Scientific Models, Misc

Planck's constant h and Boltzmann's constant kB conventionally connect spectral frequency to energy and energy to temperature. Recent analysis of blackbody spectral relations in the frequency domain revealed an unexpected result. Unlike conventional physical constants, which typically appear in the slopes of individual relations as local scaling factors, the ratio h/kB does not appear in any individual relation. Instead, different relations between spectral observables and temperature fail to close, and h/kB emerges as the intercept residual of that non-closure. This suggests (...) that h/kB acts as a global consistency factor required to reconcile the different mappings. This observation raises a deeper question. If h/kB appears neither in the spectral observables nor in temperature itself, but only in the mapping between them, is it describing the empirical structure being observed, or the mapping used to represent it? More generally, are physical constants merely scaling factors between equivalent domains of description, or do they connect fundamentally different domains? We explore this question by distinguishing between an empirical domain of directly measurable spectral quantities such as frequency, phase, and linewidth, and a representational domain of abstract constructs used to represent those observations, including temperature, energy, entropy, and spatial parametrizations. Within this framework, physical constants are interpreted as quantities introduced to achieve internal consistency among different representations of the same empirical structure. We develop this perspective through the concept of Domain Projection and discuss its implications for the interpretation of physical constants, physical laws, and the foundations of scientific description. (shrink)

Modern science relies on representational models that organize empirical observations through constructs such as electrons, energy levels, spacetime, wavefunction and causal mechanisms. While these models achieved extraordinary predictive success, they also led to an implicit inversion in which these unobservable theoretical constructs are treated as explanatory ground and observable spectral regularities as derivative. Recent large-scale analyses of atomic spectra reveal that diverse physical measurements exhibit unexpectedly low-dimensional and relational organization recoverable directly through frequency-domain relations, without introducing hidden entities or theoretical (...) assumptions. These results show that classification, compression, generalization, and prediction emerge naturally from intrinsic spectral structure prior to representational interpretation. The paper argues that representation reorganizes empirical structure rather than generates it and becomes methodologically secondary once observable relations exhibit predictive closure. The persistent demand for additional mechanistic explanation arises not from deficiencies in empirical structure, but from the expectation that physical understanding must ultimately be translated into narratives of localized objects, linear causality, and particle interactions. Once representational constructs acquire explanatory authority beyond their empirical origin, they tend to stabilize and reify, expanding the theoretical apparatus to accommodate inconsistencies. This motivates the concept of representational debt, whereby additional abstract layers accumulate beyond empirical necessity, increasing descriptive complexity and delaying the discovery of simpler organizing principles. More broadly, the analysis motivates Foundational Science, a discipline concerned not with ontological entities or local causal mechanisms, but with invariant relations, scaling laws, and structural constraints. The Spectral Framework is presented as an empirical realization of this methodological perspective, illustrating how scientific explanation may be re-grounded in simple organizational principles already present in measurements. (shrink)

Dennett’s real-pattern realism links ontology to compression and prediction, but compression alone is too permissive: contrived codings and dynamically idle aggregates can satisfy it. This paper argues that the missing ingredient is an explicit closure condition. A candidate macro-object qualifies when, for a fixed regime, horizon, and admissible intervention class, macrostate information is sufficient for macro-transitions, so within-class micro-differences do not change macro-level what-follows. In exact Markov settings, strong lumpability provides a benchmark realization of this condition. In non-ideal settings, closure (...) is graded through leakiness and convergent diagnostics under fixed constraints. The result is realist but disciplined: it excludes high-leak composites, supports graded verdicts in borderline cases, and remains compatible with microphysical completeness. By tying commitment strength to robustness under anti-gerrymandering tests, the framework explains why selective realism is principled rather than discretionary. The paper contributes a philosophical criterion and an audit protocol for applying it, while leaving domain-specific estimator engineering to downstream methodological work. (shrink)

The problem of demarcating between what is scientific and what is pseudoscientific or merely unscientific - in other words, the problem of defining scientificity - remains open. The modern debate was firstly structured around Karl Popper's falsificationist epistemology from the 1930's, before diversifying a few decades later. His central idea is that what makes something scientific is not so much how adequate it is with data, but rather to what extent it might not have been so. Since the second half (...) of the century, and in the wake of criticisms, such as the Duhem-Quine thesis, that were raised against falsificationism(s), most approaches to the problem of scientific demarcation are now multicriteria and holistic. However, the approach presented in this paper does not follow the same guideline. The present work can be seen as an attempt to adapt Popper's (sophisticated) falsificationism to a model-based view of scientific knowledge. Using formalization and focusing on a particular epistemic unit of analysis (namely, empirical models) allows to properly define the popperian corroboration degree and to view scientificity as the maximization of this degree of corroboration over all available models and data. We eventually recover, in a natural way, well-accepted scientificity criteria: empirical adequacy, Lakatos' progressive problemshifts, balance between strength and simplicity, parsimony, and coherence as special cases of this general scientificity principle. From this viewpoint, the language dependency of our empirical knowledge no longer appears as a limitation of falsificationism but as one more reason to take it as a good epistemological framework. (shrink)

This paper was written for and presented at a symposium on Multiple Realizability at the Central Division of the APA in 2022. It's in somewhat rough shape, especially the later parts. I hope to be in a position soon to post a revised and more carefully worked out version. The basic argument of the first half is this: Realization of the interesting sort (and thus MR of the interesting sort) requires tidy separation of levels (with realizers being at a lower (...) level than that which they realize). The success of mixed-scale, mixed-resource (i.e., multi-grain) modeling strongly suggests that such a tidy separation of levels is not in the cards. Thus, MR is (likely) not a widespread phenomenon in our universe. The second half of the paper develops a relation of "multiple explainability" that is meant to play the role, in many contexts, of MR, while being metaphysically and methodologically messier than MR. (shrink)

Bohr’s atomic model is one of the better known examples of empirically successful, albeit inconsistent, theoretical schemes in the history of physics. For this reason, many philosophers use this model to illustrate their position for the occurrence and the function of inconsistency in science. In this paper, I proceed to a critical comparison of the structure and the aims of Bohr’s research program – the starting point of which was the formulation of his model – with some of its contemporary (...) philosophical readings. My study comes to conclude that the attempt of certain philosophers to accommodate Bohr’s model to a form of paraconsistent logic obliterates essential aspects of scientists' actual practice and reasoning. (shrink)

Apparently modally laden terms and relations figure in scientific models, especially though not only possibility. There is an old empiricist tension between measurements as returning actual values, and stronger forms of modality. How could we measure what didn't happen, or use measurement to distinguish what didn't happen but could have, from that which did not happen and could not have? I offer several pragmatist points in the context of modeling and possibility specifically, by which to see this tension as a (...) fruitful one. Peirce offers a view of positive possibility, where it prior to and required for actuality, and which can be differentiated from merely in-principle possibility. He also offers a view of doubt where it requires one suspend use of a belief and initiate an inquiry; views of possibility like those in Humean accounts, even pragmatic Humean views, are offering what amounts to a doubt about models that do not represent these, and only represent positive possibilities. This leads to a distinction by Price between e-representation and irepresentation, where some representation is constrained externally by the environment, and some constrained internally by inferential connections. Putting these together provides a better way to understand how models can represent modality such as positive possibility, and how models can be used in ways that settle direction of fit. (shrink)

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, (...) data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised (...) as modelling idealisations motivated normatively. I then survey the landscape of methodological options: what might formal epistemologists be up to? I argue the choice is essentially binary: modelling or theorising. If NFE is theorising it is doing very poorly: generating false claims with no clear methodology for separating out what is to be taken seriously. Modelling, by contrast, is a successful methodology precisely suited to the management of useful falsehoods. Regarding NFE as modelling is not costless, however. First, our normative inferences are less direct and are muddied by the presence of descriptive idealisations. Second, our models are purpose-specific and limited in their scope. I close with suggestions for how to adapt our practice. (shrink)

In recent years, discussions on the epistemology of model organism-based research have emerged in the philosophy of science. A key topic of discussion is how the epistemic insights gained from model organisms differ from those gained through other experimental organisms used in laboratory and field research. Here, we argue that model organisms are epistemically special due to their nature as ontogenetically changeable, standardized, and evolved material model carriers. These characteristics afford six important kinds of modeling versatility that biologists marshal in (...) their investigations: (i) synchronic target versatility; (ii) synchronic scope versatility; (iii) diachronic target versatility; (iv) diachronic scope versatility; (v) manipulation versatility; and (vi) discovery versatility. In presenting these dimensions of modeling versatility, we also clarify key notions such as ‘representational target,’ ‘representational scope,’ and ‘representational power’ as these apply to modeling practices that involve model organisms. (shrink)

This paper introduces Epistemics as a framework for analyzing orientation structures, model validity, and revision under finite conditions. Epistemics is neither metaphysics nor normative theory, and it does not replace empirical science, epistemology, social epistemology, or philosophy of science. Its aim is to clarify how finite cognitive systems stabilize experience, expectation, and action, form modelable orders, and guide, limit, or revise models. -/- The paper starts from the finitude of cognition: cognitive systems never have unlimited time, attention, processing capacity, social (...) coordination, or institutional resources. Under such conditions, cognition must select, stabilize, and form orientation. Not every orientation structure is already a model; modelability arises only when an order becomes markable, available for renewed take-up, delimitable, testable, and correctable. -/- A model is defined as a condensed orientation structure whose use can be guided and whose validity can be determined. The central concern is not absolute truth, but the range within which a model remains viable under specific conditions. This validity is domain-bound: subjective orientation, intersubjective coordination, and functional-empirical robustness follow different logics of stabilization, testing, and revision. -/- The paper develops costs, friction, overextension, apparent stability, malfunction, and revision as diagnostic concepts. Friction is not treated as mere error, resistance, or falsification, but as the epistemically readable non-fit of an activated expectation, take-up, or model structure under the conditions of its operation. Revision is not the abandonment of stabilization, but the controlled processing of strain, non-fit, or overextension under finite conditions. -/- Epistemics thereby offers a conceptual framework for analyzing scientific models, intersubjective validity, domain transitions, epistemic costs, and the limits of model transfer. It aims to make visible when models remain valid, when they are overextended, when friction signals a limit of validity, and when revision becomes necessary. (shrink)

This paper introduces a model-based account of meaning, arguing that meaning properties reside in models rather than in the external world. Building on this view, it explores how such an instrumentalist framework can engage critically with various concerns raised by Wittgenstein, Quine, and Kripke[nstein]—each of whom voiced scepticism toward certain conceptions of semantic theorising and, in some cases, the reification of meaning. While the scope and nature of their respective criticisms may differ, the paper suggests they share a broadly deflationary (...) attitude toward semantic metaphysics. Twentieth-century challenges to mainstream truth-conditional semantics, from verificationism, inferentialism, and other alternatives, have further complicated the semantic landscape, prompting a reconsideration of metaphysical assumptions in theories of meaning. In light of both scepticism about meaning and explanatory disagreement in semantics, the paper questions the metaphysical interpretation of theories of meaning and proposes a reoriented understanding of semantic theorising—one that is formally tractable yet philosophically restrained. (shrink)

We present an argument about the methodology of ethics, broadly conceived, drawing on recent research on modelling in the philosophy of science. More specifically, we argue that normative ethics should adopt the methodology of modelling. We make our case in two parts. First, despite the perhaps unfamiliar terminology, modelling already happens in ethics. We identify it, and argue that its practice could be improved by recognising that it is modelling and by adopting some methodological lessons from philosophy of science. Second, (...) modelling should be adopted more widely within normative ethics, because it fits well with various methodological ends we shall identify. Models can be used to investigate ethical questions in a manner that is systematic but relatively free of foundational theoretical commitments in first-order ethics. Models are more local, and less ambitious, than theories. They can be used to break deadlocks, by focusing attention on the particularities of a sub-domain and by providing a common tool–the surrogate model system–which each side can use to make their principles precise, illustrate the implications of their view, and identify sources of disagreement or points of agreement. We are pluralists about method, so this is not a call to abandon other philosophical methods. It is simply a plea for modelling, motivated by the method’s independent benefits and its fruitfulness in resolving some persistent methodological problems in ethics. (shrink)

Scientists often disagree on the best theory to describe a scientific event. While such debates are a natural part of healthy scientific discourse, the timeframe for scientists to converge on an ideal method may not always align with real-life knowledge dynamics. In this article, I use an event from the history of chemistry as inspiration to develop Agent-Based Models of epistemic networks, exploring method selection within a scientific community. These models reveal several situations where incorrect, simpler methods can persist, even (...) when substantial evidence supports a more complex method. This becomes particularly evident when different evidence-sharing timeframes are analyzed. The network structure connecting the scientists plays a crucial role in determining how and when convergence on the correct method is achieved, guided by real-world evidence. This framework provides a foundation for further exploration of scientists’ behavior in past and future discoveries, as well as how agents internalize scientific information. (shrink)

This paper critically assesses Rizzo and Whitman’s theory of inclusive rationality in light of the ongoing cross-disciplinary debate about rationality, welfare analyses and policy evaluation. The paper aims to provide three main contributions to this debate. First, it explicates the relation between the consistency conditions presupposed by standard axiomatic conceptions of rationality and the standards of rationality presupposed by Rizzo and Whitman’s theory of inclusive rationality. Second, it provides a qualified defence of the consistency conditions presupposed by standard axiomatic conceptions (...) of rationality against the main criticisms put forward by Rizzo and Whitman. And third, it identifies and discusses specific strengths and weaknesses of Rizzo and Whitman’s theory of inclusive rationality in the context of welfare analyses and policy evaluation. (shrink)

Scientists and philosophers of science often draw methodological lessons from successful theories to justify methods of theory construction and to guide research programs. This paper proposes an epistemic framework for this practice, articulated in terms of the notion of meta-induction. By analogy to Goodman's `New Riddle of Induction', it introduces the concept of projectability of meta-inductive arguments, and demonstrates its significance in any account of meta-inductive reasoning. Likewise to scientific induction, meta-induction is shown to be constrained by naturalist epistemology in (...) determining which classifications of theoretical methods are `projectable'. Projectability judgments in meta-inductive arguments help ensure that when taking lessons from past theorizing (either as a part of scientific discussions or in a philosophical reflections about science), they would be based on the way evidence was exploited in theory construction or on theoretically plausible hypothesis about the natural world, rather than on human-made concepts. This framework emphasizes the primacy of empirically guided patterns of conjecture over purely formal considerations, enabling epistemic evaluation of research projects, even when theorizing is primarily grounded on abstract theoretical methods and at stages when direct evidence is unavailable. The paper discusses possible implications on philosophical debates concerning theory assessment in fundamental physics, particularly regarding meta-empirical justification, as well as discussions on model transfer in the special sciences. (shrink)

Recent work on the epistemology of climate science includes arguments that are against probabilistic representations of uncertainty about climate and for possibilistic ones as well as some development and use of the latter. I reinstate these arguments, partly by rebutting Corey Dethier’s recent challenge to them and partly by arguing that they remain effective against recent improvements to probabilistic representations. Recognising, however, that the case for possibilistic representations can be undermined by problematic interpretations of epistemic possibilities, I set out criteria (...) of adequacy for such interpretations in the climate context while arguing for a preferred interpretation. I criticise the appropriateness of standard interpretations, according to which a proposition is epistemically possible if and only if it is not recognised to be excluded by what is known, as well as some other prominent non-probabilistic interpretations. So too, I criticise interpretations of epistemic possibilities in terms of upper probabilities. I conclude that an interpretation of epistemic possibilities as possibilities that are consistent with knowledge that approximates the basic way things are is preferable to the other available interpretations. (shrink)

In this article, we mobilize and refine the DEKI account of scientific representation to contend that model organisms are not models tout court but model ‘carriers,’ only abstracted and selected ‘parts’ of which are included in biological models. These parts correspond to phenomena of interest that are interpreted as mechanisms or other kinds of causal processes within certain theoretical domains. The models can then be used to represent similar target phenomena in other organisms. Our proposal paves the way to reconcile (...) opposing positions apropos the representational status of model organisms and build a more robust epistemology of model organism-based research. (shrink)

Scientific models often contain assumptions known not to be true. Despite being false representations, models provide us with a key understanding of phenomena. What is more, the falsehoods that figure in models are in many cases central to them, and there is no available alternative to their use. If falsehoods play such an irreplaceable role in our understanding of phenomena, it would seem that truth is not a key concern of scientific modeling. In this paper, I assess the prospects and (...) challenges of reconciling truth and understanding in scientific modeling. More specifically, I review a thesis recently emerging in the literature, what I shall call the Derivation Thesis (DT), according to which we use models to derive true information. First, I examine different versions of the thesis and develop what I take to be its most promising formulation (what I call the generalized DT). Second, I discuss a serious challenge to the generalized DT. I consider a thought experiment in which an unreliable astrological model gives true explanations by fluke. This scenario challenges the idea that models can provide genuine understanding by generating truths. In response, I argue that genuine scientific models also fulfill a specific normative role that epistemically lucky models lack (what I call the normative generalized DT). I test this hypothesis by analysing how the Ideal Gas Law advances scientific understanding of real gases. (shrink)

This chapter examines three characteristics of environmental sciences—prediction, replication and the use of models—to explore their dissonance with the traditional representation of science. While ‘Science’ is often idealised as objective, universal, and context-independent, environmental sciences operate in ways that do not fit into these assumptions. The chapter draws on Bruno Latour’s distinction between ‘Science’ and ‘sciences’ to argue that environmental sciences, with their inherent uncertainties, local contexts, and interdisciplinary methods, conflict with the image of science as a monolithic and universally (...) reliable source of knowledge. Referring to case studies, the text explores how environmental sciences craft complex, interconnected systems that defy the reproducibility and predictability often associated with ‘Science’. This dissonance, maintains, has led to issues such as climate denialism and the politicisation of scientific knowledge. From this perspective, a call is made for a revaluation of how environmental sciences are represented in both public and policy arenas, calling for a more nuanced understanding of their provisional, context-specific nature and for educational reforms that help the public better grasp the complexities of scientific knowledge production. (shrink)

This paper argues that investigations into how to best make something often provide researchers with an objectual understanding of their target phenomena. This argument starts with an extended investigation into the non-representational uses of models. In particular, we identify a special sort of “design model” whose aim is to guide the production of phenomena. Clarifying how these design models are evaluated shows that they are evaluated in different ways than representational models. Once the character of design models has been fixed, (...) we argue that grasping design models can provide objectual understanding of phenomena. This argument proceeds through a critical engagement with Dellsén’s ( 2020 ) position that a grasp of a good representational model of dependencies provides objectual understanding of a phenomenon. We agree with Dellsén that this is one way to achieve understanding, but maintain that grasping a good design model is another way to achieve understanding. The paper concludes by considering some important objections to our proposal and also by noting some of the broader questions about understanding and knowledge in both science and engineering. (shrink)

Scientific literacy is an essential aspect of an undergraduate education. Recipes for Science responds to this need by providing an accessible introduction to the nature of science and scientific methods appropriate for any beginning college student. The book is adaptable to a wide variety of different courses, such as introductions to scientific reasoning, methods courses in scientific disciplines, science education, and philosophy of science. -/- Recipes for Science ​​was first published in 2018, and a thoroughly revised second edition was published (...) in 2024. Special features include contemporary and historical case studies from many fields of physical, life, and social sciences; visual aids to clarify and illustrate ideas; text boxes to explore related topics; plenty of exercises to support student recall and application of concepts; suggestions for further readings at the end of each chapter; a glossary with helpful definitions of key terms; and a companion website with course syllabi, PowerPoint presentations, additional exercises, and original short videos on key topics. (shrink)

James Nguyen & Roman Frigg (2022). Scientific Representation. Cambridge University Press, 90pp., €21.23 (Paperback), ISBN: 9781009009157.

In this paper, I develop a template-based analysis to include several elements of _processes_ through which templates are transferred between fields of inquiry. The analysis builds on Justin Price’s identification of the importance of a “landing zone” in the recipient domain, from which “conceptual pressure” may be created. I will argue that conceptual pressure is a characteristic feature of the process of template transfer; that this means that there are costs to the process of transfer as well as benefits; and (...) that it would be reasonable if modelers try to mitigate these costs. I will discuss two such mitigation strategies: ‘conceptual embedding’ and ‘customization’. I illustrate the claims, focusing on the mitigation strategies, with a case study: that of pioneering applications of reaction–diffusion equations in mathematical ecology. (shrink)

It is often claimed that one can avoid the kind of underdetermination that is a typical consequence of symmetries in physics by stipulating that symmetry-related models represent the same state of affairs (Leibniz Equivalence). But recent commentators (Dasgupta 2011; Pooley 2021; Pooley and Read 2021; Teitel 2021a) have responded that claims about the representational capacities of models are irrelevant to the issue of underdetermination, which concerns possible worlds themselves. In this paper I distinguish two versions of this objection: (1) that (...) a theory’s formalism does not (fully) determine the space of physical possibilities, and (2) that the relevant notion of possibility is not physical possibility. I offer a refutation of each. (shrink)

Scientific models can be performative: they can causally affect the phenomena they are intended to represent. The existing literature offers two responses. The appraisal view emphasizes that performativity can sometimes be a good-making model attribute, e.g., when predictions steer the public’s behavior in desirable ways. The mitigation view seeks to endogenize agents’ behavioral response to model-issued forecasts to get rid of performativity instead. This paper argues that neither approach is fully compelling: the appraisal view encounters severe concerns about moral values (...) illegitimately encroaching on how modelers construct and use models, while the mitigation view fails to acknowledge that endogenization is itself a choice that involves substantive value-judgments relating to the desirability of certain social outcomes. (shrink)

Depression is a widespread and debilitating disorder, but developing effective treatments has proven challenging. Despite success in animal models, many treatments fail in human trials. While various factors contribute to this translational failure, standardization practices in animal research are often overlooked. This paper argues that certain standardization choices in behavioral neuroscience research on depression can limit the generalizability of results from rodents to humans. This raises ethical and scientific concerns, including animal waste and a lack of progress in treating human (...) patients. To address these issues, animal ethics committees can establish reasonable expectations for preclinical research and highlight the impact of standardization on generalizability. Such efforts can help improve translation from animal models to human patients and ultimately benefit those suffering from depression. (shrink)

The idea that “simplicity is a sign of truth”, and the related “Occam’s razor” principle, stating that, all other things being equal, simpler models should be preferred to more complex ones, have been long discussed in philosophy and science. We explore these ideas in the context of supervised machine learning, namely the branch of artificial intelligence that studies algorithms which balance simplicity and accuracy in order to effectively learn about the features of the underlying domain. Focusing on statistical learning theory, (...) we show that situations exist for which a preference for simpler models provably slows down, instead of favoring, the supervised learning process. Our results shed new light on the relations between simplicity and truth approximation, which are briefly discussed in the context of both machine learning and the philosophy of science. (shrink)

Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...) I argue for a unified account of these key concepts that treats the concept of understanding as fundamental. This allows resources from the philosophy of science and the epistemology of understanding to help guide opacity alleviation efforts. A first significant benefit of this understanding account is that it defuses one of the primary, in-principle objections to post hoc explainable AI (XAI) methods. This “rationalization objection” argues that XAI methods provide mere rationalizations rather than genuine explanations. This is because XAI methods involve using a separate “explanation” system to approximate the original black box system. These explanation systems function in a completely different way than the original system, yet XAI methods make inferences about the original system based on the behavior of the explanation system. I argue that, if we conceive of XAI methods as idealized scientific models, this rationalization worry is dissolved. Idealized scientific models misrepresent their target phenomena, yet are capable of providing significant and genuine understanding of their targets. (shrink)

This article addresses the contributions of the literature on the new mechanistic philosophy of science for the scientific practice of model building in ecology. This is reflected in a one-to-one interdisciplinary collaboration between an ecologist and a philosopher of science during science-in-the-making. We argue that the identification, reconstruction and understanding of mechanisms is context-sensitive, and for this case study mechanistic modeling did not present a normative role but a heuristic one. We expect our study to provides useful epistemic tools for (...) the improvement of empirically-riven work in the debates about mechanistic explanation of ecological phenomena. (shrink)

Metaphilosophy, Volume 53, Issue 2-3, Page 202-220, April 2022.

Sometimes, scientific models are either intended to or plausibly interpreted as representing nonactual but possible targets. Call this “hypothetical modeling”. This paper raises two epistemological challenges concerning hypothetical modeling. To begin with, I observe that given common philosophical assumptions about the scope of objective possibility, hypothetical models are fallible with respect to what is objectively possible. There is thus a need to distinguish between accurate and inaccurate hypothetical modeling. The first epistemological challenge is that no account of the epistemology of (...) hypothetical models seems to cohere with the most characteristic function of scientific modeling in general, i.e., surrogative representation. The second epistemological challenge is a version of “reliability challenges” familiar from other areas. There is a challenge to explain how hypothetical models could be a reliable guide to what is possible, given that they are not and cannot be compared against their nonactual targets and updated accordingly. I close with some brief remarks on possible solutions to these challenges. (shrink)

In the context of pluralization, sophistication, and combination of formal models, it is becoming difficult to propose uniform – or even comparable – model comparison practices. This paper outlines a broad and classificatory comparative epistemology of models. The aim of this epistemology is to propose applicable, and if necessary rectifiable, conceptual tools that can be useful to modellers as well as to historians and epistemologists. The notion of model characteristic vector – incorporating concepts of function, nature, principle and use of (...) the model – is introduced for this end. This proposal has been developed to serve a better-equipped writing of a comparative and interdisciplinary history of models and simulations. (shrink)

Neuroscientists have in recent years turned to building models that aim to generate predictions rather than explanations. This “predictive turn” has swept across domains including law, marketing, and neuropsychiatry. Yet the norms of prediction remain undertheorized relative to those of explanation. I examine two styles of predictive modeling and show how they exemplify the normative dynamics at work in prediction. I propose an account of how predictive models, conceived of as technological devices for aiding decision-making, can come to be adequate (...) for purposes that are defined by both their guiding research questions and their larger social context of application. (shrink)

In an ever-changing world, when we search for answers on our present challenges, it can be tricky to extrapolate past realities when concerning science-based issues. Climate change, public health or artificial intelligence embody issues on how scientific evidence is often challenged, as false beliefs could drive the design of public policies and legislation. Therefore , how can we foresee if science can tip the scales of political legislation? In this article, we outline how models of historical cases can be used (...) to predict and understand how scientific evidence can influence the emergence (or fall-back) of science based-legislation. We also present frameworks on how to use past episodes of History of Science and Alternative History insights to build epistemic models, based on previous successful approaches on the spread of scientific misinformation. These models will help the accuracy of the design of eventual alternative realities, that can come insightful on present decision making methodologies. (shrink)

Examining AI spirituality can illuminate problematic assumptions about human spirituality and AI cognition, suggest possible directions for AI development, reduce uncertainty about future AI, and yield a methodological lens sufficient to investigate human-AI sociotechnical interaction and morality. Incompatible philosophical assumptions about human spirituality and AI limit investigations of both and suggest a vast gulf between them. An emergentist approach can replace dualist assumptions about human spirituality and identify emergent behavior in AI computation to overcome overly reductionist assumptions about computation. Using (...) general systems theory to organize models of human experience yields insight into human morality and spirituality, upon which AI modeling can also draw. In this context, the pragmatist Josiah Royce’s semiotic philosophy of spirituality identifies unanticipated overlap between symbolic AI and spirituality and suggests criteria for a human-AI community focused on modeling morality that would result in an emergent Interpreter-Spirit sufficient to influence the ongoing development of human and AI morality and spirituality. (shrink)

This paper argues that an important type of experiment-target inference, extrapolating causal effects, requires models to be successful. Focusing on extrapolation in Evidence-Based Policy, it is ar...

This paper discusses modeling from the artifactual perspective. The artifactual approach conceives models as erotetic devices. They are purpose-built systems of dependencies that are constrained in view of answering a pending scientific question, motivated by theoretical or empirical considerations. In treating models as artifacts, the artifactual approach is able to address the various languages of sciences that are overlooked by the traditional accounts that concentrate on the relationship of representation in an abstract and general manner. In contrast, the artifactual approach (...) focuses on epistemic affordances of different kinds of external representational and other tools employed in model construction. In doing so, the artifactual account gives a unified treatment of different model types as it circumvents the tendency of the fictional and other representational approaches to separate model systems from their “model descriptions.”. (shrink)

A Série Investigação Filosófica, uma iniciativa do Núcleo de Ensino e Pesquisa em Filosofia do Departamento de Filosofia da UFPel e do Grupo de Pesquisa Investigação Filosófica do Departamento de Filosofia da UNIFAP, sob o selo editorial do NEPFil online e da Editora da Universidade Federal de Pelotas, com auxílio financeiro da John Templeton Foundation, tem por objetivo precípuo a publicação da tradução para a língua portuguesa de textos selecionados a partir de diversas plataformas internacionalmente reconhecidas, tal como a Stanford (...) Encyclopedia of Philosophy, por exemplo. O objetivo geral da série é disponibilizar materiais bibliográficos relevantes tanto para a utilização enquanto material didático quanto para a própria investigação filosófica. (shrink)

The notion of understanding occupies an increasingly prominent place in contemporary epistemology, philosophy of science, and moral theory. A central and ongoing debate about the nature of understanding is how it relates to the truth. In a series of influential contributions, Catherine Elgin has used a variety of familiar motivations for antirealism in philosophy of science to defend a non- factive theory of understanding. Key to her position are: (i) the fact that false theories can contribute to the upwards trajectory (...) of scientific understanding, and (ii) the essential role of inaccurate idealisations in scientific research. Using Elgin’s arguments as a foil, I show that a strictly factive theory of understanding has resources with which to offer a unified response to both the problem of idealisations and the role of false theories in the upwards trajectory of scientific understanding. Hence, strictly factive theories of understanding are viable notwithstanding these forceful criticisms. (shrink)

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information (...) into the approach, appropriate to different collections of models. (shrink)

We were slightly concerned, upon having read Eric Winsberg, Jason Brennan and Chris Surprenant’s reply to our paper “Were Lockdowns Justified? A Return to the Facts and Evidence”, that they may have fundamentally misunderstood the nature of our argument, so we issue the following clarification, along with a comment on our motivations for writing such a piece, for the interested reader.

Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London may have influenced scientific advice, policy, and individual responses, (...) we consider the implications of epidemiological models’ performative capacities. We argue, first, that performativity may impair models’ ability to successfully predict the course of an epidemic; but second, that it may provide an additional sense in which these models can be successful, namely by changing the course of an epidemic. (shrink)

I identify the central theses of True Enough and argue that Elgin's principal argument for her non-factive view of understanding fails. This argument emphasizes the cognitive contributions of science (and other disciplines) that occur via false claims. Careful reflection reveals that it is actually Elgin’s view that mishandles those contributions. Her non-factive view is also unable to accommodate other types of epistemic improvement, and makes a range of simple comparisons of understanding impossible.

The second edition of the work of the Brazilian physicist Paulo C. Abrantes (2016), entitled Images of nature, images of science, is a good alternative for students of history and philosophy of science. The reason is Abrantes' thesis in this work: to defend that the development of scientific knowledge is dependent on the influence of different images of "nature" and "science" existing during the history of Western scientific-philosophical thought; and an advocate for the historian of science Studying as reasons that (...) allow the adoption of such images at a given time. Through the analysis of historical cases of scientific thought, Abrantes argues that the consolidation of research programs in different subareas of natural science - such as biology, physics and chemistry - was only possible due to the influence, in particular time, of images of nature and science. (shrink)

Carrie Figdor argues for literalism, a semantic claim about psychological predicates, on the basis of a scientific claim about the nature of psychological properties. I argue that her scientific claim is based on controversial interpretations of scientific modelling, and that even if it were correct it would not justify her claims that psychological predicates are undergoing radical conceptual change.

Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five (...) evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned. (shrink)

From the beginning of time, humans believed they were the center of the universe. Such important beings could be nowhere else than at the very epicenter of existence, with all the other things revolving around them. Was this an arrogant position? Only time will tell. What is certain is that as some people were so certain of their significance, aeons later some other people became too confident in their unimportance. In such a context, the Earth quickly lost its privileged position (...) at the center of the universe and along with this, the ideas of absolute motion and time became unbearable for the modern intellect, which saw nothing but relativeness in everything. After years of accepting the ideas of relativity at face value without doubting them, scientists are now mature enough to start questioning everything as any true scientist would do, including their own basic assumptions. And one would be surprised to see that the basic assumptions of today’s science in physics (and cosmology alike) are based on philosophically dogmatic beliefs that humans are nothing more than insignificant specks of dust. These specks cannot be in any privileged position in the cosmos, nor can their frames of reference. These specks cannot be living on a planet that is not moving while everything else is. There can be no hint of our importance whatsoever. Hence, the Copernican principle that has poisoned scientific thinking for aeons now. When one analyzes the evidence provided by science to support the idea of relativity though, he would see that the same evidence can more easily and simply fit into a model where the Earth stands still. Yet, scientists preferred to revamp all physics by introducing the totally unintuitive ides of relativity – including the absolute limit of the speed of light – than even admitting the possibility of humans having any notion of central position in the cosmos. True scientists though should examine all possible explanations, including those that do not fit their beliefs. To the dismay of so many modern scientists who blindly believe the validity of the theory of relativity at face value, the movement towards a true and honest post-modern science where all assumptions are questioned, necessarily passes through a place where the Earth we live in stands still. Non-relativistic explanations of the Michelson Morley experiment, related to a motionless Earth or to ether, are viable alternatives that deserve their place in modern scientific thought. (shrink)

Quantum mechanics was reformulated as an information theory involving a generalized kind of information, namely quantum information, in the end of the last century. Quantum mechanics is the most fundamental physical theory referring to all claiming to be physical. Any physical entity turns out to be quantum information in the final analysis. A quantum bit is the unit of quantum information, and it is a generalization of the unit of classical information, a bit, as well as the quantum information itself (...) is a generalization of classical information. Classical information refers to finite series or sets while quantum information, to infinite ones. Quantum information as well as classical information is a dimensionless quantity. Quantum information can be considered as a “bridge” between the mathematical and physical. The standard and common scientific epistemology grants the gap between the mathematical models and physical reality. The conception of truth as adequacy is what is able to transfer “over” that gap. One should explain how quantum information being a continuous transition between the physical and mathematical may refer to truth as adequacy and thus to the usual scientific epistemology and methodology. If it is the overall substance of anything claiming to be physical, one can question how different and dimensional physical quantities appear. Quantum information can be discussed as the counterpart of action. Quantum information is what is conserved, action is what is changed in virtue of the fundamental theorems of Emmy Noether (1918). The gap between mathematical models and physical reality, needing truth as adequacy to be overcome, is substituted by the openness of choice. That openness in turn can be interpreted as the openness of the present as a different concept of truth recollecting Heidegger’s one as “unconcealment” (ἀλήθεια). Quantum information as what is conserved can be thought as the conservation of that openness. (shrink)

Policymakers who seek to make scientifically informed decisions are constantly confronted by scientific uncertainty and expert disagreement. This thesis asks: how can policymakers rationally respond to expert disagreement and scientific uncertainty? This is a work of non-ideal theory, which applies formal philosophical tools developed by ideal theorists to more realistic cases of policymaking under scientific uncertainty. I start with Bayesian approaches to expert testimony and the problem of expert disagreement, arguing that two popular approaches— supra-Bayesianism and the standard model of (...) expert deference—are insufficient. I develop a novel model of expert deference and show how it can deal with many of these problems raised for them. I then turn to opinion pooling, a popular method for dealing with disagreement. I show that various theoretical motivations for pooling functions are irrelevant to realistic policymaking cases. This leads to a cautious recommendation of linear pooling. However, I then show that any pooling method relies on value judgements, that are hidden in the selection of the scoring rule. My focus then narrows to a more specific case of scientific uncertainty: multiple models of the same system. I introduce a particular case study involving hurricane models developed to support insurance decision-making. I recapitulate my analysis of opinion pooling in the context of model ensembles, confirming that my hesitations apply. This motivates a shift of perspective, to viewing the problem as a decision theoretic one. I rework a recently developed ambiguity theory, called the confidence approach, to take input from model ensembles. I show how it facilitates the resolution of the policymaker’s problem in a way that avoids the issues encountered in previous chapters. This concludes my main study of the problem of expert disagreement. In the final chapter, I turn to methodological reflection. I argue that philosophers who employ the mathematical methods of the prior chapters are modelling. Employing results from the philosophy of scientific models, I develop the theory of normative modelling. I argue that it has important methodological conclusions for the practice of formal epistemology, ruling out popular moves such as searching for counterexamples. (shrink)