Abstract This article applies the Copernican principle and Gott’s delta- t method to the infamous Humean problem of induction. Copernican reasoning uses only the observed duration of a phenomenon to predict its future duration, so this reasoning is noncircular. It also does not involve positing necessary connections, as do many other attempts to answer Hume’s challenge. Copernican reasoning is controversial, as are many of its applications, but the application to Hume’s problem is one that even most critics of the Copernican principle should accept. If Copernican reasoning is ever applicable, it is applicable here. Copernicus might have an answer for Hume.
Abstract In this article, we study difficult theory-choice situations, where division of cognitive labor is needed. Network epistemology models suggest that reducing connectivity is needed to prevent premature convergence on bad theories. We compare how network density, community size, strength of prior beliefs, adaptive learning methods, and weak ties influence epistemic outcomes, and show that reducing connectivity is only one possible way to improve collective epistemic accuracy. Our findings suggest that gains in accuracy often come at a high cost in resources used, which should be considered when results from network epistemology models are used in applied settings.
Abstract Text-analytic methods increasingly promise to transform surviving textual traces into measurements of past psychological attributes. This paper argues that such claims can mistake technical success for representational warrant. A scoring regime may perform well within a constructed evidential space without thereby validating the construct-to-trace relation required for measurement. The central problem is endogenous validation: the same operations that make texts scoreable also delimit the evidence by which success is assessed. Strong measurement claims therefore require credible conditions under which the underlying representational mapping could fail.
Abstract Missing-model problems are a type of problem that arises when scientists are trying to gain access to initially inaccessible phenomena. This article describes what missing-model problems are and examines examples of them from seismology, optics, and the theory of gases. It is argued that the examination of missing-model problems will give us a better understanding of the challenges scientists face when attempting to gain access. Two themes for the further study of missing-model problems are laid out: justification and representation.
Abstract This paper articulates how recent advances in comparative behavioral and biological research are changing the nature of the mind/body problem from a human mind/human body problem to a problem of conceptualizing and organizing diverse types of minds in a hierarchical relational structure that is non-contingently related to phylogeny. It also discusses the impact of this refinement on our assessments of modal claims about possible artificial (AI) consciousness.
Abstract This paper argues that when brain networks figure in explanations of cognition and behavior they often do so in conjunction with independent dynamical assumptions about signal transmission in the brain. In such cases explanation does not depend on network structure alone but on network structure operating in conjunction with dynamical assumptions. Moreover, dynamical assumptions embody causal information, so that the resulting explanations are not entirely non-causal. In addition, it is argued that the directional features of explanations that appeal to networks can be understood in terms of the independence of network structure and dynamics.
Abstract While scientific narratives have often been studied in relation to causality and explanation, not much attention has been given to their non-causal content. This less explored aspect of narratives is addressed here. I argue that colligatory inferences underlie narrative connections and become significant when they help constitute new scientific phenomena from scattered information. Through a sedimentological case study, I identify kinship relations as non-causal connections that provide continuity to changing phenomena without relying on causal explanations. These relations depend on more basic affinities between apparently unrelated phenomena. As I will defend, such connections contribute to a non-explanatory form of understanding.
Abstract Model organisms play a central role in biological research, yet the conceptual questions they raise continue to provoke debate among philosophers. This article examines one such question: What are the distinctive features that qualify model organisms as scientific models? Building on and extending previous work, we propose a refined conceptual framework for understanding model organisms in the context of contemporary scientific practice. Our framework highlights six key features that, collectively, uniquely characterize model organisms. We argue that model organisms should not be viewed merely as components within experimental systems but rather as autonomous experimental systems in their own right.
Abstract Teleparallel Gravity (TPG) is an alternative, but empirically equivalent, spacetime theory to General Relativity. In its modern formulation, TPG purports to be both a gauge theory of translations (G), as well as locally Lorentz-invariant (L). However, the reasoning invoked in order to implement (L) and (G) is often involved. As such, clarification of the reasoning upon which TPG proponents rely in constructing their theory is sorely needed. The present paper will address this need. It will also offer a succinct methodolog ical assessment of TPG as a theory per se .
Abstract In this paper, I distinguish between three different roles that symmetries play in physics practice that are not always distinguished in the philosophical literature and which seem to clash with each other. I then propose that the best way to make sense of these roles requires introducing a distinction between two different kinds of symmetries. One kind concerns invertible transformations that map solutions to solutions. The other is associated with the fact that one can use the same equation to model a system in many different frames.
Abstract Philosophers often defend appeals to parsimony by invoking its central role in science. I argue that this move fails once we distinguish between two uses of parsimony: non-ideal and ideal . Non-ideal parsimony enjoys strong inductive support in science, since complex models are prone to overfit to predictively irrelevant noise. But philosophical data aren’t significantly noisy in the relevant sense: when our intuitions are unreliable, their unreliability typically reflects systematic bias rather than noise, which parsimony doesn’t mitigate. Philosophers therefore need ideal parsimony, which finds only weak support from science. Thus, the scientific analogy cannot vindicate the philosopher’s use of parsimony.
Pragmatist philosophers of science often adjudicate realism debates by (1) deploying a unified ontological principle-that is, a unified rule for making ontological assertions-to all domains of empirical inquiry, and (2) comparing the ontological assertions made by realists and antirealists to the unified principle. By considering modeling practices in evolutionary biology, I motivate an alternative approach. Pragmatists should localize ontological principles to particular ontological questions in particular domains of empirical inquiry and adjudicate between various realisms and antirealisms in a radically piecemeal fashion.
The notion that theories of information and computation can augment and even complete thermodynamics has proven too enticing for many to resist, even though careful analysis has long shown that the notion fails. In so far as the results of this information-computation theoretic literature succeed, they are merely tendentious relabeling of mundane thermodynamics. When they go beyond it, they fail. The difficulties include an unsustainable conflation by Landauer's principle of the dynamic probabilities of thermalization with the static probabilities of memory devices. The most serious failure is an enduring neglect of the import of thermodynamic fluctuations.
