Models and Explanation

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)

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)

This paper proposes a conceptual framework for analyzing the order of observed dynamical systems in terms of the escalation of feedback architectures. Rather than organizing physical, chemical, biological, and technological systems according to ontological levels or evolutionary narratives, the framework treats them as successive classes of stable regulatory architectures, distinguished by the geometry of their attractors and basins of attraction. Random variations and reorganizations of feedback architectures may enable increases in control power, understood as the capacity to constrain and sustain (...) system trajectories over time. However, only those configurations whose attractors remain dynamically stable are preserved, implying a form of stability-based selection rather than adaptation or optimization. The framework is functional and non-normative: it does not presuppose goals, intentionality, or teleology at any level of analysis. Escalation is shown to proceed through qualitative reorganizations of regulatory architecture, from dynamically closed systems, through energetic and informational regulation, to metaregulatory technological systems capable of simulating and selecting architectures prior to physical realization. While later architectures achieve unprecedented control power, they do so at the cost of increasingly narrow basins of attraction, leading to heightened global fragility and sharp stability cliffs. The paper situates this account in relation to dynamical systems theory and clarifies its relation to the free-energy principle, which is interpreted as a condition of trajectory stability rather than a theory of architectural structure. Overall, the framework offers a unifying, non-teleological perspective on order, stability, and control across domains, emphasizing architecture rather than substrate as the primary axis of analysis. (shrink)

We propose a unified relational dynamical framework for the emergence of structure across physical, biological, cognitive, and technological systems. The starting point is the notion of minimal difference as the simplest deviation from a trivial regime in which relations are globally reducible and generate no structural effects. The key mechanism is the transition from minimal difference to returning difference, understood as an effect that does not vanish along closed trajectories. We show that a returning difference constitutes the minimal form of (...) feedback, enabling the accumulation of effects and the onset of nontrivial dynamics. A central role in the proposed framework is played by recursion, understood not as mere iteration of a rule, but as a structural property of dynamics in which transformations act on the results of previous transformations. Recursion is one of the fundamental mechanisms underlying the generation of structure: it enables the formation of feedback loops, hierarchical organization, and multi-level regulatory systems. In this sense, returning difference can be viewed as the elementary form of recursion grounded in relational dynamics. We formalize relational dynamics in terms of local relations and their non-closure (curvature), which, in the presence of appropriate operators, generate iterated feedback. The introduction of selection and projection mechanisms leads to invariant sets and concentration of trajectories, interpreted as the formation of stable structures. Extending the model to the statistical level allows for the description of probabilistic dynamics, metastability, and the role of fluctuations. In the continuous limit, the framework leads to effective field descriptions, and when coupled with geometry, to the co-evolution of fields and spatial structure. At a global level, entropy, information, and elements of geometric structure can be understood as emergent properties arising from relational feedback dynamics. The proposed approach is structural and effective: it does not replace existing physical theories, but identifies common mechanisms of organization across domains. In particular, we argue that the transition from minimal difference to returning difference—as the elementary form of recursion and feedback—defines a universal threshold at which the emergence of complex structures becomes possible. (shrink)

Quantum mechanics contains a single category error: it treats mechanical registration as if it were Observer‑level recognition. A detector click, pointer position, or classical record is a trace (T), not an outcome. The definite outcome arises only when a meaning‑bearing Observer performs the recognition step (Jo) and commits to a specific interpretation (C). Because QM models the physical evolution of systems and the production of traces but does not model recognition and commitment, it collapses two distinct steps into one and (...) generates the measurement problem, Schrödinger‑type paradoxes, Wigner’s Friend, decoherence confusion, and the entire interpretational landscape. -/- This paper extends the ongoing development of the Universal Principle of Collapse (UPC), presenting a focused analysis of its bridge to quantum mechanics. UPC is a formal, domain‑general operator chain: PO → MO → s → LO → Jo → C → T, that makes the missing structure explicit. It is not metaphysics, psychology, or interpretation; it is a structural architecture, and isomorphic with the measurement sequence that QM already uses. We show that QM is a special case of this universal sequence: it formalizes the device‑side operators and silently assumes the observer‑side operators. -/- Using a pocket calculator, a 1920s mechanical cash register, and a laboratory measurement, we demonstrate that only observers perform recognition and collapse. Once Jo is restored, collapse becomes semantic rather than physical, decoherence is no longer mistaken for collapse, and the paradoxes disappear. UPC reveals the full architecture that quantum mechanics has always relied on but never formalized. -/- Quantum experiments are classical–quantum–classical chains: classical apparatus prepares and detects, a microscopic quantum transition occurs, and the detector amplifies that imperceptible transition into a classical trace. The outcome is not in the device; the trace has no meaning on its own. The Observer recognizes the trace and commits to its meaning. -/- Note: This paper distinguishes physical traces from recognized outcomes. Quantum mechanics models the former (T), not the latter (Jo → C). UPC does not modify or alter QM; it clarifies the structural step QM presupposes when speaking of an “outcome.” The UPC framework is a structural operator chain, not an interpretation or metaphysical claim. A fuller reader’s guide appears at the end of the Introduction and in Appendix A7. May 26, 2026 -/- Authored by Eloy Escagedo Gutierrez as part of The Universal Principle of Collapse (UPC) Research Project. (shrink)

Some how-possibly explanations have epistemic value because they are epistemically possible; we cannot rule out their truth. One paradoxical implication of that proposal is that epistemic value may be obtained from mere ignorance. For the less we know, then the more is epistemically possible. This chapter examines a particular class of problematic epistemically possible how-possibly explanations, viz. *epistemically opaque* how-possibly explanations. Those are how-possibly explanations justified by an epistemically opaque process. How could epistemically opaque how-possibly explanations have epistemic value if (...) they result from a process about which we lack knowledge or understanding? This chapter proposes three different strategies to salvage epistemic value from epistemic opacity, namely salvaging value from 1) functional transparency, 2) modal operator interpretation, and 3) pursuitworthiness. It illustrates using cases from deep neural network modeling. (shrink)

A signal development in contemporary physics is the widespread use, in explanatory contexts, of highly idealized models. This paper argues that some highly idealized models in physics have genuine explanatory power, and it extends the explanatory role for such idealizations beyond the scope of previous philosophical work. It focuses on idealizations of nonlinear oscillator systems.

This paper introduces the concept of temporal lifting as a constructive analytic framework for reinterpreting apparent singularities in nonlinear dynamical systems, particularly the incompressible Navier–Stokes equations on the three-torus T³ = ℝ³ ∕ ℤ³. The approach suggests that finite-time blow-up is not an intrinsic breakdown of the equations but a compression of the physical time coordinate. By defining a smooth, strictly monotone lifting map φ : t ↦ τ and expressing the flow as U(x, τ) = u(x, φ⁻¹(τ)), the system (...) becomes globally C^∞ while preserving energy inequalities and invariants. This procedure is formally analogous to the Path Lifting Lemma in topology, in which a loop on S¹ lifts to a smooth path on ℝ. Philosophically, the result reframes mathematical singularity as a coordinate phenomenon, implying that continuity and regularity are functions of parametrization rather than absolute properties of nature. -/- Categories: Philosophy of Mathematics → Mathematical Methodology Philosophy of Science → Theoretical Physics -/- Keywords: time geometry; continuity; Navier–Stokes; regularity; coordinate systems; topology of flows; mathematical ontology -/- External link: HAL preprint (8 Oct 2025) See Link. (shrink)

In this paper, I examine Reinforcement Learning modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang’s account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.

The Covid-19 global pandemic had a profound effect on scientific practice. During this time, officials crucially relied on the work done by modellers. This raises novel questions for the philosophy of science. Here, I investigate the possibility of ‘natural models’ in predicting the virus’ trajectory for epidemiological purposes. I argue that to the extent that these can be consideredscientific models, they support the possibility of a continuum from scientific models to natural models differing in artifactual commitment. In making my case, (...) I draw from work on model organisms and natural experiments as well as recent work in epidemiology. (shrink)

Mechanistic explanation is typically understood as a model-based activity, yet the role of structure in mechanistic modeling remains insufficiently clarified. This paper argues that mechanistic modeling requires two irreducible types of structure, dynamical and hierarchical, and analyzes structure as a constraint on model construction within Frigg's framework of scientific representation. Dynamical structure captures the temporal organization of mechanisms, whereas hierarchical structure captures their compositional organization, together imposing complementary constraints on the construction of mechanistic models. A case study of the action (...) potential shows that adequate mechanistic explanation requires the coordinated integration of both forms of structure. The paper thereby provides a unified framework integrating mechanistic modeling, mechanistic explanation, and scientific representation through structured model construction. (shrink)

Coherence-Weighted Information Theory and the induced-weights program that grounds it require empirical validation across the cross-domain regime where their universality claims live. Adaptive Realism and Synthetic Epistemology forbid correspondence-truth validation at the foundational level. The real is what persists across recursive coherence arbitration, and external ground-truth oracles violate the framework's ontology before any measurement is taken. I specify a meta-coherence validation architecture that operates inside the framework's commitments. Three within-domain signatures of self-consistent measurement are required jointly: cross-philosophy convergence (R2) across (...) estimator classes with genuinely different inductive biases, persistence prediction (R1) grounding induced weights in Adaptive Realism's persistence criterion, and intervention asymmetry (R3) operationalizing the substrate-symbol distinction. A fourth condition, M5, carries the domain-translation invariance commitment as a structurally distinct admissibility gate sequenced before the within-domain matrix. The program tests these conditions across three domains spanning a substrate-stream transparency gradient, including a hidden semi-Markov source with structured emissions, Pfam protein families, and FOMC statements. An eight-cell outcome interpretation matrix binds in advance what each result configuration means for specific framework claims. The paper specifies the program; running it is the next layer. (shrink)

Modern cosmology is marked by persistent tensions among different measurement methods, ranges of scale, and cosmological epochs. These tensions are often treated as local inconsistencies within a fundamentally unified theoretical framework. This paper proposes a different methodological reading: persistent tensions, provided they are stably reproducible and arise at transitions between modeling regimes, may indicate an overextension of the cosmological claim to unity. This paper is not offered as a physical alternative to modern cosmology, but as a philosophy-of-science case analysis of (...) domain-bound model validity. It argues that cosmological practice already operates with different modeling regimes at the operational level, but that this domain dependence is not always consistently translated into an explicit theory of validity, domain boundaries, transition rules, and the level at which falsification applies. Cosmological tensions are thereby not relativized, but made legible at a different level: as possible indications that not only parameters or auxiliary assumptions, but model-domain assignments themselves may become subject to assessment. What is proposed is therefore not the abandonment of scientific unity, but its reorganization as second-order unity. Unity then no longer resides exclusively in a single model that is supposed to bear all cosmological domains directly, but in explicit rules of domain assignment, controlled transitions, overlapping testing zones, and shared consistency conditions. The level of falsification thereby shifts: not only individual parameters are at stake, but also the question of whether a model is still appropriately assigned to a particular domain. The paper therefore decides no physical hypothesis, but develops a methodological architecture in which model plurality and scientific unity are related to one another in a controlled way. (shrink)

Contemporary debates in philosophy, science, media studies, and technology are increasingly shaped by conflicts over what counts as real. These conflicts are frequently framed as contests between realism and relativism, objectivity and construction, facts and narratives. This paper argues that such oppositions conceal a more fundamental problem: the absence of a coherent framework for distinguishing modes and degrees of reality. Relative Reality Theory (RRT) develops a systematic account of reality as a graded and mode-specific status rather than as an absolute (...) property. Reality is understood as a function of experiential immediacy, intersubjective stability, and functional efficacy. On this basis, RRT reconstructs distinct modes of reality, subjective, intersubjective, and functional reality, without dissolving them into a unified ontological hierarchy or reducing them to mere opinions. RRT provides a conceptual framework for analyzing real-world phenomena such as scientific models, media realities, political narratives, simulations, and virtual environments. By clarifying the conditions under which something can count as real in a given sense, the theory avoids both ontological absolutism and arbitrary relativism. It provides tools for identifying category mistakes, resolving apparent conflicts of reality, and understanding why certain realities remain stable despite contestation. The paper positions RRT as a philosophical ordering framework rather than as an operative or technical theory of model management. Its aim is not to calculate or optimize reality claims, but to clarify their modes, scope, limits, and conditions of validity. In doing so, RRT contributes to contemporary epistemology and philosophy of science by providing a precise vocabulary for navigating reality under conditions of pluralism, mediality, and systemic complexity. (shrink)

Epistemics understands epistemic practice as model management under finite conditions (Rapp 2026a). This determination remains incomplete, however, as long as it remains unclear how fields of experience become model-capable, that is, available for model formation, in the first place. Models do not arise directly from experience; rather, they presuppose that experience has already been ordered, differentiated, and epistemically stabilized. The present paper examines this prior level of stabilization and determines models as explicitly identifiable, workable, and revisable stabilizations of epistemically ordered (...) fields of experience. The central thesis is this: epistemic stabilization does not simply reduce uncertainty; it often first makes it localizable. As long as a field of experience remains diffuse, it also remains unclear what precisely its uncertainty consists in. Only when an epistemic system posits a provisional order, such as a heuristic, a schema, a case hypothesis, or a model, does it become apparent which relations hold, which information is missing, which assumptions are too strong, and where fault lines arise. This provisional stabilization is therefore not merely a preliminary stage of secured knowledge, but an epistemic act of testing. From this perspective, central concepts of Epistemics are made more precise. Validity designates the carrying capacity of a stabilization under finite conditions. Friction is the point at which its boundary becomes visible. Revision is the restructuring of a stabilization when this boundary becomes epistemically relevant. At the same time, the paper shows that degrees of stabilization, from heuristic through schema and model to formalization, do not form a ladder of progress. Stronger stabilization is not automatically better inquiry; what is decisive is the adequacy of the stabilization in relation to the state of information, purpose, domain of validity, costs, and revisability. The paper supplements Epistemics by systematically opening up the area before the explicit model. Model management does not begin only with completed models, but wherever an epistemic system stabilizes a field of experience in such a way that it can become visible what holds, what is missing, and what does not fit. (shrink)

This paper develops the concept of friction as an epistemic diagnostic category for stability under finite conditions. Friction is not treated as mere disturbance, resistance, error, anomaly, falsification, or revision. Rather, it is defined as the epistemically legible non-fitting of an activated expectation or model structure under conditions of enactment. Friction becomes relevant where a pattern of stabilization no longer carries fittingly under strain and thereby makes boundaries, costs, mis-couplings, or the need for transformation visible. -/- The paper distinguishes subjective, (...) intersubjective, and functional-empirical domains as analytical axes for reading different forms of friction. It argues that friction functions as a boundary signal of finite load-bearing capacity: it shows where models, routines, institutions, technical systems, or scientific theories remain viable under load, where their costs increase disproportionately, and where revision, limitation, recoupling, or transformation becomes necessary. On this basis, the paper develops a typology of subjective, intersubjective, functional-empirical, and cross-domain friction. -/- Special attention is given to scientific model formation, physical boundary concepts, mathematical indeterminacy, bureaucracy, technology, law, and artificial intelligence. The paper argues that many systems do not eliminate friction, but displace, conceal, or redistribute it. Against both naive realism and naive relativism, friction is presented as an intermediate diagnostic concept: it makes the costs and limits of model validity visible before a model is prematurely rejected as false or dogmatically stabilized despite rising costs. The paper concludes by introducing friction competence as a design and diagnostic principle for keeping friction legible, attributable, and handleable under conditions of finite stabilization. (shrink)

Scientific and technical search processes unfold under finite conditions. Limited resources, alongside growing model complexity, generate structural tensions between stability consolidation and exploratory opening. Although this tension field has been described across disciplines, a generic architecture is often missing that can structure the dynamic reweighting between consolidation and exploration by means of explicit efficiency indicators. The operative vocabulary used in this paper is canon-compatible, derived from Epistemik as an epistemic infrastructure as well as from the concept of friction as a (...) boundary signal of finite capacity developed therein. This is not an independent theory, but rather an architectural extraction and continuation of these structural concepts. The specific contribution lies in elaborating a Dual-Mode Architecture of model management that describes search processes as dynamic configurations of stabilized transition types and formulates a relational dominance logic: In real search systems, transitions between consolidation and exploration typically occur as gradual shifts of relative mode dominance in resource allocation. Validity is understood as robustness under disturbance, costs as required process tension. Friction functions as a diagnostic indicator of a declining ratio of robustness gain to effort. Increasing friction density, robustness plateaus, and nonlinear tension dynamics mark threshold consolidations at which a priority reweighting in favor of exploratory transition types becomes structurally plausible. The architecture is intended as a generic instrument for increasing structural search efficiency under finite conditions. It neither replaces domain-specific theories nor offers truth guarantees, but makes the control logic of model spaces explicitly formulable and thereby describable as an operative prerequisite for adaptive model development. (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)

The classical distinction between physics and metaphysics has become increasingly inadequate for describing contemporary epistemic practice. While empirical physics is often implicitly treated as the sole standard of the real, subjective experience and social order are either reduced or relegated to an indeterminate metaphysical remainder. This paper proposes an alternative epistemic ordering. Starting from Epistemics as a prior clarification framework, reality is not understood as a unified domain of objects, but as a configuration of distinct physics of stability: a subjective (...) physics of experience, an intersubjective physics of social order, and a functional-empirical physics of effective constraints. These physics are neither ontological domains nor hierarchically ordered; they differ by their mechanisms of stabilization, load-bearing limits, and characteristic boundary signals. Central to this ordering is the distinction between validity and stability. Epistemics clarifies in which physics a reality claim can be meaningfully raised and tested, that is, its physics of validity. Only within such a validity-physics can stability, robustness, and failure be determined. In this context, friction is analyzed as an epistemically legible boundary signal that makes limited load-bearing capacity visible without constituting an ontological principle. Friction also functions as a selection mechanism over time. Different stabilization patterns generate different costs under strain, which stabilizes some structures while others are abandoned. In this way, core functions traditionally assigned to metaphysics can be fulfilled with greater epistemic precision, without formulating ultimate ontological claims. The focus shifts from whether something is real to the question under which validity conditions and stability limits reality claims can be meaningfully tested. (shrink)

For various reasons, it has become common wisdom in science that there exists a principled epistemic distinction between direct and indirect observation. In this paper, I present a twofold argument. First, I argue against such a principled epistemic distinction. Second, I highlight a pervasive incongruence between the methodological and epistemological distinctions between direct and indirect observations. My arguments revolve around the idea that it is one thing to make a methodological distinction between observations and another to ascribe epistemic significance to (...) them. I begin by unfolding the historical and philosophical foundations of the distinction, identifying three tenets that have served to sustain the distinction to the present day. I then provide a detailed analysis of two recent philosophical efforts to preserve the epistemic distinction in astrophysics and specific areas of astrophysics, ultimately suggesting that these approaches face significant challenges. (shrink)

It is plausible that the models of scientific theories correspond to possibilities. But how do we know which models of which scientific theories so correspond? This paper provides a novel proposal for guiding belief about possibilities via scientific theories. The proposal draws on the notion of an effective theory: a theory that applies very well to a particular, restricted domain. We argue that it is the models of effective theories that we should believe correspond, at least in part, to possibilities. (...) It is thus effective theories that should guide modal reasoning in science. (shrink)

We present a latent-space formulation of adaptive temporal reparametrization for continuous-time dynamical systems. The method, called temporal lifting, introduces a smooth monotone mapping t↦τ​(t) that regularizes near-singular behavior of the underlying flow while preserving its conservation laws. In the lifted coordinate, trajectories such as those of the incompressible Navier–Stokes equations on the torus T3 become globally smooth. From the standpoint of machine-learning dynamics, temporal lifting acts as a continuous-time normalization or time-warping operator that can stabilize physics-informed neural networks and other (...) latent-flow architectures used in AI systems. The framework links analytic regularity theory with representation-learning methods for stiff or turbulent processes. (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)

The “sloppy models” program originated in systems biology, but has seen applications across a range of fields. Sloppy models are dependent on a large number of parameters, but highly insensitive to the vast majority of parameter combinations. Sloppy models proponents claim that the program may explain the success of science. I argue that the sloppy models program can at best provide a very partial explanation. Drawing a parallel with renormalization group realism, I argue that it would only give us grounds (...) for a minimal kind of scientific realism. Nonetheless, the program can offer certain epistemic virtues. (shrink)

Contemporary action theory is generally concerned with giving theories of action ontology. In this paper, we make the novel proposal that the standard view in action theory—the Causal Theory of Action—should be recast as a “model”, akin to the models constructed and investigated by scientists. Such models often consist in fictional, hypothetical, or idealized structures, which are used to represent a target system indirectly via some resemblance relation. We argue that recasting the Causal Theory as a model can not only (...) accomplish the goals of causal theorists, but also give the theory greater flexibility in responding to common objections. (shrink)

Modeling cuts across sundry scientific practices, contributing to theorizing, experimentation, prediction, measurement, scientific instrumentation, and science education. Beyond the sciences, modeling plays a crucial role in citizen engagement with science and public policy decision-making. It plays a major role in the efforts to address the huge challenges of the 21st century, including but not limited to climate change, shortage of natural resources, loss of biodiversity, and economic forecasting in increasingly unforeseeable situations. The diversity of scientific models is astounding; side-by-side mathematical (...) and scale models, technological advances such as rapidly expanding big data, computational and synthetic approaches, and generative AI are pushing modeling toward new frontiers, redefining the epistemic agency between humans and scientific instruments. A discussion of what we can achieve through modeling, and how we should manage model-based practices, is critical for ensuring a good and responsible use of this epistemic resource. The chapters of The Routledge Handbook of Philosophy of Scientific Modeling, written by experts in various areas of the philosophy of science, seek to provide enduring philosophical insights and useful analyses for understanding modeling in its multiplicity. -/- . (shrink)

Models are used to explore possibilities across all scientific fields. Climate models simulate the potential future climatic conditions under various emissions scenarios, macroeconomic models investigate the implications of various fiscal and monetary policy initiatives, and infectious diseases models study the spread of viral diseases under a range of conditions. Such modeling approaches have not gone ignored by philosophers of science, but they have only recently started to explicitly address modeling the possible. So far, the discussion has been spread across a (...) variety of more or less isolated pockets of debate in the philosophy of science. Modeling the Possible: Perspectives from Philosophy of Science draws together these studies, focusing specifically on how various modeling practices probe possibilities and justify claims concerning them. -/- The volume is divided into three sections, plus an introductory chapter. The introductory chapter provides a state-of-the-art survey of the discussions of modeling possibilities within the philosophy of science, as well as an introduction to the book’s main themes and individual papers. The three sections focus on different kinds of possibility concepts, possibility spaces, and how-possibly modeling in practical situations. The chapters contained in this volume address conceptual and theoretical issues while also presenting case studies from various scientific domains: physics, evolutionary and synthetic biology, network science, climate science, economics, and epidemiology. -/- Essential reading for philosophers of science, epistemologists, and modelers in various scientific disciplines, Modeling the Possible is also suitable for anyone interested in model-based scientific inferences, their validity, and the policy conclusions derived from them. (shrink)

У монографії поглиблено науково-методологічні підходи та запропоновано практичні рекомендації щодо удосконалення управління та застосування управлінського інструментарію підвищення ринкової капіталізації машинобудівних підприємств на основі вартісної концепції. Розкрито теоретичні основи управління ринковою капіталізацією підприємства на основі вартісної концепції. Здійснено оцінювання вартості машинобудівних підприємств України, аналіз формування ринкової капіталізації та оцінювання впливу зміни складових вартості досліджуваних підприємств на її цільове значення. Удосконалено механізм управління ринковою капіталізацією підприємств на основі вартісної концепції, оцінено вплив стимулюючих інструментів на підвищення ринкової капіталізації, запропоновано систему управлінських рішень з (...) оптимізації структури капіталу та визначено її вплив на зміну вартості та ринкової капіталізації машинобудівних підприємств. Видання розраховане на наукових працівників, викладачів ЗВО, керівників підприємств, а також усіх, хто має намір поглибити знання з управління бізнесом. (shrink)

Galaxy clusters are commonly used tracers of cosmology. Gravitational lensing analysis of the Bullet Cluster is claimed to evidentially support dark matter, an important component in the ${\rm{\Lambda }}$ CDM cosmology. I argue that such ${\rm{\Lambda }}$ CDM-based models of individual galaxy clusters should be explanatory to meet such claims, but hardly in an ontic sense, due to galaxy cluster anisotropies, empirically equivalent non- ${\rm{\Lambda }}$ CDM-based models, and currently unaccountable cases. I propose that adopting an alternative epistemic/representational conception of (...) scientific explanation can maintain the explanatory nature of individual galaxy cluster models, cope with the three complications, and be potentially generalizable to other branches of astrophysics. (shrink)

Several philosophers of science have drawn attention to a number of modeling practices where scientific models primarily contribute modal information. Examples now abound, and, recently, there have also been some preliminary attempts to address questions of under what conditions, and by virtue of what, models can perform this modal epistemic function. This paper sets out to constructively review those attempts through a prism of the more general literature on the epistemology of modality. One aim of this exercise is to expose (...) the underlying justificatory strategies of the modal modeling accounts – which are strikingly convergent with those already identified by modal epistemologists. Another aim is to highlight some challenges familiar to modal epistemology and discuss the form they take in the context of modal modeling as well as the resources that modal modeling accounts have for dealing with them. This is all done with an eye to pointing out ways forward in further developing the work on the epistemology of modal modeling. (shrink)

According to a widely held view, scientific modelling consists in entertaining a set of model descriptions that specify a model. Rather than studying the phenomenon of interest directly, scientists investigate the phenomenon indirectly via a model in the hope of learning about some of the phenomenon’s features. I call this view the description-driven modelling (DDM) account. I argue that although an accurate description of much of scientific research, the DDM account is found wanting as regards the mechanistic modelling found in (...) many branches of biology. By analysing research practices in cancer immunology concerning the development of mechanistic models of the process of cancer metastasis, this paper presents and argues for a complementary account of scientific modelling, herein called the experimentation-driven modelling (EDM) account. In EDM, scientists investigate a set of experimental systems and then integrate the results obtained from experiments into a mechanistic model. While EDM shares some key features with DDM, the two are epistemically very different approaches to research. (shrink)

Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides a selective and critical overview (...) of the available strategies for solving this puzzle, mainly focusing on idealized models and how they explain. (shrink)

The philosophical literatures on models and thought experiments have been developing exponentially, and independently, for decades. This independence is surprising, given how similar models and thought experiments are. They each have “lives of their own,” they sit between theory and experience, they are important for both pedagogy and cutting-edge science, they galvanize conceptual changes and paradigm shifts, and they involve entertaining imaginary scenarios and working out what happens. Recently, philosophers have begun to highlight these similarities. This entry aims at taking (...) the idea further, by trying to systematically identify places where insights from one literature can be taken up in the other. Along the way, important differences will also be highlighted. (shrink)

This chapter surveys key topics concerning the analysis of scientific understanding. It focuses on how scientific models challenge the views that scientific understanding is a form of knowledge and that it is tied to explanation. Scientists increase their understanding of phenomena with the help of heavily idealized scientific models and sometimes use these (typically non-propositional) models to explain those phenomena. The chapter also sheds light on other connections between models, explanations, and understanding, and it highlights how models showcase the importance (...) of abilities for understanding. (shrink)

This chapter concerns modal modeling practices: scientific modeling practices that are explicitly said to deliver, or should arguably be interpreted as delivering, support for modal conclusions. That includes, for instance, conclusions concerning possible causes, potential properties, and counterfactual histories. The chapter first outlines and gives examples of modal modeling practices and stresses the fact that such practices encompass a number of different kinds of modality, including both epistemic and objective modalities. It then describes three distinct but related sets of methodological (...) and epistemological issues raised by modal modeling and briefly reviews some possible ways to approach them. The chapter ends by highlighting some lacunae in the literature where further work is needed. (shrink)

Deep learning (DL) is a statistical technique for pattern classification through which AI researchers train artificial neural networks containing multiple layers that process massive amounts of data. I present a three-level account of explanation that can be reasonably expected from DL models in cognitive neuroscience and that illustrates the explanatory dynamics within a future-biased research program (Feest Philosophy of Science 84:1165–1176, 2017 ; Doerig et al. Nature Reviews: Neuroscience 24:431–450, 2023 ). By relying on the mechanistic framework (Craver Explaining the (...) brain: Mechanisms and the mosaic unity of neuroscience. Clarendon, 2007 ; Stinson Eppuor si muove: Doing history and philosophy of Science with Peter Machamer. Springer, 2017, The Routledge Handbook of the computational mind. Routledge, 2018 ), I develop an account that corresponds to the stages of mechanism discovery, i.e., our shifting epistemic position and epistemic goals, and propose a representative model for each level. Generic, theoretical DL models at _Level 1_ address the general features of a cognitive phenomenon through exploration and provide us with how-possibly explanations. On the other hand, DL models at _Level 2_ either identify the interaction between the features or represent the epistemic stage when the researcher is still unsure if the modeled features are crucial or arbitrary. These models should provide us with how-plausibly explanations. Finally, specific DL models of specific brain areas, i.e., ersatz models filled with relevant cognitive and neuroscientific details, are at _Level 3_. At this level, a researcher can advance how-actually explanations of cognitive phenomena. The main strength of this account is that it elucidates both _global explanatory dynamics_ and _local explanatory dynamics_ (cf. Dresow Synthese 199:10441–10474, 2021 ). The former occurs when the transition between levels happens in accordance with the process of obtaining more details about a particular cognitive phenomenon through multiple DL models. The latter, meanwhile, involves cases in which the transition between levels takes place within a single DL model by elucidating internal mechanisms (e.g., using Explainable AI techniques for rendering models more transparent). (shrink)

Maps and mapping raise questions about models and modeling and in science. This chapter archives map discourse in the founding generation of philosophers of science (e.g., Rudolf Carnap, Nelson Goodman, Thomas Kuhn, and Stephen Toulmin) and in the subsequent generation (e.g., Philip Kitcher, Helen Longino, and Bas van Fraassen). In focusing on these two original framing generations of philosophy of science, I intend to remove us from the heat of contemporary discussions of abstraction, representation, and practice of science and thereby (...) see in a more distant and neutral light the many productive ways in which maps can stand in analytically for scientific theories and models. The chapter concludes by complementing the map analogy – i.e., a scientific theory is a map of the world – with a model analogy, viz., a scientific model is a vehicle for understanding. (shrink)

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

Models are indispensable tools of scientific inquiry, and one of their main uses is to improve our understanding of the phenomena they represent. How do models accomplish this? And what does this tell us about the nature of understanding? While much recent work has aimed at answering these questions, philosophers' focus has been squarely on models in empirical science. I aim to show that pure mathematics also deserves a seat at the table. I begin by presenting two cases: Cramér’s random (...) model of the prime numbers and the function field model of the integers. These cases show that mathematicians, like empirical scientists, rely on unrealistic models to gain understanding of complex phenomena. They also have important implications for some much-discussed theses about scientific understanding. First, modeling practices in mathematics confirm that one can gain understanding without obtaining an explanation. Second, these cases undermine the popular thesis that unrealistic models confer understanding by imparting counterfactual knowledge. (shrink)

This paper will discuss the recent LIGO-Virgo observations of gravitational waves and the binary black hole mergers that produce them. These observations rely on having prior knowledge of the dynamical behaviour of binary black hole systems, as governed by the Einstein Field Equations (EFEs). However, we currently lack any exact, analytic solutions to the EFEs describing such systems. In the absence of such solutions, a range of modelling approaches are used to mediate between the dynamical equations and the experimental data. (...) Models based on post-Newtonian approximation, the effective one-body formalism, and numerical relativity simulations (and combinations of these) bridge the gap between theory and observations and make the LIGO-Virgo experiments possible. In particular, this paper will consider how such models are validated as accurate descriptions of real-world binary black hole mergers (and the resulting gravitational waves) in the face of an epistemic circularity problem: the validity of these models must be assumed to justify claims about gravitational wave sources, but this validity can only be established based on these same observations. (shrink)

Online identification is a common problem but so far resolved unsatisfactorily, as consumers cannot fully control how much data they share and with whom. Self-Sovereign Identity (SSI) technology promises to help by making use of decentralized data repositories as well as advanced cryptographic algorithms and protocols. This paper examines the effects of SSIs on responsible, confident, and vulnerable consumers in order to develop the missing understanding of consumer needs in SSI adoption and define preconditions and necessary considerations for the development (...) of SSI-based platforms and applications. (shrink)

Arguments for the effectiveness, and even the indispensability, of mathematics in scientific explanation rely on the claim that mathematics is an effective or even a necessary component in successful scientific predictions and explanations. Well-known accounts of successful mathematical explanation in physical science appeals to scientists’ ability to solve equations directly in key domains. But there are spectacular physical theories, including general relativity and fluid dynamics, in which the equations of the theory cannot be solved directly in target domains, and yet (...) scientists and mathematicians do effective work there (McLarty 2023, Elder 2023). Building on extant accounts of structural scientific explanation (Bokulich 2011, Leng 2021), I argue that philosophical accounts of the role of equations in scientific explanation need not rely on scientists’ ability to solve equations independently of their understanding of the empirical or experimental context. For instance, the process of formulating solutions to equations can involve significant appeal to information about experimental contexts (Curiel 2010) or of physically similar systems (Sterrett 2023). Working from a close analysis of work in fluid mechanics by Martin Bazant and Keith Moffatt (2005), I propose an account of heuristic structural explanation in mathematics (Einstein 1921, Pincock 2021), which explains how physical explanations can be constructed even in domains where basic equations cannot be solved directly. (shrink)

Model transfer is the scientific practice of taking a model which was initially applied in one particular kind of target system in some particular scientific domain and applying it to represent a novel target system in a novel scientific domain. This paper motivates a realist interpretation of empirically successful model transfers and the implications of such an interpretation for the metaphysics of science. The paper uses two examples of empirically successful model transfer, the first of which is a strikingly successful (...) recent application of the Ising model to neurobiology, the second of which is Maxwell’s “method of physical analogy”. The paper first defends the need for a realist picture regarding the transfer of model templates and the apparent discovery of physical analogies. Then, it examines further the implications of such a picture, finding it to be quite revisionary in its conception of the nature of causal processes. (shrink)

This paper challenges the conventional methodological tendencies of current monistic treatments of subjective consciousness (SC). I argue that it is highly unlikely that any one position will ‘solve’ the SC problem, as monism supposes. Instead, I argue for treating theories of SC akin to scientific models, that (like models) theories only apply under certain empirical conditions, where each simply explains a necessary aspect of SC. Hence, a pluralistic, rather than monistic, approach is preferable to the literature as a whole. In (...) lieu of conventional metaphysics, I advocate applying a form of scientific realism to models of SC, scientific perspectivism. As authors must rely on some intuitive and/or experiential description of what is problematic about SC, theories are better treated as models deriving from a plurality of interpretive perspectives. (shrink)

Letheby’s "Philosophy of Psychedelics" relies on Predictive Processing to try and find unifying explanations relevant to understanding how serotonergic psychedelics work in psychiatric therapy, what subjective experiences are associated with their use and whether such experiences are epistemically defective. But if Predictive Processing lacks genuinely explanatory unifying power, Letheby’s account of psychedelic therapy risks being unwarranted. In this commentary, I motivate this worry and sketch an alternative interpretation of psychedelic therapy within the Reinforcement Learning framework.

A well known conception of axiomatization has it that an axiomatized theory must be interpreted, or otherwise coordinated with reality, in order to acquire empirical content. An early version of this account is often ascribed to key figures in the logical empiricist movement, and to central figures in the early “formalist” tradition in mathematics as well. In this context, Reichenbach’s “coordinative definitions” are regarded as investing abstract propositions with empirical significance. We argue that over-emphasis on the abstract elements of this (...) approach fails to appreciate a rich tradition of empirical axiomatization in the late nineteenth and early twentieth centuries, evident in particular in the work of Moritz Pasch, Heinrich Hertz, David Hilbert, and Reichenbach himself. We claim that such over-emphasis leads to a misunderstanding of the role of empirical facts in Reichenbach’s approach to the axiomatization of a physical theory, and of the role of Reichenbach’s coordinative definitions in particular. (shrink)

This chapter gives an overview of the various themes and issues discussed in the volume. It includes summaries of all chapters and places the contributions, some of which are part of a critical conversation format, in the context of the larger literature and debates.