenology," or a way of understanding the phenomenal character of conscious experiences that doesn’t require one to have had the kinds of experiences under consideration. My central thesis is that structural facts about experience—facts that characterize purely how conscious experiences are structured—are objective phenomenal facts. I begin by precisifying the idea of objective phenomenology and diagnosing what makes any given phenomenal fact subjective. Then I defend the view that structural facts about experience are objective. I also argue that structural facts about experience, despite being objective, nevertheless still give rise to an explanatory gap.

Sadi Carnot’s 1824 Reflections on the Motive Power of Fire created the new science of thermodynamics. It succeeded in its audacious goal of finding a very general theory of the efficiency of heat engines, by introducing and exploiting the strange and unexpected notion of a thermodynamically reversible process. The notion is internally contradictory. It requires the states of these processes to be both in unchanging equilibrium, with a perfect balance of driving forces, while also changing. The work of Sadi’s father, Lazare Carnot, on the efficiency of ordinary machines provided Sadi with a template of a very general theory of the efficiency of ordinary machines; and a characterization of the most efficient processes in them as those that minimize differences of driving forces and can be run in reverse. Lazare’s work could provide these resources because of its choice of a dissipative ontology of inelastic collisions among hard bodies. This historically ill-fated choice meant that Lazare’s machines were analogous to Sadi’s heat engines in their key aspects: they are built from essentially dissipative processes. Lazare’s strategies for controlling dissipation and optimizing his machines were transferrable to the analogous problems Sadi found in heat engines. The unanswerable historical question is whether Sadi would have sought a general theory of heat engines at all or found these general theoretical devices without the template provided in analogy by the prior work of Lazare.

Separatists about grounding take explanations to be separate from their corresponding grounding-facts. Grounding-facts are supposed to underlie, or back, such explanations. However, the backing relation hasn’t received much attention in the literature. The aim of this paper is to provide an informative definition of backing. First, I examine two prominent proposals: backing as explaining (Kovacs 2017; 2019a) and backing as grounding (see Sjölin Wirling 2020). Finally, I put forward my own proposal. I argue that under plausible assumptions about the role of backing and the nature of explanation, backing should be understood as a form of truthmaking, minimally construed.

This paper reviews the contemporary discussion on the epistemological and ontological effects of Big Data within social science, observing an increased focus on relationality and complexity, and a tendency to naturalize social phenomena. The epistemic limits of this emerging computational paradigm are outlined through a comparison with the discussions in the early days of digitalization, when digital technology was primarily seen through the lens of dematerialization, and as part of the larger processes of “postmodernity”. Since then, the online landscape has become increasingly centralized, and the “liquidity” of dematerialized technology has come to empower online platforms in shaping the conditions for human behavior. This contrast between the contemporary epistemological currents and the previous philosophical discussions brings to the fore contradictions within the study of digital social life: While qualitative change has become increasingly dominant, the focus has gone towards quantitative methods; while the platforms have become empowered to shape social behavior, the focus has gone from social context to naturalizing social patterns; while meaning is increasingly contested and fragmented, the role of hermeneutics has diminished; while platforms have become power hubs pursuing their interests through sophisticated data manipulation, the data they provide is increasingly trusted to hold the keys to understanding social life. These contradictions, we argue, are partially the result of a lack of philosophical discussion on the nature of social reality in the digital era; only from a firm metatheoretical perspective can we avoid forgetting the reality of the system under study as we are affected by the powerful social life of Big Data.

In the wake of the paleobiological revolution of the 1970s and 1980s, paleontologists continue to investigate far-reaching questions about how evolution works. Many of those questions have a philosophical dimension. How is macroevolution related to evolutionary changes within populations? Is evolutionary history contingent? How much can we know about the causes of evolutionary trends? How do paleontologists read the patterns in the fossil record to learn about the underlying evolutionary processes? Derek Turner explores these and other questions, introducing the reader to exciting recent work in the philosophy of paleontology and to theoretical issues including punctuated equilibria and species selection. He also critically examines some of the major accomplishments and arguments of paleontologists of the last 40 years.

Derek Turner, Professor of Philosophy, has written an introductory logic textbook that students at Connecticut College, or anywhere, can access for free. The book differs from other standard logic textbooks in its reliance on fun, low-stakes examples involving dinosaurs, a dog and his friends, etc. This work is published in 2020 under a Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. You may share this text in any format or medium. You may not use it for commercial purposes. If you share it, you must give appropriate credit. If you remix, transform, add to, or modify the text in any way, you may not then redistribute the modified text.

This paper discusses the relevance of supertask computation for the determinacy of arithmetic. Recent work in the philosophy of physics has made plausible the possibility of supertask computers, capable of running through infinitely many individual computations in a finite time. A natural thought is that, if supertask computers are possible, this implies that arithmetical truth is determinate. In this paper we argue, via a careful analysis of putative arguments from supertask computations to determinacy, that this natural thought is mistaken: supertasks are of no help in explaining arithmetical determinacy.

Evolutionary Debunking Arguments argue that, if naturalism and evolution are true, then our moral beliefs are merely human constructs nature selected because they increased our prospects for survival and reproduction. Atheist Erik Wielenberg disagrees; he has recently argued that morality could be objectively real, and that we could have moral knowledge, even if naturalism and evolution are true. I argue that Wielenberg is unsuccessful in his attempt to deflect a major concern raised by EDA proponents, namely, that moral knowledge would be extremely unlikely given naturalism and evolution because it would involve a vast amount of unexplained lucky coincidences.

_Logic: The Basics_ is a hands-on introduction to the philosophically alive field of logical inquiry. Covering both classical and non-classical theories, it presents some of the core notions of logic such as validity, basic connectives, identity, ‘free logic’ and more. This book: introduces some basic ideas of logic from a semantic and philosophical perspective uses logical consequence as the focal concept throughout considers some of the controversies and rival logics that make for such a lively field This accessible guide includes chapter summaries and suggestions for further reading as well as exercises and sample answers throughout. It is an ideal introduction for those new to the study of logic as well as those seeking to gain the competence and skills needed to move to more advanced work in logic.

Dennett has maintained that a careful examination of our intuitive notion of qualia reveals that it is a confused notion, that it is advisable to accept that experience does not have the properties designated by it and that it is best to eliminate it. Because most scientists share this notion of qualia, the major line of attack of his project becomes that of raising objections against the ability of science to answer some basic questions about qualia. I try to show that science appeals to qualia and that it in fact adheres to a notion of qualia different from the one that Dennett has attributed to it. It is argued that qualia are amenable to scientific investigation and that this is the reason why science contributes toward the clarification of the notion of qualia. I also try to show that Dennett's skepticism about the abilities of science in answering questions posited by one of his thought experiments is unwarranted. I conclude that we need not accept Dennett's eliminativism about qualia.

There are researchers in cognitive science who use clinical and experimental evidence to draw some rather skeptical conclusions about a central feature of our conscious experience, its unity. They maintain that the examination of clinical phenomena reveals that human consciousness has a much more fragmentary character than the one we normally attribute to it. In the article, these claims are questioned by examining some of the clinical studies on the deficit of anosognosia. I try to show that these studies support a moderate sense of the unity of reflexive consciousness

Heisenberg’s uncertainty principle is a milestone of twentieth-century physics. We sketch the history that led to the formulation of the principle, and we recall the objections of Grete Hermann and Niels Bohr. Then we explain that there are in fact two uncertainty principles. One was published by Heisenberg in the Zeitschrift für Physik of March 1927 and subsequently targeted by Bohr and Hermann. The other one was introduced by Earle Kennard in the same journal a couple of months later. While Kennard’s principle remains untarnished, the principle of Heisenberg has recently been criticized in a way that is very different from the objections by Bohr and Hermann: there are reasons to believe that Heisenberg’s formula is not valid. Experimental results seem to support this claim.

This article focuses on the conceptual relationship between awe and the experience of the sublime. I argue that the experience of the sublime is best conceived as a species of awe, namely, as aesthetic awe. I support this conclusion by considering the prominent conceptual relations between awe and the experience of the sublime, showing that all of the options except the proposed one suffer from serious shortcomings. In maintaining that the experience of the sublime is best conceived as aesthetic awe, I draw from historical theories of the sublime as well as recent work in empirical psychology.

Abstract Theories are metaphysically equivalent just if there is no fact of the matter that could render one theory true and the other false. In this paper I argue that if we are judiciously to resolve disputes about whether theories are equivalent or not, we need to develop testable criteria that will give us epistemic access to the obtaining of the relation of metaphysical equivalence holding between those theories. I develop such ?diagnostic? criteria. I argue that correctly inter-translatable theories are metaphysically equivalent, and what we need are ways of determining whether a putative translation is correct or not. To that end I develop a number of tools we can employ to discern whether a translation is a correct one

_Locked_ and _unlocked_ strategies are illustrated in this article as concepts that deal with important cognitive aspects of deep learning systems. They indicate different inference routines that refer to poor (locked) to rich (unlocked) cases of creative production of creative cognition. I maintain that these differences lead to important consequences when we analyze computational deep learning programs, such as AlphaGo/AlphaZero, which are able to realize various types of abductive hypothetical reasoning. These programs embed what I call locked abductive strategies, so, even if they present spectacular performances for example in games, they are characterized by poor types of hypothetical creative cognition insofar as they are constrained in what I call _eco-cognitive openness_. This openness instead characterizes unlocked human cognition that pertains to higher kinds of abductive reasoning, in both the creative and diagnostic cases, in which cognitive strategies are instead unlocked. This special kind of “openness” is physically rooted in the fundamental character of the human brain as an open system constantly coupled with the environment (that is, an “open” or “dissipative” system): its activity is the uninterrupted attempt to achieve the equilibrium with the environment in which it is embedded, and this interplay can never be switched off without producing severe damage to the brain. The brain cannot be conceived as deprived of its physical quintessence that is its openness. In the brain, contrary to the computational case, ordering is not derived from the outside thanks to what I have called in a recent book “computational domestication of ignorant entities”, but it is the direct product of an “internal” open dynamical process of the system.

I will analyse three fundamental ways of governing ignorance though abduction, which are essential from an eco-cognitive and eco-logical point of view, in which the central role in human cognition of natural and artefactual environment is taken into account. First of all, according to the so-called GW-schema, proposed by Gabbay and Woods, abduction presents an ignorance-preserving or (ignorance-mitigating) character: given the fact that the abduced hypotheses aim at becoming truths, the basic ignorance is neither solved nor left untouched. Second, I will contend that abduction is not always ignorance preserving but also knowledge enhancing. Indeed, according to the eco-cognitive model of abduction, I myself have recently proposed, we can easily see that, occasionally, knowledge can be immediately increased (and ignorance ‘killed’) through abduction: in this perspective, abduction does not have to be rigidly conceptualized as an inference to the best explanation in the received sense, which is as an inference that, to grant truth, also takes advantage of an empirical evaluation phase or an inductive phase, as Peirce classified it. Finally, it is important to stress that abductive cognition is at the roots of two fundamental cognitive processes. On one side, abductive cognition constructs (and/or takes advantage of the exploitation of) embodiments, external representations, enactions, artefacts and props, which involve ignorant entities suitably transformed in cognitive representations and/or devices. On the other side, abductive cognition dominates those ‘special’ cognitive creative processes that lead to what I call the disseminated ‘computational domestication of ignorant entities’: I will illustrate the example of the recent unconventional computational domestication of ignorant entities, which creates novel cognitive embodiments (morphological computation).

The “origins” of (geometric) space is examined from the perspective of the so-called “conceptual space” or “semantic space”. Semantic space is characterized by its fundamental “locality” that generates an “implicit” mode of geometrizing. This view is examined from within three perspectives. First, the role that various diagrammatic entities play in the everyday life and pragmatic activities of selected ethnic groups is illustrated. Secondly, it is shown how conceptual spaces are fundamentally linked to the meaning effects of particular natural languages and these are very different from the global and universal aspects of Euclidean spaces. Thirdly, it is contended that these modes of creating body and culture-based spatial frameworks and related cosmogonies and cosmologies can be described as forms of “latent geometry” that initially appear unexplainable in any rational way. Nonetheless, and thanks to the deep mathematical reflections provided by René Thom, it is illustrated how the various ways of generating space can be further analyzed as distortions of mainstream spatialization furnished by Euclidean geometry that established the dominant universality of the ideas of space (and time).

This book employs a new eco-cognitive model of abduction to underline the distributed and embodied nature of scientific cognition. Its main focus is on the knowledge-enhancing virtues of abduction and on the productive role of scientific models. What are the distinctive features that define the kind of knowledge produced by science? To provide an answer to this question, the book first addresses the ideas of Aristotle, who stressed the essential inferential and distributed role of external cognitive tools and epistemic mediators in abductive cognition. This is analyzed in depth from both a naturalized logic and an ecology of cognition perspective. It is shown how the maximization of cognition, and of abducibility – two typical goals of science – are related to a number of fundamental aspects: the optimization of the eco-cognitive situatedness; the maximization of changeability for both the input and the output of the inferences involved; a high degree of information-sensitiveness; and the need to record the “past life” of abductive inferential practices. Lastly, the book explains how some impoverished epistemological niches – the result of a growing epistemic irresponsibility associated with the commodification and commercialization of science – are now seriously jeopardizing the flourishing development of human creative abduction.

This handbook offers the first comprehensive reference guide to the interdisciplinary field of model-based reasoning. It highlights the role of models as mediators between theory and experimentation, and as educational devices, as well as their relevance in testing hypotheses and explanatory functions. The Springer Handbook merges philosophical, cognitive and epistemological perspectives on models with the more practical needs related to the application of this tool across various disciplines and practices. The result is a unique, reliable source of information that guides readers toward an understanding of different aspects of model-based science, such as the theoretical and cognitive nature of models, as well as their practical and logical aspects. The inferential role of models in hypothetical reasoning, abduction and creativity once they are constructed, adopted, and manipulated for different scientific and technological purposes is also discussed. Written by a group of internationally renowned experts in philosophy, the history of science, general epistemology, mathematics, cognitive and computer science, physics and life sciences, as well as engineering, architecture, and economics, this Handbook uses numerous diagrams, schemes and other visual representations to promote a better understanding of the concepts. This also makes it highly accessible to an audience of scholars and students with different scientific backgrounds. All in all, the Springer Handbook of Model-Based Science represents the definitive application-oriented reference guide to the interdisciplinary field of model-based reasoning.

This Handbook offers the first comprehensive reference guide to the interdisciplinary field of abductive cognition, providing readers with extensive information on the process of reasoning to hypotheses in humans, animals, and in computational machines. It highlights the role of abduction in both theory practice: in generating and testing hypotheses and explanatory functions for various purposes and as an educational device. It merges logical, cognitive, epistemological and philosophical perspectives with more practical needs relating to the application of abduction across various disciplines and practices, such as in diagnosis, creative reasoning, scientific discovery, diagrammatic and ignorance-based cognition, and adversarial strategies. It also discusses the inferential role of models in hypothetical reasoning, abduction and creativity, including the process of development, implementation and manipulation for different scientific and technological purposes. Written by a group of internationally renowned experts in philosophy, logic, general epistemology, mathematics, cognitive, and computer science, as well as life sciences, engineering, architecture, and economics, the Handbook of Abductive Cognition offers a unique reference guide for readers approaching the process of reasoning to hypotheses from different perspectives and for various theoretical and practical purposes. Numerous diagrams, schemes and other visual representations are included to promote a better understanding of the relevant concepts and to make concepts highly accessible to an audience of scholars and students with different scientific backgrounds.

We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive non-classical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logically correct formalizations for each of the individual arguments. At the next layer we select among those correct formalizations the ones which honor the argument’s dialectic role, i.e. attacking or supporting other arguments as intended. We operate at these two layers in parallel and continuously rate sentences’ formalizations by using, primarily, inferential adequacy criteria. An interpretive, logical theory will thus gradually evolve. This theory is composed of meaning postulates serving as explications for concepts playing a role in the analyzed arguments. Such a recursive, iterative approach to interpretation does justice to the inherent circularity of understanding: the whole is understood compositionally on the basis of its parts, while each part is understood only in the context of the whole. We summarily discuss previous work on exemplary applications of human-in-the-loop computational hermeneutics in metaphysical discourse. We also discuss some of the main challenges involved in fully-automating our approach. By sketching some design ideas and reviewing relevant technologies, we argue for the technological feasibility of a highly-automated computational hermeneutics.