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1. The Monist: Volume > 97 > Issue: 4
Erratum
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2. The Monist: Volume > 97 > Issue: 4
Francesco Berto, Frederick Kroon, Alberto Voltolini Metaontology: Introduction
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3. The Monist: Volume > 97 > Issue: 4
Graham Priest Sein Language
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4. The Monist: Volume > 97 > Issue: 4
Jody Azzouni Freeing Talk of Nothing from the Cognitive Illusion of Aboutness
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Rejecting Quine’s criterion, strictly speaking, yields the neutralist interpretation of the quantifiers. Quantifiers, by virtue of their logical role, impose no metaphysical conditions whatsoever. According to neutralism, the bare (or formal) quantifier-role can be supplemented with varous metaphysical constraints, or none. A comparison between Pure Metaphysical Deflationism (a position directly motivated by the neutralist interpretation of the quantifiers) and Aboutness Meinongianism is undertaken. Evaluating the evidential role of aboutness intuitions is the primary tool for determining whether Aboutness Meinongianism is rightabout there being an aboutness relationship between the cognitive experience of what does not exist (and aboutness relations of nonreferring quantifiers and terms) and what does not exist.
5. The Monist: Volume > 97 > Issue: 4
Chris Daly, David Liggins In Defence of Existence Questions
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6. The Monist: Volume > 97 > Issue: 4
Peter Van Inwagen Fictionalist Nominalism and Applied Mathematics
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7. The Monist: Volume > 97 > Issue: 4
Cian Dorr Quantifier Variance and the Collapse Theorems
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8. The Monist: Volume > 97 > Issue: 4
Kyle Mitchell Pragmatism, Joint-Carving, and Ontology
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9. The Monist: Volume > 97 > Issue: 4
Giorgio Lando, Giuseppe Spolaore Transcendental Disagreement
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In metaphysical theorizing, it is common to use expressions whose function is that of denoting or being true of absolutely everything. Adopting a scolastic term, these may be called ‘transcendentals’. Different metaphysical theories may adopt different transcendentals, the most usual candidates being ‘thing’, ‘entity’, ‘object’, ‘be’, ‘exist’, and their counterparts in various languages dead or alive. We call ‘transcendental disagreement’ any dissent between philosophical theories or traditions that may be described as a disagreement in the choice of transcendentals. Examples of transcendental disagreement include the debate about Lewis’s conception of actuality and a number of recent discussions between Meinongians and their ‘Quinean’ rivals. A case is made for the conclusion that, as such, transcendental disagreements are purely terminological in character. It is argued that transcendental disagreements are generally to be assessed on pragmatic rather than on metaphysical grounds. Finally, the paper makes a start in the analysis of those pragmatic grounds which include the pursuit of univocity, fairness, and ease of comparison between theories.
10. The Monist: Volume > 97 > Issue: 4
Scope of Forthcoming Issues
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11. The Monist: Volume > 97 > Issue: 4
Contents of Volume 97
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12. The Monist: Volume > 97 > Issue: 3
Anouk Barberousse, Cyrille Imbert Recurring Models and Sensitivity to Computational Constraints
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Why are some models, like the harmonic oscillator, the Ising model, a few Hamiltonian equations in quantum mechanics, the poisson equation, or the Lokta-Volterra equations, repeatedly used within and across scientific domains, whereas theories allow for many more modeling possibilities? Some historians and philosophers of science have already proposed plausible explanations. For example, Kuhn and Cartwright point to a tendency toward conservatism in science, and Humphreys emphasizes the importance of the intractability of what he calls “templates.” This paper investigates more systematically the reasons for this remarkable interdisciplinary recurrence. To this aim, the authors describe in more detail the phenomenon they focus on and review competing potentialexplanations. The authors disentangle the various assumptions underlying these explanations based on sensitivity to a computational constraints and assess its relationships with the other analyzed explanatons.
13. The Monist: Volume > 97 > Issue: 3
Magnets, Spins, and Neurons: The Dissemination of Model Templates Across Disciplines
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One of the most conspicuous features of contemporary modeling practices is the dissemination of mathematical and computational methods across disciplinary boundaries. We study this process through two applications of the Ising model: the Sherrington–Kirkpatrick model of spin glasses and the Hopfield model of associative memory. The Hopfield model successfully transferred some basic ideas and mathematical methods originally developed within the study of magnetic systems to the field of neuroscience. As an analytical resource we use Paul Humphreys’s discussion of computational and theoretical templates. We argue that model templates are crucial for the intra- and interdisciplinary theoretical transfer. A model template is an abstract conceptual idea associated with particular mathematical forms and computational methods.
14. The Monist: Volume > 97 > Issue: 3
Seth Bullock Levins and the Lure of Artificial Worlds
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What is it about simulation models that has led some practitioners to treat them as potential sources of empirical data on the real-world systems being simulated; that is, to treat simulations as ‘artificial worlds’within which to perform computational ‘experiments’? Here we use the work of Richard Levins as a starting point in identifying the appeal of this model building strategy, and proceed to account for why this appeal is strongest for computational modellers. This analysis suggests a perspective on simulation modelling that makes room for ‘artificial worlds’ as legitimate science without having to accept that they should be treated as sources of empirical data.
15. The Monist: Volume > 97 > Issue: 3
Alisa Bokulich How the Tiger Bush Got Its Stripes: ‘How Possibly’ vs. ‘How Actually’Model Explanations
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Simulations using idealized numerical models can often generate behaviors or patterns that are visually very similar to the natural phenomenon being investigated and to be explained. The question arises, when should these model simulations be taken to provide an explanation (or part of an explanation) for why the natural phenomena exhibit the patterns that they do? An important distinction for answering this question is that between ‘how-possibly’ explanations and ‘how-actually’ explanations. Despite the importance of this distinction there has been surprisingly little agreement over how exactly this distinction should bedrawn. I shall argue that inadequate attention has been paid to the different contexts in which an explanation can be given and the different levels of abstraction at which the explanandum phenomenon can be framed. By tracing how scientists are using model simulations to explain a striking periodic banding of vegetation known as tiger bush, I will show how our understanding of the distinction between how-possibly and how-actually model explanations needs to be revised.
16. The Monist: Volume > 97 > Issue: 3
Johannes Lenhard Autonomy and Automation: Computational Modeling, Reduction, and Explanation in Quantum Chemistry
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This paper discusses how computational modeling combines the autonomy of models with the automation of computational procedures. In particular, the case of ab-initio methods in quantum chemistry will be investigated to draw two lessons from the analysis of computational modeling. The first belongs to general philosophy of science: Computational modeling faces a trade-off and enlarges predictive force at the cost of explanatory force. The other lesson is about the philosophy of chemistry: The methodology of computational modeling puts into doubt claims about the reduction of chemistry to physics.
17. The Monist: Volume > 97 > Issue: 3
Eckhart Arnold What’s Wrong with Social Simulations?
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This paper tries to answer the question why the epistemic value of so many social simulations is questionable. I consider the epistemic value of a social simulation as questionable if it contributes neither directly nor indirectly to the understanding of empirical reality. To examine this question, two classical social simulations are analyzed with respect to their possible epistemic justification: Schelling’s neighborhood segregation model (Schelling 1971) and Axelrod’s reiterated Prisoner’s Dilemma simulations of the evolution of cooperation (Axelrod 1984). It is argued that Schelling’s simulation is useful because it can be related to empirical reality, while Axelrod’s simulations and those of his followers cannot and thus that their scientific value remains doubtful. I relate this findingto the background beliefs of modelers about the superiority of the modeling method as expressed in Joshua Epstein’s keynote address “Why model?” (Epstein 2008).
18. The Monist: Volume > 97 > Issue: 3
Otávio Bueno Computer Simulations: An Inferential Conception
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In this paper, I offer an inferential conception of computer simulations, emphasizing the role that simulations play as inferential devices to represent empirical phenomena. Three steps are involved in a simulation: an immersion step (from aspects of the empirical set up to the simulated model), a derivation step (that yields the relevant results), and an interpretation and correction step (that interprets the results in light of the empirical set up). After presenting the view, I mention some cases, such as simulations of the current flow between silicon atoms and buckyballs as well as of genetic regulatory systems. I argue that the inferential conception accommodates the integration of empirical and theoretical data; it makes sense of the role that is played by false traits in a simulation, andhighlights the similarities and differences between simulations and scientific instruments.
19. The Monist: Volume > 97 > Issue: 3
Claus Beisbart Are We Sims? How Computer Simulations Represent and What this Means for the Simulation Argument
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N. Bostrom’s simulation argument and two additional assumptions imply that we likely live in a computer simulation. The argument is based upon the following assumption about the workings of realistic brain simulations: The hardware of a computer on which a brain simulation is run bears a close analogy to the brain itself. To inquire whether this is so, I analyze how computer simulations trace processes in their targets. I describe simulations as fictional, mathematical, pictorial, and material models. Even though the computer hardware does provide a material model of the target, this does not suffice to underwrite the simulation argument because the ways in which parts of the computer hardware interact during simulations do not resemble the ways in which neurons interact in the brain. Further, there are computer simulations of all kinds of systems, and it would be unreasonable to infer that some computers display consciousness just because they simulate brains rather than, say, galaxies.
20. The Monist: Volume > 97 > Issue: 2
Leonardo Caffo Foreword
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