We study versions of limit models adapted to the context of metric abstract elementary classes. Under categoricity and superstability-like assumptions, we generalize some theorems from 7, 15-17. We prove criteria for existence and uniqueness of limit models in the metric context.

Chemistry and physics are two sciences that are hard to connect. Yet there is significant overlap in their aims, methods, and theoretical approaches. In this book, the reduction of chemistry to physics is defended from the viewpoint of a naturalised Nagelian reduction, which is based on a close reading of Nagel's original text. This naturalised notion of reduction is capable of characterising the inter-theory relationships between theories of chemistry and theories of physics. The reconsideration of reduction also leads to a (...) new characterisation of chemical theories. This book is primarily aimed at philosophers of chemistry and chemists with an interest in philosophy, but is also of interest to the general philosopher of science. (shrink)

We prove the following main theorem: Let be an abstract elementary class satisfying the joint embedding and the amalgamation properties with no maximal models of cardinality μ. Let μ be a cardinal above the the Löwenheim‐Skolem number of the class. If is μ‐Galois‐stable, has no μ‐Vaughtian Pairs, does not have long splitting chains, and satisfies locality of splitting, then any two ‐limits over M, for, are isomorphic over M.

We study notions of independence appropriate for a stability theory of metric abstract elementary classes (for short, MAECs). We build on previous notions used in the discrete case, and adapt definitions to the metric case. In particular, we study notions that behave well under superstability‐like assumptions. Also, under uniqueness of limit models, we study domination, orthogonality and parallelism of Galois types in MAECs.

The surge in interest in natural language processing in artificial intelligence has led to an explosion of new language models capable of engaging in plausible language use. But ensuring these language models produce honest, helpful, and inoffensive outputs has proved difficult. In this paper, I argue problems of inappropriate content in current, autoregressive language models—such as ChatGPT and Gemini—are inescapable; merely predicting the next word is incompatible with reliably providing appropriate outputs. The various fine-tuning methods, while helpful, (...) cannot transform the model from mere next word prediction to the kind of planning and forethought necessary for saying the right thing. The upshot is that these models will increasingly churn out bland, generic responses that will still fail to be accurate or appropriate. (shrink)

Concern about the role and the limits of modeling has heightened after repeated questions were raised regarding the dependability and suitability of the models that were used in the run-up to the 2008 financial crash. In this book, Lawrence Boland provides an overview of the practices of and the problems faced by model builders to explain the nature of models, the modeling process, and the possibility for and nature of their testing. In a reflective manner, the author raises (...) serious questions about the assumptions and judgments that model builders make in constructing models. In making his case, he examines the traditional microeconomics-macroeconomics separation with regard to how theoretical models are built and used and how they interact, paying particular attention to the use of equilibrium concepts in macroeconomic models and game theory and to the challenges involved in building empirical models, testing models, and using models to test theoretical explanations. (shrink)

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex, the less explanatory it will be. Since machine learning increases its performances when more components are added, (...) then it generates models which are not intelligible, and hence not explanatory. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise algorithmic (...) computation and to offer foundations for scientific computing. -/- The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. -/- In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. -/- In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

This commentary elaborates on Gray's conclusion that his neurophysiological model of consciousness might explain how consciousness arises from the brain, but does not address how consciousness evolved, affects behaviour or confers survival value. The commentary argues that such limitations apply to all neurophysiological or other third-person perspective models. To approach such questions the first-person nature of consciousness needs to be taken seriously in combination with third-person models of the brain.

The paper's thesis is that dialogue is not an adequate model for all types of argument. The position of Walton is taken as the contrary view. The paper provides a set of descriptions of dialogues in which arguments feature in the order of the increasing complexity of the argument presentation at each turn of the dialogue, and argues that when arguments of great complexity are traded, the exchanges between arguers are turns of a dialogue only in an extended or metaphorical (...) sense. It argues that many of the properties of engaged back-and-forth exchanges of paradigmatic argument dialogues are not found in ‘solo’ arguments, and that at least some of the norms appropriate to the former type of argument, such as some of the pragma-dialectical rules of van Eemeren and Grootendorst's model, do not apply to the latter. (shrink)

Given an ontological model of a quantum system, a “genuine measurement,” as opposed to a quantum measurement, means an experiment that determines the value of a beable, i.e., of a variable that, according to the model, has an actual value in nature before the experiment. We prove a theorem showing that in every ontological model, it is impossible to measure all beables. Put differently, there is no experiment that would reliably determine the ontic state. This result shows that the positivistic (...) idea that a physical theory should only involve observable quantities is too optimistic. (shrink)

This paper is devoted to some generalizations of explanatory potential of connectionist approaches to theoretical problems of the philosophy of mind. Are considered both strong, and weaknesses of neural network models. Connectionism has close methodological ties with modern neurosciences and neurophilosophy. And this fact strengthens its positions, in terms of empirical naturalistic approaches. However, at the same time this direction inherits weaknesses of computational approach, and in this case all system of anticomputational critical arguments becomes applicable to the connectionst (...) models of mind. The last developments in the field of deep learning gave rich empirical material for cognitive sciences. Multilayered networks, mathematical models of associative dynamics of learning, self-organizing neuronets and all that allow to explain the principles of human conceptual organizing and after this to emulate these processes in computer systems. At all engineering achievements of this technology there is a traditional criticism from representatives of cognitive psychology who cannot accept a thesis about learning ability of a neuronet on the basis of redistribution of scales. Process of learning of natural intelligence, according to cognitive models, happens due to attraction of knowledge broadcast in a symbolical form (mental representations, concepts) at the expense of the systems of output knowledge expressed in the propositional contents. Some philosophical aspects of «neural metaphor» in modern cognitive sciences create the problem field which demands comprehensive understanding, the first step towards which is taken in this work. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise algorithmic (...) computation and to offer foundations for scientific computing. The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

The purpose of this paper is to describe some limitations on scientific behaviorist and computational models of the mind. These limitations stem from the inability of either model to account for the integration of experience and behavior. Behaviorism fails to give an adequate account of felt experience, whereas the computational model cannot account for the integration of our behavior with the world. Both approaches attempt to deal with their limitations by denying that the domain outside their limits is a (...) part of psychology. These attempts to turn the shortcomings of the two models into virtues would be more convincing if their limitations were not diametrically opposed. I will argue that in each case the limitations are too restrictive unless the theories are augmented by physiology. (shrink)

We extend the construction of Mitchell and Steel to produce iterable fine structure models which may contain Woodin limits of Woodin cardinals, and more. The precise level reached is that of a cardinal which is both a Woodin cardinal and a limit of cardinals strong past it.

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would (...) allow one to use models for possibilistic prediction and salvage widespread scientific practice. (shrink)

Thomas Metzinger’s self-model theory offers a frame¬work for naturalizing subjective experiences, e.g. first-person perspective. These phenomena are explained by referring to representational contents which are said to be interrelated at diverse levels of consciousness and correlated with brain activities. The paper begins with a consideration on naturalism and anti-naturalism in order to roughly sketch the background of Metzinger’s claim that his theory renders philosophical speculations on the mind unnecessary . In particular, Husserl’s phenomenological conception of consciousness is refuted as uncritical (...) and inadequate. It will be demonstrated that this critique is misguided. . The main deficiencies of Metzinger’s theory will be elucidated by referring to the conception of phenomenal transparency which will be compared to a phenomenological idea of transparency . Then we shall enlarge our critical horizon by focusing on some implications of representationalism, including reification of consciousness, brain-Cartesianism, and exclusion of the social dimension . Finally, we shall take up our meta-theoretical reflections on the naturalism debate. (shrink)

Any deviation of speed in a traffic flow from a speed limit represents a potential risk of traffic accidents, so speed management appears as an imperative. However, an inadequately set speed limit often causes drivers’ noncompliance to it in the conditions of real traffic flow. By determining the value of exceeding the speed limit according to vehicle classes, it is possible to recommend a credible speeding value that can be considered credible up to a value above the (...) speed limit. In this paper, deterministic multistep mathematical models of speed deviation from the speed limit as a function of longitudinal gradient for the proposed vehicle classes were developed. A total of 11 measuring sections with different traffic flow types were analyzed. Based on a detailed analysis of speeding, models for the deviation of the 15th, 50th, and 85th percentiles were obtained, with the aim of adjusting the credible deviation to control measures. The results obtained in this study were compared with a survey of traffic flow speeding on two-lane roads conducted in Serbia. (shrink)

The biopsychosocial (BPS) model has been of considerable utility to those researching health and illness. This has been particularly the case for critical realists and those with a systemic orientation to their work. Whilst the strengths of the model are conceded in this article, its limitations are also examined. These relate to its ontological sophistication being compromised by its proneness to epistemological naivety. It is a model to explain the emergence of disease and disability, not a reflexive theory applicable to (...) the whole field of health research. This tension is linked to the emergence of a re-orientation of medical knowledge away from bio-medical reductionism. From the outset the model was not about a general critique, or a grand theory, of health and illness. Instead it was a useful intervention, on the part of medical practitioners, like George Engel and Adolf Meyer, to create a reformed medical model on behalf of their profession. Case studies of the problematics of diagnosis and medicalization are offered to illuminate the limitations of the BPS model for health researchers. (shrink)

Hoerl & McCormack's dual systems framework provides a new avenue toward the scientific investigation of temporal cognition. However, some shortcomings of the model should be considered. These issues include their reliance on a somewhat vague consideration of “systems” rather than specific computational processes. Moreover, the model does not consider the subjective nature of temporal experience or the role of consciousness in temporal cognition.

Clarke and Beck use behavioural evidence to argue that approximate ratio computations are sufficient for claiming that the approximate number system represents the rationals, and the ANS does not represent the reals. We argue that pure behaviour is a poor litmus test for this problem, and that we should trust the psychophysical models that place ANS representations within the reals.

Evolutionary transitions in individuality (ETIs) involve the formation of Darwinian collectives from Darwinian particles. The transition from cells to multicellular life is a prime example. During an ETI, collectives become units of selection in their own right. However, the underlying processes are poorly understood. One observation used to identify the completion of an ETI is an increase in collective-level performance accompanied by a decrease in particle-level performance, for example measured by growth rate. This seemingly counterintuitive dynamic has been referred to (...) as fitness decoupling and has been used to interpret both models and experimental data. Extending and unifying results from the literature, we show that fitness of particles and collectives can never decouple because calculations of fitness performed over appropriate and equivalent time intervals are necessarily the same provided the population reaches a stable collective size distribution. By way of solution, we draw attention to the value of mechanistic approaches that emphasise traits, and tradeoffs among traits, as opposed to fitness. This trait-based approach is sufficient to capture dynamics that underpin evolutionary transitions. In addition, drawing upon both experimental and theoretical studies, we show that while early stages of transitions might often involve tradeoffs among particle traits, later—and critical—stages are likely to involve the rupture of such tradeoffs. Thus, when observed in the context of ETIs, tradeoff-breaking events stand as a useful marker of these transitions. (shrink)

This chapter contains sections titled: Introduction Investing in Science: Child Health and U.S. Medicine in the Twentieth Century The Impact of Specific Medical Interventions The Changing Definition of ID The “Flynn Effect” and the Impact of Improved Public Health Conclusion References.

RésuméLa sélection du matériel biologique, sa préparation en vue de ľapplication de diverses techniques?on;investigation de plus en plus sophistiquées ainsi qu'un univers experimental bien déterminé vont contraindre le chercheur à travailler sur un modèle parfois éloigné de son contexte biologique propre. Aussi la signification des résultats obtenus doit être de plus en plus discutée en fonction des limites imposées, non seulement par les techniques, mais également par le modèle même de recherche choisi.SummaryThe selection of the biological material, its preparation for (...) the application of several techniques more and more complex and selected experimental conditions strain the biologist to use biological samples sometimes removed of their environment. The discussion of the results obtained must be done carefully and not only in function of the technical limitations but also in function of the limits due to the experimental material itself.ZusammenfassungDie Auswahl des biologischen Materials, seine Vorbereitung im Hinblick auf die Anwendung auf immer kompliziertere Untersuchungstechniken sowie ein wohlbestimmter Experimentier‐bereich werden den Forscher zwingen, mit einem Modell zu arbeiten, das gelegentlich weit von seinem eigentlichen biologischen Kontext entfernt ist. Deshalb muss die Bedeutung der erhaltenen Ergebnisse immer mehr mit Riicksicht auf die Grenzen diskutiert werden, die nicht allein durch die Techniken, sondern auch durch das Forschungsmodell selbst gesetzt werden. (shrink)

Educators are conflicted about whether school provides an appropriate space to teach ethics. Still, they want to develop the moral character of their students, and most of these efforts have used various citizenship values to address our frustration with students’ ‘lack of character’. Recently, a wave of work in the philosophy of education has rejuvenated discussion of Aristotelian virtue ethics, which forms the backbone for programmes that many schools are now adopting. Mark Jonas and Yoshiaki Nakazawa, however, argue that schools (...) should revisit Plato’s pedagogical methods, as well. If educators want to develop virtue in students, they need to understand the mechanism behind moral development. Guided by Plato, they argue that expert teachers use psychological, pedagogical, and philosophical reasoning to induce epiphanies in students, then guide them through virtue-oriented rehabituation. The goal of this article is to explore the legitimacy of Jonas and Nakazawa’s Platonic theory. I begin by describing the state of modern democratic education and its relationship to teaching ethics. I argue that character development is the appropriate route educators should take but that Jonas and Nakazawa’s theory only gives educators a partial understanding of how to do so. They give a plausible model for how our best educators are effective, but we are left wanting a much more robust, instructive picture. I suggest that we need a broader overhaul of the way we view education in a modern democratic society; character development must proceed from a different normative picture of the individual’s place in society. (shrink)

Analysis of records of women at risk for abuse showed that though information about abuse was present, emergency room physicians rarely utilized it. The doctor-patient interaction tended to obscure rather than elucidate knowledge of abuse. Medicine's epistemologic model of care reconstructs abusive relationships through a medical encounter in which what is most significant is not seen. Nurses are less affected by the model but are under institutional constraints that lead to similar outcomes.

Proponents of neuroeconomics often argue that better knowledge of the human neural architecture enables economists to improve standard models of choice. In their view, these improvements provide compelling reasons to use neural findings in constructing and evaluating economic models. In a recent article, I criticized this view by pointing to the trade-offs between the modeling desiderata valued by neuroeconomists and other economists, respectively. The present article complements my earlier critique by focusing on three modeling desiderata that figure prominently (...) in economic and neuroeconomic modeling. For each desideratum, I examine findings that neuroeconomists deem to be especially relevant for economists and argue that neuroeconomists have failed to substantiate their calls to use these findings in constructing and evaluating economic models. In doing so, I identify methodological and evidential constraints that will continue to hinder neuroeconomists’ attempts to improve such models. Moreover, I draw on the literature on scientific modeling to advance the ongoing philosophical discussion regarding the prospects of interdisciplinary models of choice. (shrink)

In this article I give a critical evaluation of the use and limitations of null-model-based hypothesis testing as a research strategy in the biological sciences. According to this strategy, the null model based on a randomization procedure provides an appropriate null hypothesis stating that the existence of a pattern is the result of random processes or can be expected by chance alone, and proponents of other hypotheses should first try to reject this null hypothesis in order to demonstrate their own (...) hypotheses. Using as an example the controversy over the use of null hypotheses and null models in species co-occurrence studies, I argue that null-model-based hypothesis testing fails to work as a proper analog to traditional statistical null-hypothesis testing as used in well-controlled experimental research, and that the random process hypothesis should not be privileged as a null hypothesis. Instead, the possible use of the null model resides in its role of providing a way to challenge scientists’ commonsense judgments about how a seemingly unusual pattern could have come to be. Despite this possible use, null-model-based hypothesis testing still carries certain limitations, and it should not be regarded as an obligation for biologists who are interested in explaining patterns in nature to first conduct such a test before pursuing their own hypotheses. (shrink)

Complexly organized systems include biological and cognitive systems, as well as many of the everyday systems that form our environment. They are both common and important, but are not well understood. A complex system is, roughly, one that cannot be fully understood via analytic methods alone. An organized system is one that shows spatio-temporal correlations that are not determined by purely local conditions, though organization can be more or less localizable within a system. Organization and complexity can vary independently to (...) some extent, but they are interconnected: organisation requires some complexity, but complexity cannot be maximum in an organized system. I will define complexity and organization more precisely, and show how these definitions imply the above properties. Next I will discuss how organized complexity can be modelled, with an eye to limitations on the tractability of both the models and the modelling process. I will finish with some remarks on the limits of our possible understanding of complexly organized systems. Keywords: complexity, organization, modelling, holism, information theory.. (shrink)