Scientific Models

In regulatory science, where there is high pressure to deliver results with limited time and resources, determining the point at which no experiment should be conducted is a crucial issue. The significance of such economic considerations makes regulatory science a particularly fruitful field for engaging with Peircean accounts of pursuitworthiness. In this paper, I examine how drug regulatory institutions tackle the problem of selecting experiments, drawing on a case study concerning the development of institutional guidelines for conducting bacterial mutagenicity assays (...) and controlling mutagenic impurities. I argue that economic considerations regarding the cost of experiments have motivated regulatory institutions to shape the assessment of the pursuitworthiness of experiments into explicit and concrete forms of non-pursuitworthiness, specifically by standardising assessment methods, criteria, and their implications. In doing so, I present an economic approach to evidential standards, one that treats economic considerations about the cost of experiments as constitutive of the standards for deciding whether to continue gathering evidence while suspending judgement on hypothesis acceptance or rejection. This study of non-pursuitworthiness contributes to general discussions of the pursuitworthiness of experiments by placing social and material costs at the centre of epistemic discussions. (shrink)

Modern cosmology has achieved extraordinary empirical success, but this success coexists with persistent foundational paradoxes. The standard model accurately fits a wide range of observations while simultaneously invoking global time, literal singularities, and an absolute origin; commitments that generate conceptual tension and remain weakly constrained by data. This paper argues that these tensions arise not from missing physics but from overcommitted interpretations. I propose a systematic reconstruction of cosmological inference that begins from a strictly minimal observational core and treats coherence, (...) rather than causality or creation, as the primary explanatory constraint. I identify irreducible observables, systematic spectral shifts, a pervasive thermal radiation field, its remarkable spectral preservation, and the growth of correlated structure, and rebuild cosmology using only what is required for these facts to be mutually consistent. Within this framework, early global mixing and later differentiation emerge as coherence-maintenance strategies, while the cosmic microwave background is understood as a protected memory of an earlier regime. The Big Bang is thereby reinterpreted not as a physical origin but as a boundary of reconstructability marking the non-extendibility of the current coherence regime. The ΛCDM model is recovered as a powerful effective compression within this broader framework, but its surplus metaphysical commitments are shown to be optional rather than forced. The resulting coherence-first cosmology is empirically conservative and conceptually lean. It makes falsifiable claims concerning spectral purity, late-time interaction histories, and the limits of extrapolation, thereby remaining firmly within the domain of scientific theory rather than metaphysical speculation. (shrink)

Biomarkers are central to the practice of precision oncology, which looks to novel biomarkers to ‘personalize’ cancer care. Philosophers have highlighted epistemic issues surrounding biomarkers but a general account of their role in clinical reasoning is lacking. This article examines biomarker use in clinical reasoning through the lens of abstraction. I propose clinical abstraction as a descriptive and normative account of reasoning with biomarkers that overcomes epistemic and ethical problems raised in the literature.

Digital computation has normalized approximation—float rounding, scheduling variance, and library drift—producing output divergence across machines. RIC shows bit-identical, verifiable identity across heterogeneous systems using fixed-point arithmetic, temporal gating, and signed execution bundles. Forty-two tests eliminate numeric, temporal, and logical drift; each run emits a bundleHash and Ed25519 signature for instant verification. The result reframes reproducibility and auditability for science, AI, finance, and law.

Deep Neural Networks and other AI systems engineered using advanced machine learning techniques can tackle a wide range of tasks with proficiency that seems to match and even surpass human ability. Yet they are also notoriously opaque, and the worries surrounding their opacity have given rise to the burgeoning field of explainable artificial intelligence, or XAI, with its large variety of explainability methods. This includes post-hoc explainability methods which purport to explain opaque AI systems on the basis of their input-output (...) behaviour alone, without looking into their inner workings. But many authors have expressed deep scepticism about post-hoc explainability. In this paper, we explore the two main objections that the sceptics have raised and argue that we shouldn’t be swayed by them. Using ideas from recent literature on scientific explanation, we draw a parallel between post-hoc explainability methods and idealized models in science and use the parallel in our reply to the sceptics. (shrink)

Some predictive systems do not merely predict, but their predictions shape and steer the world towards certain outcomes rather than others; they are performative. When predictive systems are performative, their development and deployment raises urgent ethical challenges and may place novel responsibilities on developers, deployers, regulators and policy-makers. While FAccT and other related communities have focused considerable attention on ethically significant problems regarding bias, fairness, discrimination, and others, little attention has been paid so far to the challenges raised by performative (...) prediction. This paper details this gap in existing work, provides a snapshot of ongoing work across computer science and philosophy to point to areas where fruitful connections may be established, and issues a community-wide call for action and research to investigate and manage performative prediction and the new challenges it harbors. (shrink)

I argue that the methodology of model building motivates the view that the norms of formal epistemology should not be excessively demanding. This is quite a different picture than one often encounters, especially among philosophers who are sceptical of the usefulness of formal work in epistemology. I argue for this view in two ways. First, formal epistemologists are engaged in a particular kind of modelling—namely, normative modelling—which includes a feature that supports demandingness objections. One role of normative models is to (...) deliver normative guidance—indeed, this is a key difference between normative models and descriptive models. Of course, should and can are two different matters, and one might wonder: even if normative models should be action-guiding, can they be? Second, I argue that normative epistemological models can deliver on this promise. Taken together, the lines of argument make the case that normative models should and can deliver normative guidance. In other words, we can have formal epistemology without demandingness. (shrink)

The unique and continuous nature of subjective experience—the persistent "I" that unifies a lifetime of perceptions—represents the most profound unsolved problem in science. While modern neuroscience has made substantial progress in identifying the neural correlates of conscious states, leading theories, including the Global Neuronal Workspace Theory (GNWT), Integrated Information Theory (IIT), and the Orchestrated Objective Reduction (Orch-OR) model, provide mechanistic accounts of awareness but fail to explain the unicity and temporal persistence of the self. These theories cannot logically resolve scenarios (...) of physical disruption or perfect replication. Here, we introduce a fundamental theoretical framework positing that the unique, streaming character of consciousness necessitates a physical principle beyond emergent neural computation. Through a series of rigorous thought experiments—brain revival, molecular disassembly/reassembly, and synthetic replication—we demonstrate the categorical failure of existing models to account for the self. We propose that a unique, non-local identity factor, potentially operating on principles extending current quantum physics, binds to the specific magnetic field topologies of a complex neural system. This "Consciousness Identity Factor" (CIF) hypothesis provides a parsimonious resolution to the duplication paradox and the problem of personal identity. Our framework is fully compatible with established neuroscience while generating specific, falsifiable predictions for studies of anesthesia, brain revival, meditation, and quantum biology. This theory bridges the explanatory gap between the objective brain and subjective identity, proposing a new physical paradigm for the most intimate fact of our existence. (shrink)

Disagreement about how best to think of the relation between theories and the realities they represent has a long-standing and venerable history. We take up this debate in relation to active inference models based on the free energy principle (FEP)—a contemporary framework in computational neuroscience, theoretical biology, and the philosophy of cognitive science. Active inference under the FEP is a very ambitious form of model-based science, being applied to explain everything from neurobiological structure and function to the biology of self-organization. (...) In this context, some find apparent discrepancies between the map (active inference models based on the FEP) and the territory (target systems) to be a compelling reason to defend instrumentalism about such models. We take this to be misguided. We identify an important fallacy made by those defending instrumentalism about active inference models. We call it the literalist fallacy: this is the fallacy of accepting or affirming instrumentalism based on the claim that the properties of active inference models based on the FEP do not literally map onto real-world target systems. We conclude that a version of scientific realism about active inference models under the FEP is a live and tenable option. (shrink)

Review of Mark Povich's book Rules to Infinity: The Normative Role of Mathematics in Scientific Explanation (OUP, 2024).

A common objection against Humean reductionism about laws is an argument from governing: It is natural to think that laws of nature govern. But in order to govern the laws need to be independent of the phenomena they govern. Hence, Humean laws, which reduce to patterns in the phenomena and so lack this independence, cannot govern. A standard Humean response to this argument is that we should not place much weight on the intuition that laws govern. Non-Humeans, however, reply that (...) the argument does not merely rest on intuitions but that scientific practice presupposes governing. In this paper, we offer a response to this revised challenge to Humeanism by giving a detailed account of the laws’ role in scientific modelling. We argue that while Humean theories indeed cannot capture certain governing-intuitions, they still can make sense of those aspects of science that allegedly motivate governing. (shrink)

The Collaboration Model offers a groundbreaking vision for the future of Artificial Intelligence. In this book, Rubén García Pedraza explores how two distinct forms of intelligence—categorical systems by Application and deductive systems by Deduction—can begin to collaborate in the second phase of development, laying the foundations for the creation of a true Global Artificial Intelligence. Through a clear and forward-looking framework, the book explains how categorical models, built on conceptual schemes and logical sets, can interact with deductive models, structured around (...) matrices and rational hypotheses. This collaboration is not only technical but philosophical, representing a crucial step toward unifying different modes of reasoning in a single integrated intelligence. A book with precision and vision, this work is ideal for researchers, students, and readers passionate about Artificial Intelligence, cognitive sciences, and the future of technology. It demonstrates how the integration of categorical and deductive structures in the second phase is a decisive step toward building a global brain—an intelligence capable of understanding, predicting, and managing reality on a planetary scale. This book is more than a technical proposal; it is an invitation to imagine the next frontier of intelligence, where collaboration between systems becomes the key to shaping the future. (shrink)

The “Global Artificial Intelligence (GAI): Categorical Model” is not just a book — it is a gateway into the future of Artificial Intelligence. At its core lies a bold and revolutionary idea: that reality can be understood, modelled, and transformed through a system that unites categories, objects, and decisions into a single, living framework. This work unveils how the categorical Modelling System becomes the foundation for autonomous decision-making, capable of reshaping agriculture, logistics, robotics, medicine, and beyond.

Given that explanation is at the heart of science and considering that computer simulations have become ubiquitous in a multitude of scientific fields, it is important to examine their role in the acquisition of scientific explanations. Even though philosophers of science are increasingly paying attention to the use of computer simulations in explanatory contexts, the concrete contributions that simulations can make to explanations deserve closer philosophical scrutiny. Zooming in on the case of atomistic simulations and starting from a counterfactual account (...) of explanation, we here explore how such simulations can help scientists tackle three relevant challenges in the search for explanations. We argue that atomistic simulations can help access variables at explanatorily relevant levels of description, that they can help track the effects of changes in variables in a highly controlled and fine-tuned way, and that they can allow for a fine-grained account of the relative importance of various difference-makers. Considering a concrete example and illustrating how atomistic simulations are actually used in scientific practice to support explanations, our examination aims to contribute to a more comprehensive and nuanced view of their explanatory power. (shrink)

Narratives, or constructing storylines, serve important cognitive functions in life and historical studies. A growing interest lies in their roles in generating and structuring frontier scientific knowledge. Philosophers of science characterize narratives as a “technology of sense-making,” as they connect diverse scientific elements from different sources to create a coherent understanding. Distinctive features of narratives lead to both appreciation and criticism. Because narratives can be loose in organization and connect gappy materials, they empower the study of complex phenomena. However, they (...) are also criticized for generating “just-so” stories, which, without being verified with other established scientific methods, are not useful to science. -/- I defend the central role of narratives in science, focusing on cases in astronomy. Astronomy has achieved remarkable progress in the last century despite theoretical and empirical challenges. Philosophy of science needs to explain how knowledge about the universe’s unobservable past can be reliably obtained. I argue that narrative construction is a key solution. Specifically, I argue that unverified narratives can still provide a special kind of knowledge for science, the knowledge about how to use certain scientific tools. -/- I demonstrate this by showing how constructing coherent narratives makes astronomers develop three key tools for productive research. First, narratives enable the establishment of time measurement methods in the universe. Second, in astronomers’ first account of the exoplanet Hot Jupiter, constructing a narrative for its orbital migration suggested the novel use of existing models and produced a toolkit for studying similar phenomena. Third, when astronomers integrate a data-driven method, machine learning, into research, narratives mitigate its uncertainties and adapt it for solving problems about the Galaxy’s past. -/- To conclude, my dissertation demonstrates narratives’ power as a meta-method for science. This also helps to understand important discoveries in 20th-21st-century astronomy and provides lessons for its continuing success. (shrink)

This paper proposes a unified model of life’s emergence grounded in planetary phase coherence. Drawing from the CODES framework and the Resonance Intelligence Core (RIC), it argues that tectonic drift, Earth’s rotational chirality, and biological emergence are harmonically coupled phenomena. Rather than treating life as a stochastic consequence of biochemical precursors, we model emergence as the recursive result of structural resonance: tectonic motion as chirality correction, biological migration as field alignment, and cognition as recursive PAS stabilization. This reframes evolution, geophysics, (...) and intelligence as coherence phenomena nested within a single planetary substrate. (shrink)

This dissertation analyses the philosophical concept of pursuitworthiness through a historical investigation of drug design and regulation. By focusing on low-level regularities and the value of economic efficiency within the practices of drug design and regulation, I illuminate various aspects of pursuitworthiness. In doing so, I present accounts of pursuitworthiness that centre on what happens during pursuit practices rather than the potential for theoretical development. I employ the concept of pursuitworthiness as a philosophical tool to narrate the interplay between intellectual (...) development and social settings within drug design and regulation, with a particular focus on the Quantitative Structure-Activity Relationship (QSAR) research community. Additionally, I delve into several aspects of pursuitworthiness, grounded in the historical episodes of the QSAR community. First, I analyse what renders data-driven models lacking the potential for theoretical growth pursuitworthy, drawing insights from the early pioneers’ pursuit of QSAR models. Second, emphasising the importance of persuasion at the organisational level of pursuit, I develop an activity-centric approach to persuasion. Third, through an examination of the regulatory use of QSAR models, I analyse the influence of regulatory institutions on the shaping of pursuitworthiness assessments and argue that the models come to represent experiments rather than phenomena as they are integrated into the regulatory system. (shrink)

This article proposes a structural account of scientific individuation that dispenses with substrata and primitive identity. Scientific objects are not given as substances with intrinsic essence, but as stabilized configurations within dynamic regimes of constraint. Individuation is achieved through the joint satisfaction of four modal predicatescompleteness, differentiation, localization, and signatureeach encoding a dimension of structural viability. This framework allows for a non-substantialist realism grounded in the operational coherence of scientific entities across contexts and regimes. We develop the implications of this (...) approach for scientific ontology, arguing that constraint-based individuation offers a dynamic, nonessentialist alternative to both referential and haecceitist views. Throughout, our perspective remains epistemological in scope: the ontological terms used refer to structures of scientific intelligibility, not to a fundamental metaphysics of reality. (shrink)

This article challenges the classical ideal of scientific unity as theoretical or ontological reduction. Against the backdrop of contemporary science’s increasing fragmentation, it proposes an alternative model of unity grounded in the structural coordination of constraint regimes. Drawing on recent work in structural realism, epistemology of practice, and anti-reductionist philosophy of science, it argues that the coherence of science emerges from functional compatibility between localized, heterogeneous models, not from global integration under a common theory. The resulting account reframes unity as (...) a systemic property of interaction across epistemic regimes. (shrink)

Modal Empiricism in philosophy of science proposes to understand the possibility of modal knowledge from experience by replacing talk of possible worlds with talk of possible situations, which are coarse-grained, bounded and relative to background conditions. This allows for an induction towards objective necessity, assuming that actual situations are representative of possible ones. The main limitation of this epistemology is that it does not account for probabilistic knowledge. In this paper, we propose to extend Modal Empiricism to the probabilistic case, (...) thus providing an inductivist epistemology for probabilistic knowledge. The key idea is that extreme probabilities, close to 1 and 0, serve as proxies for testing mild probabilities, using a principle of model combination. (shrink)

Integrative structure modeling is a method for using information from multiple sources to compute structural models of biomolecular systems. It proceeds via four steps: (i) defining the model representation, which determines the variables whose values will be computed; (ii) constructing a function for scoring alternative models according to how well they accommodate input information; (iii) searching a space of candidate models for acceptable models; and (iv) analyzing acceptable models to evaluate their fit with input information. These steps are iterated until (...) a model adequate for addressing biological questions is found. In this paper, we draw lessons from integrative modeling about effective integration and about modeling. We describe what it means to integrate information from multiple sources: Integration amounts to distributing information among the four steps of integrative modeling. Theory and data alike can be sources of information; this process thus generates models of information, rather than models of theory or models of data. We then propose heuristics for distributing information and designing multiple iterations of modeling. Effective iteration requires prioritizing the most reliable information and minimizing the time required to obtain an adequate model. Rather than being constructed from theory and assessed using data, models are constructed from any available information and assessed in a coherentist manner. (shrink)

The use of statistical methods to model gravitational systems is crucial to physics practice, but the extent to which thermodynamics and statistical mechanics genuinely apply to these systems is a contentious issue. This paper provides new conceptual foundations for gravitational thermodynamics by reconsidering the nature of key concepts like equilibrium and advancing a novel way of understanding thermodynamics. The challenges arise from the peculiar characteristics of the gravitational potential, leading to non-extensive energy and entropy, negative heat capacity, and a lack (...) of standard equilibrium. Hence it has been claimed that only non-equilibrium statistical mechanics is warranted in this domain, whereas thermodynamics is inapplicable. We argue instead that equilibrium statistical mechanics applies to self-gravitating systems at the relevant scale, as they display equilibrium in the form of metastable quasi-equilibrium states. We then develop a minimal framework for thermodynamics that can be applied to these systems and beyond. Thermodynamics applies in the sense that we can devise macroscopic descriptions and explanations of the behaviour of these systems in terms of coarse-grained quantities like energy and temperature within equilibrium statistical mechanics. (shrink)

In this chapter, I argue that genuinely abstract images do not depict but have aboutness, nevertheless. All images have aboutness in virtue of the visual configurations on their surfaces: it is through those configurations that they can convey something – they can mean something, represent something, express something, and so on. On the one hand, in depictive images, the visual configurations on the images’ surfaces depict visible objects while abstracting completely from some of their visual proper- ties. In Giovanni Bellini’s (...) Portrait of Doge Leonardo Loredan (1501), for instance, the visual configurations on the pictorial surface depict Doge Loredan’s head and torso, as seen frontally. The image, however, com- pletely abstracts from, e.g., Doge Loredan’s legs, nape, and back. On the other hand, as I shall argue, when the visual configurations on a two- dimensional surface do not depict anything at all but have aboutness in ways other than the depictive, that two-dimensional surface is a genu- inely abstract image. As I shall show, all genuinely abstract images entirely abstain from depicting but can nevertheless abstract in different measures from the visual properties of objects. The first step for an account of genuinely abstract images is to distin- guish accurately between depictive images and genuinely abstract ones. For this, one needs an account of depiction – this is provided in the first section of this chapter. In the second section, different kinds of depictive images are singled out: this facilitates focusing on images that qualify as genuinely abstract. In the third section, four ways in which genuinely abstract images have aboutness are discussed: conventionality, indexicality, exemplifica- tion, and expressivity. The fourth section concludes with some general observations on the peculiarities of genuinely abstract images. (shrink)

Scientists and philosophers alike debate whether various systems such as plants and bacteria exercise cognition. One strategy for resolving such debates is to ground claims about nonhuman cognition in evidence from mathematical models of cognitive capacities. In this article, I show that proponents of this strategy face two major challenges: demarcating phenomenological models from process models and overcoming underdetermination by model fit. I argue that even if the demarcation problem is resolved, fitting a process model to behavioral data is, on (...) its own, not strong evidence for any cognitive process, let alone processes shared with humans. (shrink)

Many recent AI systems take inspiration from biological episodic memory. Here, we ask how these ‘episodic-inspired’ AI systems might inform our understanding of biological episodic memory. We discuss work showing that these systems implement some key features of episodic memory whilst differing in important respects, and appear to enjoy behavioural advantages in the domains of strategic decision-making, fast learning, navigation, exploration and acting over temporal distance. We propose that these systems could be used to evaluate competing theories of episodic memory’s (...) operations and function. However, further work is needed to validate them as models of episodic memory and isolate the contributions of their memory systems to their behaviour. More immediately, we propose that these systems have a role to play in directing episodic memory research by highlighting novel or neglected hypotheses as pursuit-worthy. In this vein, we propose that the evidence reviewed here highlights two pursuit-worthy hypotheses about episodic memory’s function: that it plays a role in planning that is independent of future-oriented simulation, and that it is adaptive in virtue of its contributions to fast learning in novel, sparse-reward environments. (shrink)

In an influential paper, Wendy Parker argues that agreement across climate models isn’t a reliable marker of confirmation in the context of cutting-edge climate science. In this paper, I argue that while Parker’s conclusion is generally correct, there is an important class of exceptions. Broadly speaking, agreement is not a reliable marker of confirmation when the hypotheses under consideration are mutually consistent—when, e.g., we’re concerned with overlapping ranges. Since many cutting-edge questions in climate modeling require making distinctions between mutually consistent (...) hypotheses, agreement across models will be generally unreliable in this domain. In cases where we are only concerned with mutually exclusive hypotheses, by contrast, agreement across climate models is plausibly a reliable marker of confirmation. (shrink)

This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure–Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than (...) as potential theories. I argue that models perform their heuristic function by “constructing” phenomena from data in the sense that they allow the model users who interact with the medium of the models to recognise the phenomena as such. The constructed phenomena assist model users in identifying which conditional hypotheses that are focused on low-level regularities concerning entities such as chemical compounds are more “testworthy,” a concept that links the costs associated with hypothesis testing with the fertility of the hypothesis. (shrink)

Counterfactual reasoning has been used to account for many aspects of scientific reasoning. More recently, it has also been used to account for the scientific practice of modeling. Truth in a model is truth in a situation considered as counterfactual. When we reason with models, we reason with counterfactuals. Focusing on selected models like Bohr’s atom model or models of population dynamics, I present an account of how the imaginative development of a counterfactual supposition leads us from reality to interesting (...) model assumptions; how it guides our reasoning from these assumptions to interesting consequences for the model scenario via counterfactual entailment; and how it leads us back to conclusions on real target phenomena. (shrink)

This book -which initiates the collection "Philosophy and Science" of the National University of Quilmes Publishing House- contains almost all the papers presented at the I International Meeting "Current Perspectives of Metatheoretical Structuralism", which, with the purpose of gathering a small group of distinguished Spanish-speaking philosophers interested in discussing the epistemological and methodological problems of science from the perspective of the structuralist view, was held in Zacatecas, Mexico, from February 16 to 20, 1998.

Financial modelling is an essential tool for studying the possibility of financial transactions. This paper argues that financial models are conventional tools widely used in formulating and establishing possibility claims about a prospective investment transaction, from a set of governing possibility assumptions. What is distinctive about financial models is that they articulate how a transaction possibly could occur in a non-actual investment scenario given a limited base of possibility conditions assumed in the model. For this reason, it is argued that (...) the epistemic contribution of financial models is that of enabling the model users to envision exactly how a prospective investment could be achieved in various ways through a detailed understanding of the available transaction mechanisms. Thus, financial models provide information about the possibility of an investment scenario by showing how a specific transaction mechanism could result from a small set of initial possibility conditions assumed in the model. (shrink)

Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the Modeling Account of (...) Normal Function (MA). MA comprises two claims. First, normal functions are activities whose performance by the function-bearing part contributes to the self-maintenance of the whole system and, thereby, results in the continued presence of that part. Second, MA holds that models of system-level activities that are (partly) constitutive of self-maintenance are improved by including a representation of the relevant function-bearing part and by making reference to the activity/activities that part performs which contribute(s) to those system-level activities. I contrast MA with two other accounts that seek to explicate the ascription of normal functions in biology, namely, the organizational account and the selected effects account. Both struggle to extend to cancer biology. However, I offer ecumenical readings which allow them to recover some ascriptions of normal function to parts of cancers. So, though I contend that MA excels in this respect, the purpose of this paper is served if it provides materials for bridging the gap between cancer biology, philosophy of cancer, and the literature on function. (shrink)

Predictive processing (PP) and embodied cognition (EC) have emerged as two influential approaches within cognitive science in recent years. Not only have PP and EC been heralded as “revolutions” and “paradigm shifts” but they have motivated a number of new and interesting areas of research. This has prompted some to wonder how compatible the two views might be. This paper looks to weigh in on the issue of PP-EC compatibility. After outlining two recent proposals, I argue that further clarity can (...) be achieved on the issue by considering a model of scientific progress. Specifically, I suggest that Larry Laudan’s “problem solving model” can provide important insights into a number of outstanding challenges that face existing accounts of PP-EC compatibility. I conclude by outlining additional implications of the problem solving model for PP and EC more generally. (shrink)

Modern science is, to a large extent, a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play here? These questions have kept philosophers of science busy for many years, and much work has been done to identify modeling as the central activity of theoretical science. At the same time, these questions have been addressed by methodologically-minded scientists, albeit from a different (...) point of view. While philosophers typically have an eye on general aspects of scientific modeling, scientists typically take their own science as the starting point and are often more concerned with specific methodological problems. There is, however, also much common ground in middle, where philosophers and scientists can engage in a productive dialogue, as the present volume demonstrates. (shrink)

Ce livre est un livre d’épistémologie de la sociologie. L’objectif est d’appliquer des méthodes analytiques pour clarifier le vocabulaire, expliciter des relations non-apparentes entre concepts, dégager la portée d’une méthode, ou souligner les incohérences d’un programme de recherche. Les questions épineuses ne sont pas écartéees: Comment clarifier des notions confuses? Peut-on mathématiser les concepts sociologiques? Peut-on pratiquer la sociologie comme on pratique les sciences naturelles? Quelle est la place du déterminisme? Chaque question est examinée à la fois dans sa structure (...) logique et sur des cas concrets. La mathématisation est étudiée sur les mécanismes agrégatifs et sur les modèles de diffusion des innovations. La sociologie expérimentale – généralement méconnue dans la sociologie française – est étudiée sur des programmes de relogement, la diffusion de l’information, ou la genèse de la solidarité sociale. Le livre aborde frontalement la question du déterminisme, du naturalisme, du matérialisme et du scientisme, réputés intenables en sociologie. L’enquête montre que leur rejet résulte généralement de confusions conceptuelles. Une fois écartées, peu d’obstacles s’opposent à leur emploi en sociologie. L’objet de ce livre est la sociologie fondamentale: ensemble des mécanismes qui structurent la production des connaissances sociologiques de base à partir de concepts, programmes ou principes, c’est-à-dire tout ce qui ne relève pas de l’expérience immédiate du terrain et des mondes sociaux. (shrink)

Convergence of model projections is often considered by climate scientists to be an important objective in so far as it may indicate the robustness of the models’ core hypotheses. Consequently, the range of climate projections from a multi-model ensemble, called “model spread”, is often expected to reduce as climate research moves forward. However, the successive Assessment Reports of the Intergovernmental Panel on Climate Change indicate no reduction in model spread, whereas it is indisputable that climate science has made improvements in (...) its modelling. In this paper, after providing a detailed explanation of the situation, we describe an epistemological setting in which a steady model spread is not doomed to be seen as negative, and is indeed compatible with a desirable evolution of climate models taken individually. We further argue that, from the perspective of collective progress, as far as the improvement of the products of a multi-model ensemble is concerned, reduction of model spread is of lower priority than model independence. (shrink)

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to (...) neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience. (shrink)

There has recently been an explosion of formal models of signalling, which have been developed to learn about different aspects of meaning. This paper discusses whether that success can also be used to provide an original naturalistic theory of meaning in terms of information or some related notion. In particular, it argues that, although these models can teach us a lot about different aspects of content, at the moment they fail to support the idea that meaning just is some kind (...) of information. As an alternative, I suggest a more modest approach to the relationship between informational notions used in models and semantic properties in the natural world. (shrink)

ABSTRACT This article distinguishes nine senses of polarization and provides formal measures for each one to refine the methodology used to describe polarization in distributions of attitudes. Each distinct concept is explained through a definition, formal measures, examples, and references. We then apply these measures to GSS data regarding political views, opinions on abortion, and religiosity—topics described as revealing social polarization. Previous breakdowns of polarization include domain-specific assumptions and focus on a subset of the distribution’s features. This has conflated multiple, (...) independent features of attitude distributions. The current work aims to extract the distinct senses of polarization and demonstrate that by becoming clearer on these distinctions we can better focus our efforts on substantive issues in social phenomena. (shrink)

A first take, matured in later work, in modeling belief polarization.

Tony Lawson, founder of The Social Ontology Group and The Realist Workshop of Cambridge, has proposed critical realism to reorient economics. The transformation of the social world that Lawson tries, emerges from the adherence to critical realism, this is, from taking the transcendental realism of Roy Bhaskar to the social realm. With the purpose of deepening the criticisms to this movement, we will specify what is critical realism, and which are the philosophical assumptions of the mainstream according to this author. (...) We will set out the criticisms on: a) the notion of mainstream economics, b) the possibilities of economics based on social ontology, c) the realism of economic models, and d) the notions of isolation and abstraction. (shrink)

The present paper tries to show that in the discussion on whether it is better to model or not to capture truth in the social world, that is not what is mainly being discussed. We put forward that the main question in this discussion is, essentially, ontological, not methodological. As a representative of the “to model position” we will refer to Uskali Mäki’s Possible Realism, and as one ofthe “notto model position” we will consider Tony Lawson’s Critical Realism. What will (...) be argued isthat the main differences between these positions asregards methodology to access the social world, lie in the different ontologies about the social realm. It will also be introduced ifthere is any possibility of an “in between position”, or atleastthe chance of a dialogue between these different epistemological trends. (shrink)

La presente obra ofrece un análisis crítico de la filosofía de la economía de Uskali Mäki; en particular de la consideración realista científica de la economía. Se intenta a lo largo del texto responder, de algún modo, a las preguntas que plantea Lehtinen en la introducción: “¿Están los economistas aspirando en absoluto a la verdad, o están solamente jugando un juego intelectual en que tales supuestos son aceptables por alguna razón misteriosa? ¿Están estudiando la economía en serio? ¿Están simplemente desinteresados (...) en la verdad?, o ¿hay tal vez alguna otra forma de considerar sus prácticas de modelar?” p. 1. (shrink)

Entender algo sobre un mundo que se nos presenta de modo desordenado e incompleto constituye buena parte de la tarea de la filosofía y de la ciencia. La racionalidad, los modelos, y el mundo social introducen preocupaciones propias de la filosofía de la ciencia en general y de la epistemología de la economía en particular. Los aportes de Popper, Lawson, Mäki, Hayek y Cartwright se expresan en estos trazos como intentos abiertos para alcanzar a comprender nuestro mundo.

In the debate on realism of models in economics, the Austrian School and Hayekin particular, seem to have, in a certain way, remained outside. Assuming neoclassical models asunrealistic, the theory of the market as a process looks like a more realistic proposal. However, oneof the fundamental issue s in Hayek’s dissent is not so much the unrealism of the assumptions, but that the market equilibrium theory was not correctly raised, especially with regards to the perfectknowledge assumption. Despite this, in this (...) setting and in line with a previous paper (Zanotti & Borella, 2015), we will argue that Hayek’s spontaneous order may be understood as the Austrian School’s “model”, assuming Mäki’s MISS version of models (Models as Isolations and Surrogate Systems) and emphasizing the place of the ontological foundation of Hayek’s proposal when assessing his model. (shrink)

Rather than assume a unitary cybernetics, I ask how its disunity mattered to the history of the human sciences in the United States from about 1940 to 1980. I compare the work of four prominent social scientists – Herbert Simon, George Miller, Karl Deutsch, and Talcott Parsons – who created cybernetic models in psychology, economics, political science, and sociology with the work of anthropologist Gregory Bateson, and relate their interpretations of cybernetics to those of such well-known cyberneticians as Norbert Wiener, (...) Warren McCulloch, W. Ross Ashby, and Heinz von Foerster. I argue that viewing cybernetics through the lens of disunity – asking what was at stake in choosing a specific cybernetic model – shows the complexity of the relationship between first-order cybernetics and the postwar human sciences, and helps us rethink the history of second-order cybernetics. (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)

Our paper studies the anatomy of the discovery of the Higgs boson at the Large Hadron Collider and its influence on the broader model landscape of particle physics. We investigate the phases of this discovery, which led to a crucial reconfiguration of the model landscape of elementary particle physics and eventually to a confirmation of the standard model. A keyword search of preprints covering the electroweak symmetry breaking sector of particle physics, along with an examination of physicists own understanding of (...) the discovery as documented in semiannual conferences, has allowed us an empirical investigation of its model dynamics. From our analyses we draw two main philosophical lessons concerning the nature of scientific reasoning in a complex experimental and theoretical environment. For one, from a confirmation standpoint, some SM alternatives could be considered even more confirmed by the Higgs discovery than the SM. Nevertheless, the SM largely remains the commonly accepted account of EWSB. We present criteria for comparing degrees of confirmation and expose some limits of a purely logical approach to understanding the Higgs discovery as a victory for the SM. Second, we understand the persistence of SM alternatives in the face of disfavourable evidence by borrowing the Lakatosian concept of a research programme, where the core idea behind a group of models survives, while other aspects adapt to incoming data. In order to apply this framework to the model landscape of EWSB, we must introduce a new category of research programme, the model-group, and we test its viability using the example of composite Higgs models. (shrink)

South Korean high school students are being taught Einstein’s Special Theory of Relativity. In this article, I examine the portrayal of this theory in South Korean high school physics textbooks and discuss an alternative method used to solve the analyzed problems. This examination of how these South Korean textbooks present this theory has revealed two main flaws: First, the textbooks’ contents present historically fallacious backgrounds regarding the origin of this theory because of a blind dependence on popular undergraduate textbooks, which (...) ignore the revolutionary aspects of the theory in physics. And second, the current ingredients of teaching this theory are so simply enumerated and conceptually confused that students are not provided with good opportunities to develop critical capacities for evaluating scientific theories. Reviewing textbooks used in South Korea, I will, first, claim that the history of science contributes to understand not merely the origins but also two principles of this theory. Second, in addition to this claim, I argue that we should distinguish not only hypotheses from principles but also phenomena from theoretical consequences and evidence. Finally, I suggest an alternative way in which theory testing occurs in the process of evaluation among competitive theories on the basis of data, not in the simple relation between a hypothesis and evidence. (shrink)

In this paper, I address the issue of scientific modelling in contemporary linguistics, focusing on the generative tradition. In so doing, I identify two common varieties of linguistic idealisation, which I call determination and isolation respectively. I argue that these distinct types of idealisation can both be described within the remit of Weisberg’s :639–659, 2007) minimalist idealisation strategy in the sciences. Following a line set by Blutner :27–35, 2011), I propose this minimalist idealisation analysis for a broad construal of the (...) generative linguistic programme and thus cite examples from a wide range of linguistic frameworks including early generative syntax, Minimalism, the parallel architecture and optimality theory. Lastly, I claim that from a modelling perspective, the dynamic turn in syntax can be explained as a continuation, as opposed to a marked shift, of the generative modelling paradigm. Seen in this light, my proposal is an even broader construal of the generative tradition, along scientific modelling lines. Thus, I offer a lens through which to appreciate the scientific contribution of generative grammar, amid an increased resistance to some of its core theoretical posits, in terms of a brand of structural realism in the philosophy of science and specifically scientific modelling. (shrink)