Scientific Models

Modeling is a core practice in science and a central part of scientific literacy. We present theoretical and empirical motivation for a learning progression for scientific modeling that aims to make the practice accessible and meaningful for learners. We define scientific modeling as including the elements of the practice (constructing, using, evaluating, and revising scientific models) and the metaknowledge that guides and motivates the practice (eg, understanding the nature and purpose of models). Our learning progression ...

Elgin has argued that scientific models that are, strictly speaking, inaccurate representations of the world, are epistemically valuable because the "falsehoods" they contain are "felicitous". Many, including Elgin herself, have interpreted this claim as offering an alternative to scientific realism and "veritism". In this paper, I will argue that there is a more felicitous interpretation of Elgin's work: "felicitous falsehoods" do play a role in the epistemic value of inaccurate models, but that role is of instrumental value. Elgin's view is not best understood as claiming that falsehoods provide scientific understanding in and of themselves, only that they facilitate epistemic access to the fundamental, even if partial, truths that are contained within models. While falsehoods may be felicitous in that they facilitate exemplification, the epistemic value of inaccurate models ultimately relies on their partial accuracy. Keywords Understanding • Idealization • Models • Factivity • Realism If truth is mandatory, much of our best science turns out to be epistemologically unacceptable and perhaps intellectually dishonest. Our predicament is this: We can retain the truth requirement and construe science either as cognitively defective or as noncognitive, or we can reject, revise, or relax the truth requirement and remain cognitivists about and devotees of science. I take it that science provides an understanding of the natural order. By this I do not mean merely that an ideal science would provide such an understanding or that at the end of inquiry science will provide one, but that much actual science has done so and continues to do so. I take it, then, that much actual science is cognitively reputable-indeed, estimable. The view according to which truth is a "requirement" is dubbed "veritism" by Elgin. She claims that her account of scientific understanding offers an alternative to veritism, such that modern science, including its falsehoods, can be construed as having epistemic value.

The present paper argues that 'mature mathematical formalisms' play a central role in achieving representation via scientific models. A close discussion of two contemporary accounts of how mathematical models apply -- the DDI account (according to which representation depends on the successful interplay of denotation, demonstration and interpretation) and the 'matching model' account -- reveals shortcomings of each, which, it is argued, suggests that scientific representation may be ineliminably heterogeneous in character. In order to achieve a degree of unification that is
compatible with successful representation, scientists often rely on the existence of a 'mature mathematical formalism', where the latter refers to a -- mathematically formulated and physically interpreted -- notational system of locally applicable rules that derive from (but need not be reducible to) fundamental theory. As mathematical formalisms undergo a process of elaboration, enrichment, and entrenchment, they come to embody theoretical, ontological, and methodological commitments and assumptions. Since these are enshrined in the formalism itself, they are no longer readily obvious to either the novice or the proficient user. At the same time as formalisms constrain what may be represented, they also function as inferential and interpretative resources.

If we were seeking consolation for the huge difficulties that have plagued us since the COVID-19 pandemic broke out, we might draw some comfort from the breadth and depth of the discussions that it has given rise to, for all that these have often led to the expression and propagation of misleading views. The present number of Argumenta opens with a Special Issue that intends to take stock of the tenor and quality of these discussions, bringing to light the awareness that we have been acquiring over the last few months about our overall cognitive situation. The Special Issue, edited by Margherita Benzi, Raffaella Campaner and Francesco Barone-Adesi, is entitled Modelling the Covid-19 Pandemic: Epidemiological, Epistemological, and Ethical Challenges, and draws attention to the intricacy of formulating models that take into account the many factors at work in a pandemic. The result is an up-to-date discussion of these aspects that, by focusing on epistemological and epidemiological views, seek to identify what epistemic virtues should guide the choice of models for explanatory and predictive purposes. The present number also includes three articles that have already appeared in 'early view' (by Fabio Bacchini and Nicola Piras, John Biro, and Anna Ichino and Juha Räikkä), and that have already made and will continue to make significant contributions to discussion in their respective fields.

The motivation to write a paper on the general nature of science comes from the scientific nature of Quran, which has been a guidance and help in revealing that science is an ontological and teleological construct which the Quran supports. A much-needed discussion of science had to be done because the trend among the people today suggest anything remotely sounding science has become a substituting value for religion and God. People have started believing rather blindly, in science without really understanding what it is, how it works and its limitations. What is science and what is the purpose of science? This paper explains the answers of the question and help reader understand the difference between science and pseudoscience which often people mistake as one. It explains the difference from example of macroevolution, single common ancestor and natural selection. The discussion also elaborates on the essential foundations of science that makes science, science. At the end, the paper elaborates why science cannot be used to ascertain moral truths. The discussion has been analytical in nature rooted in classic literature of philosophy of science and sociology. The readers will come to appreciate the fine principles of science and it’s limitations in revealing scientific truths.

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, in-tracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes.

This research project examines the use of Feynman diagrams in Quantum Field Theory (QFT) to substantiate a defence of a structuralist approach to scientific representation. I argue that a structuralist conception can uniquely account for the ubiquity of idealization in model-building, as well as provide a framework through which our best theories can make contact with reality. As such, the dissertation engages two distinct forms of philosophical inquiry which are identified in the literature; the methodological inquiry studies the functional utility of idealization in pragmatic terms, overlooking what our models say about the real world; whereas the metaphysical inquiry addresses how our idealized models can indeed 'latch onto reality' (Saatsi, 2012: 245). I suggest the structuralist view can accommodate both modes, which necessarily leads the narrative to position the argument within the realism debate. Accordingly, I defend 'ontic' structural realism (OSR), as originally conceived by Ladyman (1998), which uses recent advances in quantum physics to validate a metaphysical reconceptualization of the status of objects to establish the ontological primacy of structure (Ladyman, 1998: 420). The case study will show that we can develop a structural account of representation which can distinctively accommodate the indispensability of idealization in modelling, whilst also meshing well with the metaphysical demands of modern physics (French, 2014: 61 & 170, and Ladyman and Ross, 2007: 131).

Taking scientific practice as its starting point, this book charts the complex territory of models used in science. It examines what scientific models are and what their function is. Reliance on models is pervasive in science, and scientists often need to construct models in order to explain or predict anything of interest at all. The diversity of kinds of models one finds in science – ranging from toy models and scale models to theoretical and mathematical models – has attracted attention not only from scientists, but also from philosophers, sociologists, and historians of science. This has given rise to a wide variety of case studies that look at the different uses to which models have been put in specific scientific contexts. By exploring current debates on the use and building of models via cutting-edge examples drawn from physics and biology, the book provides broad insight into the methodology of modelling in the natural sciences. It pairs specific arguments with introductory material relating to the ontology and the function of models, and provides some historical context to the debates as well as a sketch of general positions in the philosophy of scientific models in the process.

The philosophy of one of the greatest physicists, Erwin Schrödinger, seems outdated and his spiritualist vocabulary almost incongruous. How can we understand however his aspiration to the unity of consciousness and the world, the unity of consciousnesses, and the capacity of the mind to act freely?
This angle is symbolic. Look for a balance or reciprocity between terms that refer to an abstract "spiritual" reality and to more concrete symbolic objects, which human consciousness has produced since the Upper Paleolithic: various images that are at the origin of scientific models and theories and "subjects" with the power to act: subject of knowledge (Schrödinger),  transcendental subject (Husserl, Descola), abstract ego (Von Neumann, Stapp) which are embodied in theoretical constructions and various experimental devices, freely invented. Schrödinger's symbolic consciousness was to elaborate world pictures (or universal pictures) and models, such as the famous equation that bears his name and delivers a wave image of matter and radiation or the model of the living and dead cat at the same time or the disturbing image of the identification of the mind with the image he conceived. He was inspired by literature, painting and music. His symbolic consciousness was analogistic as well as naturalistic – I refer here to the matrix of the four ontological regions of the anthropologist Philippe Descola. Naturalist in his aim for objectivity by means of equations, analogist in his use of general images of the world to find the presence of the mind in this quest for objectivity. But it was restrained by a mystical unity that he thought superior while it is before our eyes, in the symbolic environment elaborated during cultural evolution.
In this essay, I focus on two themes:
(1) To apply the "principle of objectification", the "Subject of Cognitanze" must necessarily exclude itself from the scientific image of the world it constructs, which contains only physical systems in causal interaction. But, made invisible, he is deprived of any effective power of action on the world. If the mind is detached from matter, how could it act on it? How to solve such an antinomy?
(2) Schrödinger's position on consciousness and free will remains implicit and unclear. Schrödinger thinks he can attribute some power to act to the human mind only by linking the unity of his personal consciousness (Atman) to the unity of an all-encompassing consciousness (Brahman) which, being itself the unity of the living and the non-living, has a legislative power over the non-living, a freedom to act from which the human mind inherits in a way by transitivity.
Do we have better than a vague transitivity between identities to justify talking about the capacity of man to act? Do we have better than a new antiphon of the totalizing or monotheistic vision that makes free will the gift of a transcendent unity, whether it is called Brahman or God?
The identity Atman = Brahman, sometimes called Schrödinger's second equation, is supposed to solve the antinomy of the invisibility of the mind: it can act on the world “because it is itself that world picture”. A mysterious image that this essay will strive to unravel and illuminate. For my part, I take this statement "literally", ruling out any mystical temptation: the mind is perfectly visible in images in the very broad sense of symbolic resources that it produces freely, some of them which have a universal scope and a particular causal effectiveness as real as natural causality. Schrödinger approaches such a solution by believing that determinism is a necessary auxiliary of free will that gives it its coherence and effectiveness, where partial natural determinisms interlock with the controlled determinism of motivations and reasons. The concept of "legislative subject" of itself is also an echo of William James' will to will, which triggers the symbolic work that is the medium of exercise of free will. Thus, Schrödinger's position is illuminated: a direct experience of this will and then a mediated experience that this free will cannot be exercised without symbolic resources. 
One can retort that such questions are only a distraction of a philosopher out of step with his time, the time of neuroscience in search of a scientific explanation of human consciousness that would solve once and for all the old problem of free will. I think, on the contrary, that these questions illuminate this "ultimate quest" in the sense that symbolic consciousness extends in space and time, far beyond the traces shown by brain imaging, even if these observations are crucial to analyze the neural correlates of the conscious contents that this consciousness builds and maintains active by means of external and public symbols. 
The outdated strangeness of free will is therefore only apparent if we look for it in only one place. It is only by embracing with the same gaze the conscious contents (internalized in the form of traces or neural signatures) and their sensitive correlates (externalized in symbolic forms), without establishing a hierarchy, that the exercise of free will becomes an obvious fact that I tried to translate into the form of a proof in another essay. Remembering Schrödinger's spiritualism has at least the merit of being able to see it as a headlong rush opposite to that of the "naturalization" of consciousness and free will by neuroscience.  For Schrödinger, it is a refuge in identities so encompassing that we no longer see anything at all, for neuroscience it is a refuge in identities so fragmentary that we will never see intentional action. Schrödinger's thought in action of wanting to "transfuse" some of the Eastern mind into the fundamental principles of Western science illustrates all the symbolic work necessary to accompany scientific naturalism. 
Here is the main conclusion of this essay:
The artist can choose whether or not to appear in his work (like Homer or Dürer). The subject of knowledge, the conscious mind, cannot be included in a scientific picture of the world in the narrow sense of boarding by means of physical or biological laws, but it occupies a central position and plays an active role if we include in this image the process of scientific action that acts in return on it. And, in this sense, the mind is indeed this image.
Such a conclusion only takes prominence in the history of figurative and scientific thought that may have been "troubled" by the necessary existence of a subject to implement it. Schrödinger was an emblematic case of this type of questioning.
The symbolic work produced by Schrödinger removes the antinomy. The subject of knowledge produces symbolic systems that have causal effectiveness through their technological extensions at the scale of scientific action, a power to act by means of symbolic causality on the scale of social reality. This symbolic work can be described as a semiodynamic process (in reference to Terrence Deacon's dynamic theory) of reciprocal adjustments of constraints between subjects, physical bodies and ways of composing worlds. A key concept is that of "epistemic cut" between matter and symbols, due to Howard Pattee (the precursors are the genetic code and afterwards the "neuronal inscriptions" in memories), which becomes a material-symbol complementarity when the symbolic detachment is reached by the symbolic consciousness. The theoretical, experimental and critical freedoms of the scientist has been achieved through a long struggle with reality since Sapiens designed the first symbolic tools in the Upper Paleolithic.
This essay is inspired mainly by What is Life? and Mind and Matter. I also rely on other texts such as Science and Humanism, Indeterminism and free will and the critical texts of Michel Bitbol which accompany them in their French versions. This is a new complement to my essay The Scientific Concept of Free Will - Emergence and Coevolution.
This adaptation of Schrödinger's thought is presented as a series of cycles where I dive into his texts, derive a perplexity, propose an outcome, find another perplexity similar to the previous one and resume a new cycle... This structure expresses more than an adjustment of two out of step symbolic freedoms, it expresses the symbolic reason that here espouses the form of Schrödinger's philosophical work, that of a haunting revival.

The ubiquitous presence of computer simulations in all kinds of research areas evidence their role as the new driving force for the advancement of science and engineering research. Nothing seems to escape the image of success that computer simulations project onto the research community and the general public. One simple way to illustrate this consists in asking ourselves how would contemporary science and engineering look like without the use of computer simulations. The answer would certainly diverge from the current image we have of scientific and engineering research.

This paper takes its motivation from investigations on the influence Kant’s reflections on meta-philosophy, more precisely, his discussions of the possibility  of developing a scientific method for philosophy. My broad claim is that Kant thereby sets the ground for specific conception of philosophical science, which can be traced throughout much of Post-Kantian philosophy. At the heart of this programme stands the idea that philosophy can only become a science by virtue of adopting a scientific method. In this paper, I argue that Solomon Maimon’s method of philosophical fictions presents one implementation of this research programme. Ensuing from his criticism of Kant’s First Critique, Maimon proposes an understanding of philosophy that treats its theoretical principles and concepts as scientific fictions and is science by virtue of employing this method of fictions. Only by virtue of producing what I call 'fictions of systematicity', that is, only by virtue of providing models to treat A as if it instantiated feature F (e.g. nature as if it were systematically ordered), does philosophy proceed in scientific manner. Philosophical fictions, so Maimon, should function as model-systems that are manipulated and investigated to get clear about the possibilities and limits of genuine scientific explanation to then instruct actual scientific practice from a normative standpoint.

nges and F ls SIONS IN Future Dire N SCIENC ections CE A C.I.P. record for this book is available from the Library of Congress. ISBN: 978-94-6091-822-3 (paperback) ISBN: 978-94-6091-823-0 (hardback) ISBN: 978-94-6091-824-7 (e-book) Published by: Sense Publishers,

The article addresses the genesis and visualization of the capstone image to Kepler's polyhedral hypothesis of the planetary intervals from his first major work, Mysterium Cosmographicum (1596). The contention is that the famous Tabula III was directed less by Kepler than it was an initiative spearheaded by Georg Gruppenbach, the printer of Mysterium, and Kepler's mentor Michael Mäistlin, who sought to produce a marketable broadsheet that would appeal to the contemporary German fashion for illustrations of polyhedral geometry. More generally, the article seeks to redefine the key role played by the printing workshop and the decorative arts in the theory's inception and ultimate graphic manifestation.

Aunque las partículas subatómicas son invisibles, los físicos las representan visualmente a fin de guiar la concepción social sobre cómo se integra y evoluciona el Universo. En este sentido, la imaginería científica —ya sea la que se utiliza entre expertos, o la que persigue fines divulgativos— es un medio para crear una ilusión referencial de realidad a través de la cual sus autores y observadores visualizan algo coherente, estipulan algo útil, e interpretan algo significativo sobre el
microcosmos imperceptible que configura al macrocosmos manifiesto.
Esta tesis argumenta que dicha noción tripartita de realidad puede ser
comprendida a través de la teoría de la significación del químico, matemático y filósofo estadounidense Charles Sanders Peirce (1839-1914), a la luz de diversas aportaciones y replanteamientos contemporáneos en semiótica, filosofía de la ciencia y hermenéutica.
A fin de sustentar lo anterior se realiza un análisis sintáctico, pragmático y semántico de una imagen divulgativa publicada en 2013 por el Experimento ATLAS (Aparato Toroidal del Gran Colisionador de Hadrones, por sus siglas en inglés) con motivo de la detección exitosa de una partícula subatómica que coincide con las propiedades de decaimiento de un bosón Higgs.

This paper reviews models to predict or postdict naked-eye visibility of celestial objects. Several models are discussed, such as observational-based models (like Kolev's and Schoch') and combined mathematical and observational models (like Inklaar's and Schaefer's). After some observational definitions, a detailed sensitivity analysis of the author's implementation of one combined (Schaefer's) model is provided, identifying the most critical celestial, geographic, atmospheric, and observer parameters. To benchmark Schaefer's model, comparisons with real observations and other models (such as de Jong, Kolev, Ptolemy, Schoch) is provided. The author concludes with a summary of his findings, including recommendations for future study areas.

Ron Giere’s recent book Scientific perspectivism sets out an account of science that attempts to forge a via media between two popular extremes: absolutist, objectivist realism on the one hand, and social constructivism or skeptical anti-realism on the other. The key for Giere is to treat both scientific observation and scientific theories as perspectives, which are limited, partial, contingent, context-, agent- and purpose-dependent, and pluralism-friendly, while nonetheless world-oriented and modestly realist. Giere’s perspectivism bears significant similarity to earlier ideas of Paul Feyerabend and John Dewey. Comparing these to Giere’s work not only uncovers a consilience of ideas, but also can help to fill out Giere’s account in places where it is not fully developed, as well as helping us understand the work of these earlier authors and their continuing relevance to contemporary concerns in philosophy of science.

Keywords: Ronald Giere; Paul Feyerabend; John Dewey; Perspectivism; Pragmatism; Pluralism

(PhD Thesis, 2016)

This study attempts to counter a specific brand of scientific realism: the not-much-explicated metaphysical stance of the proponents of mechanismic philosophy of science (the mechanismic philosophers, better known as “the new mechanistic philosophers”). These philosophers aim to put philosophical flesh on scientific mechanism-talk that is widely observed in life sciences and some other high-level (non-fundamental) domains of scientific inquiry. They advance a particular
understanding of scientific explanation based on mechanism-talk, asserting that the explanation of high-level phenomena by describing the mechanisms underlying them is a non-lawful and non-reductive practice. On their line of argumentation, such practice sets high-level sciences apart from fundamental physics, and it accordingly demands a philosophical treatment that is different than the canonical approaches to scientific explanation. Most of the charges brought against mechanismic philosophy of science are concerned with its
proponents' overestimation of mechanistic explanation. In the final analysis, I intend to be siding with these philosophers challenging the mechanismic philosophers. However the line of attack that I
pursue here is different from theirs (the former) for not focusing on the supposed non-lawful and non-reductive character of mechanistic explanation. Instead I am concerned with the metaphysical
foundations of mechanistic explanation, and the implications of the mechanismic philosophers' assessment of mechanistic explanation. In particular I argue against the ontological stance with regards to mechanisms construed as causally organized token occurrences which are found relevant to the investigated phenomenon. I claim that, as opposed to being the subjects of models, mechanisms themselves are the models of real systems. This position, which I label as
“mechanismic non-realism”, can be shown to be compatible with scientific antirealism. Nevertheless I purposefully avoid this, since I am a scientific realist. Hence the challenge for me is accounting for what is out there that mechanistic explanations are about. I suggest that dispositional realism is a viable position to adopt in this context. On the metaphysical approach I defend, mechanisms represent the repeatable patterns in the causal structure, underlain by the causal properties conferring dispositions on their bearers to behave in certain ways. I develop a meta-ontological argument to show that causal properties enjoy an ontological priority over mechanisms.

Representational views of the mind traditionally face a skeptical challenge on perceptual knowledge: if our experience of the world is mediated by representations built upon perceptual inputs, how can we be certain that our representations are accurate and our perceptual apparatus reliable? J. J. Gibson's ecological approach provides an alternative framework, according to which direct perception of affordances does away with the need to posit internal mental representations as intermediary steps between perceptual input and behavioral output. Gibson accordingly spoke of his framework as providing " reasons for realism. " In this paper I suggest that, granting Gibson his reasons for perceptual realism, the Gibsonian framework motivates anti-realism when it comes to scientific theorizing and modeling. If scientists are Gibsonian perceivers, then it makes sense to take their use of models in indirect investigations of real-world phenomena not as representations of the phenomena, but rather as autonomous tools with their own affordances.

The term 'scientific model' picks out a great many things: scale models, physical models, sets of mathematical equations, theoretical models, toy models, and so forth. This raises the question of whether a general answer to the question 'What is a model?' is even possible. The present chapter surveys a number of philosophical approaches that bear on the question of what, in general, a scientific model is. While some approaches aim for a unitary account that would apply to models in general, regardless of their specific features, others take as their basic starting point the manifest heterogeneity of models in scientific practice. The chapter first motivates the ontological question of what models are by reflecting on the diversity of different kinds of models and arguing that models are best understood as 'functional entities'. It then provides some historical background regarding the use of analogy in science as a precursor to contemporary notions of 'scientific model'. This is followed by a contrast between the syntactic and the semantic views of theories and models and their different stances towards the question of what a model is. Scientists, too, typically operate with tacit assumptions about the ontological status of models: this gives rise to what has been called the 'folk ontology' of models, according to which models may be thought of as descriptions of missing (i.e., uninstantiated) systems. There is a close affinity between this view and recent philosophical positions (to be discussed in the penultimate section) according to which models are fictions. The chapter concludes by considering various pragmatic conceptions of models, which are typically associated with what may be called 'mixed ontologies' – that is, with the view that any quest for a unitary account of the nature of models is bound to be fruitless.

With the war in Ukraine raging, my book entitled Comprehending the Complexity of Countries: The Way Ahead has been timely published by Springer. The title is deliberately chosen, for the book, dealing with a theme that is more general than, but certainly not excluding, Sinology (the study in which I am holding a Master's from Leiden University), challenges both defenders and detractors of globalization For details, visit

Does the materiality of a three-dimensional model have an effect on how this model operates in an exploratory way, how it prompts discovery of new mathematical results? Material mathematical models were produced and used during the second half of the nineteenth century, visualizing mathematical objects, such as curves and surfaces-and these were produced from a variety of materials: paper, cardboard, plaster, strings, wood. However, the question, whether their materiality influenced the status of these models-considered as exploratory, technical, or representational-was hardly touched upon. This article aims to approach this question by investigating two case studies: Beltrami's paper models vs. Dyck's plaster ones of the hyperbolic plane; and Chisini's string models of braids vs. Artin's and Moishezon's algebraization of these braids. These two case studies indicate that materiality might have a decisive role in how the model was taken into account mathematically: either as an exploratory or rather as a technical or pedagogical object.

ABSTRACT
The purpose of the current study was to explore learners’ evaluation of the credibility of scientific models that represent natural entities and phenomena. Participants were 845 students in grades 9 – 11 (aged 15 – 17 years) and 108 prospective science teachers in Oman, totaling 953 students. A survey called Epistemologies about the Credibility of Scientific Models was designed to explore participants’ epistemological positions regarding the credibility of scientific models. This instrument was based on a credibility taxonomy proposed by the author. This taxonomy was composed of four epistemological levels: certainty, imaginary, suspicious, and denial; thus, it was called the CISD taxonomy. Findings revealed that natural entities and phenomena were assigned to CISD levels according to their level of abstractness. This level of abstractness is usually constructed by the most frequently used models to represent each natural entity or phenomenon. For instance, entities, which were usually represented by photographs or micrographs, such as meteors and meteorites, blood cells and bacteria, fell at the certainty level. On the other hand, theoretical entities such as electron cloud and photons had a high suspicious–denial combinational level. Some entities, with possible competing concrete–abstract parallel nature of the scientific models that represent them, had both high certainty and suspicious–denial levels. The overall students’ epistemological perceptions across grade levels showed a decrease in the certainty level and an increase in the imaginary level. It might also be plausible to conclude that new, detailed microscopic and more abstract knowledge raised the suspicious and denial levels of some entities. Further research based on qualitative research methodologies is needed to explore these findings.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

""This paper examines the role of machine metaphors in the natural philosophy and metaphysics of René Descartes (1596-1650). Descartes was a key figure in the close alliance of systematic reason and technological practice that characterizes the technoscientific turn of the seventeenth century. I argue that for Descartes the machine was a metaphysical thesis and a heuristic device, and that these two aspects are closely interdependent in his philosophy.

Descartes’ contribution was to ground the machine on an ontological and epistemological basis, exploring, among other things, the poetic and conceptual possibilities of a mechanical theory of life. The machine is, on one hand, a true expression of how the world really is, as well as the conceptual foundation for our understanding of this same world. I focus, then, on these two main aspects: how technical artifacts and processes enter Descartes’ philosophy of nature, and the role of analogy in his scientific method.

I begin by situating Descartes in his historical, conceptual and technological milieu, pinpointing the material and cultural sources of natural-philosophical explanation. I move on to a broad outline of Descartes’ physics, which has a markedly metaphysical character. Then, to the heuristic machinery of epistemology: the apparatus of perception and how mechanics serves as the basis for clear and distinct knowledge. I argue that, for Descartes, science is about the creation of intermediary hypotheses, and situate this approach in the context of the Cartesian view of human knowledge in the larger scheme of things (i.e., the theological framework for Descartes’ philosophy of science and technology). In turn, Descartes’ perspective is informed by a ‘semiotics’ of non-resemblance (in which our impressions do not ‘resemble’ their referent or source). I then examine how the machine becomes the ‘master’ metaphor, constitutive and productive of knowledge.

I hope this paper is a contribution to the historical understanding of the scientific revolution—in particular, as establishing one of the most influential paths through which technology and science entered in dialogue, and became productive of each other. More generally, it is a historical case study on the role of metaphors in scientific knowledge.
""

This paper investigates one of the great achievements of 20th-century cell biology: determining the structure of the cell membrane. This case differs in important ways from the better-known case of the identification of the DNA double helix as the carrier of genetic information, especially regarding the evaluation of potential evidence in light of prior theoretical commitments. Whereas it has been argued that adherence to a structural hypothesis enabled Watson and Crick to ignore a surplus of (potentially confusing) empirical findings, similar adherence to an elegant and universal structural hypothesis, we argue, unduly shielded the so-called 'unit-membrane' model from legitimate challenges on the basis of known phenomena.

W. R. Ashby is known as a central figure of the mid-twentieth century cybernetics movement. Here his historical significance concerning the following development of the cognitive and behavioral sciences is assessed. Specifically, the paper focuses on the epistemological and methodological assumptions and implications behind the homeostat, a famous cybernetic device presented by Ashby during the 1952 Macy conference in order to study adaptive behavior. On the one hand, the contact points between the behavioral tradition in psychology and the theoretical and programmatic statement underlying the homeostat are considered: Over and above Ashby’s strong behaviorist roots, it will be shown how the homeostat also clearly departs from behaviorism. On the other hand, the guidelines of a solid philosophy of scientific modeling stemming from Ashby’s early contribution are highlighted.

Rispetto a quanto presentato ne "La Struttura delle Rivoluzioni Scientifiche", la definizione che Thomas Kuhn propone nel Poscritto del '69 in sostituzione del termine "paradigma" è quella di "esemplari" [exemplars]. Il tentativo del testo proposto è quello di sottolineare il valore che Kuhn dà alla pratica scientifica nell'acquisizione di quella che - utilizzando le parole di Michael Polanyi - il filosofo definisce la "tacita conoscenza" antecedente la possibilità di imparare e comprendere regole e leggi sul mondo. Questa attività pratica sarebbe per Kuhn la culla delle soluzioni concrete dei rompicapi con le quali lo studente impara a riconoscere e a decifrare situazioni e fenomeni simili o dissimili fra loro (in linea con l'aspettativa che deriverebbe dalla struttura della Gestalt rispecchiante la scienza normale all'interno della quale egli stesso opera). Per Kuhn la riproposizione di fenomeni particolari associati a determinate leggi o regole rappresenterebbe il modo migliore di educare lo studente a costruire la propria concezione del mondo su una struttura coerente a quella proposta dal paradigma normale. L'assimilazione del principio secondo il quale una legge sia o meno in grado di spiegare i perché di determinate tipologie di fenomeni simili sarebbe data proprio da questa educazione esemplare.

According to one influential view, model-building in science is primarily a matter of simplifying theoretical descriptions of real-world target systems using abstraction and idealization. This view, however, does not ade­ quately capture all types of models. Many contemporary models in the natural and social sciences-from physics to biology to economics-stand in a more tenuous relationship with real-world target systems and have a decidedly stip­ ulative element, in that they create, by fiat, 'model worlds' that operate according to some set of specified rules. While such models may be motivated by an interest in actual target phenomena, their validity is not-at least not primarily-to be judged by whether they constitute an empirically adequate representation of any particular empirical system. The present paper compares and contrasts three such types of models: minimal models, toy models, and exploratory models. All three share some characteristics and thus overlap in interesting ways, yet they also exhibit significant differences. lt is argued that, in all three cases, modal considerations have an important role to pl ay : by exploring the modal structure of theories and phenomena-that is, by probing possibilities in various ways-such models deepen our understanding and help us gain knowledge not only about what there is in the world, but also about what there could be.

This chapter discusses a variety of closely related uses of models which are best described collectively as ‘exploratory modeling’. The importance of exploration to science has recently been emphasized by a number of historians and philosophers of science writing on scientific experimentation, and the chapter begins by reviewing this lively debate. Following a clarification of the meaning of ‘exploration’ by distinguishing between a ‘convergent’ and a ‘divergent’ sense, the concept is then applied to the case of scientific models. In particular, four different functions of exploratory models are distinguished: they may function as a starting point for future inquiry, feature in proof-of-principle demonstrations, generate potential explanations of observed (types of) phenomena, and may lead to reassessments of the suitability of the target. These functions are neither mutually exclusive, nor are they thought to exhaust the spectrum of possible exploratory uses to which models may be put. Examples for each of the four types of exploratory uses are provided and range from models in sociodynamics (traffic flow models) to proposed mechanisms of molecular rearrangements in physical organic chemistry. The chapter closes with a discussion of the prospects and limitations of exploratory modeling and concludes that exploration deserves a place alongside explanation and prediction as one of the core functions of scientific modeling.

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation.

Recent debates concern the question of whether topological or “network” explanations are a species of mechanistic explanation. In this paper, we provide a more principled framework for the purposes of advancing these discussions. Our innovations on this front are threefold. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations, and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. We conclude from this that topological explanations are autonomous from their mechanistic counterparts.

This article provides a state of the art review of the philosophical literature on scientific representation. It first argues that the topic emerges historically mainly out of what may be called the modelling tradition. It then introduces a number of helpful analytical distinctions, and goes on to divide contemporary approaches to scientific representation into two distinct kinds, substantive and deflationary. Analogies with related discussions of artistic representation in aesthetics, and of the nature of truth in metaphysics are pursued. It is finally urged that the most promising approaches - and the ones most likely to feature prominently in future developments - are deflationary. In particular, a defence is provided of a genuinely inferential conception of representation.

Sağlamlık analizi (robustness analysis), çok sayıda birbirinden bağımsız prosedür aracılığıyla aynı sonucun elde edilmeye çalışıldığı epistemik bir stratejidir. Söz konusu strateji bilim pratiğinde açıklama verme ve öndeyi türetiminde başvurulan modellerde sıklıkla kullanılır. Bilimsel modellerin ne derece sağlam ve hassas olduğunun belirlenmesi ile ilgili bir yöntem olduğu için sağlamlık analizi hem bilim insanları hem de bilim felsefecileri açısından epistemik bir öneme sahiptir. Bu makalede öncelikle, sağlamlık analizleri tarihsel çerçevede ele alınmaktadır. Ardından literatürde yer alan farklı sağlamlık analizi sınıflandırmaları ve sağlamlık analizlerinin işlevleri tartışılıp, modeller sağlamlık analizleri ve model temelli açıklamalar arasındaki ilişki ile ilgili bir ayrım yapılmaktadır. Bu ayrım modeller hakkında akıl yürütme ile modeller ile akıl yürütme arasındaki farklılıklar ile ilgilidir. Bu ayrıma göre
bilimsel açıklamalarda kullanılan modellerin doğruluğunun koşullarının belirlenmesi sağlamlık analizleri olarak değerlendirilirken, modellerin uygulanabilirliği modeller ile akıl yürütme ile ilgilidir..

En este trabajo me ocupo de un viejo tema: el de la carga valorativa del conocimiento científico. A lo largo de la historia diversos autores, como Max Weber y el Wittgenstein del Tractatus, han defendido un ideal, el de la “neutralidad valorativa” de la ciencia, que muchos han considerado un mito. Una estrategia reciente para poner en cuestión este mito, en lo tocante a las teorías científicas, lo proporciona el “realismo constructivo” de Ronald Giere y su original concepción de los modelos de la ciencia. Pues, partiendo de esa concepción de los modelos, Giere reconoce el papel que muy diversos valores e intereses, entre ellos valores e intereses no epistémicos, juegan en la construcción de la ciencia. Sin embargo, voy a defender (utilizando la analogía, que el propio Giere establece, entre los modelos científicos y los mapas) que el planteamiento de este autor acarrea algunas consecuencias indeseables, como una excesiva descarga normativa de la filosofía de la ciencia y una peligrosa desprotección frente al relativismo. Finalmente, propondré una corrección de la propuesta de Giere que evite dichos problemas.

The volume addresses a problem rarely discussed by philosophers - the question of provincialism in science (in the broadest sense of the term). There are only a few great centers of science, which attract funding and provide almost ideal opportunities for research and development. They also attract some of the best researchers. Some - but not all. For a variety of reasons, some of the best researchers, or ones who have that potential, may do science outside these centers, in the provinces. The volume is devoted to the problems they face. What is an intellectual province? Who are the provincial thinkers? What is the mark of provincialism? Do provincial (or central) thinkers have any special duties? Are there ways of overcoming one's own provincialism? The authors address these questions across different disciplines, cultures, locations and time periods.

Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as a tool for reassessing the suitability of the target system (and sometimes of whole research agendas). This is illustrated by a case study of the varied career of reaction-diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing's initial model, it is argued, is best explained by recognizing it as an exploratory tool (rather than as a purported representation of an actual target system).

This essay presents a model-theoretic account of dispositional properties, according to which dispositions are not ordinary properties of real entities; dispositions capture the behavior of abstract, idealized models. This account has several payoffs. First, it saves the simple conditional analysis of dispositions. Second, it preserves the general
connection between dispositions and regularities, despite the fact that some dispositions are not grounded in actual regularities. Finally, it brings together the analysis
and the explanation of dispositions under a unied framework.

The goal of any science and engineering education is to give the student the ability to “perform Reduction”. Some of you may not be familiar with this term, but you have all done it. It is the most commonly used process in science and engineering and we tacitly assume we will use it at every opportunity. Therefore there has been little need to discuss Reduction as a topic outside of epistemology and philosophy of science.

I will be making the claim that for the limited purpose of creating an Artificial General Intelligence (AGI) we must avoid this common kind of Reduction. This article (second in a series; see the first article here) will discuss what Reduction is and why it is useless in the domains where AGI is expected to operate. The third article will discuss why it is also unnecessary. The fourth article will discuss available alternatives. As a bonus, we will come to Understand what it means to Understand something.