Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different (...) epistemic characters. Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion. (shrink)
Understanding of the λ model has greatly increased in recent years as evidenced by most of the commentaries. Some commentators underscored the potential of the model to integrate aspects of different sensorimotor systems in the production of movement. Other commentators focused on not-yet-fully-developed parts of the model. A few persisted in misunderstanding some of its basic concepts, and on these grounds they reject it. In responding to commentaries we continue to elaborate on some fundamental points of the model, especially control (...) variables, the idea of movement production by shifting the positional frame of reference and the hypothesis of biomechanical correspondence in motor control. We also continue to develop our ideas on the intrinsic generation of the frame of reference associated with external space and utilized for the control of arm movement and locomotion. The dynamic principles underlying the model are discussed in light of the dynamical systems approach. (shrink)
The paper explores a metaphysics of information enriched by a computational view of Buddhism consistent with onto-ethics. To the extent that Floridi has explained the new philosophy of information as borrowing methods from computer science to approach philosophical problems computationally, I believe an applied philosophy of information can return the fruits of these results back to grounding issues in the practices of information technology. With this process we also foster a cross-fertilization between Eastern and Western philosophies, in the larger, intercultural (...) arena. (shrink)
The study deals with the communicative interaction between the author, the hero, the text, the reader in a postmodern novel. A similar and ambiguous reality, on the one hand, sometimes led to the subjectivist hypertrophy, absolutizing the author’s world view, and at times minimized and devaluated the author’s identity, on the other. Therefore, from the end of the 1990s the ways of expressing author’s “Self” changed dramatically, which directly affected the means of creating a hero in the contemporary Ukrainian literature. (...) An important place in the communicative literary model was occupied by the text as an independent semantic unit and the reader as an interpreter of the text. The specifics of deploying the dialog between the author and the hero point to the transformation of their functions in the Ukrainian postmodern novel. Considering the statement of the death of the author proclaimed by R. Barthes, the former stops being the main holistic text creator, thus rather becoming its product and the way of expression. The author, the hero and the text have a certain integrity aimed at the interpretative game with the recipient, who diffuses the newly created semantic integrity into a diversity of meanings. (shrink)
Many have expected that understanding the evolution of norms should, in some way, bear on our first-order normative outlook: How norms evolve should shape which norms we accept. But recent philosophy has not done much to shore up this expectation. Most existing discussions of evolution and norms either jump headlong into the is/ought gap or else target meta-ethical issues, such as the objectivity of norms. My aim in this paper is to sketch a different way in which evolutionary considerations can (...) feed into normative thinking—focusing on stability. I will discuss two forms of argument that utilize information about social stability drawn from evolutionary models, and employs it to assess claims in political philosophy. One such argument treats stability as feature of social states that may be taken into account alongside other features. The other uses stability as a constraint on the realization of social ideals, via a version of the ought-implies-can maxim. These forms of argument are not new; indeed they have a history going back at least to early modern philosophy. But their marriage with evolutionary information is relatively recent, has a significantly novel character, and has received little attention in recent moral and political philosophy. (shrink)
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)
Mathematical models provide explanations of limited power of specific aspects of phenomena. One way of articulating their limits here, without denying their essential powers, is in terms of contrastive explanation.
Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles of (...) counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)
Animal models have long been used to investigate human mental disorders, including depression, anxiety, and schizophrenia. This practice is usually justified in terms of the benefits (to humans) outweighing the costs (to the animals). I argue on utility maximization grounds that we should phase out animal models in neuropsychiatric research. Our leading theories of how human minds and behavior evolved invoke sociocultural factors whose relation to nonhuman minds, societies, and behavior has not been homologized. Thus it is not (...) at all clear that we are gaining the epistemic or clinical benefits we want from this animal-based research. (shrink)
It is shown that the classes of Routley-Meyer models which are axiomatizable by a theory in a propositional relevant language with fusion and the Ackermann constant can be characterized by their closure under certain model-theoretic operations involving prime filter extensions, relevant directed bisimulations and disjoint unions.
Some philosophers of science – the present author included – appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction-making is essentially non-truth-regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the paper is to describe these two packages. Toward the end I comment on how to (...) choose among them. (shrink)
Educational drama or drama-in-education, also known as process drama, is intrinsically an inclusive, mosaic pedagogy that utilizes various dramatic techniques and exercises as educational tools in schools. The goal of this teaching discipline is to engender a holistic and experiential learning that creates meaning and enhances self-expression and personal growth, leading the students to a better understanding of the complexity of human behavior, while also containing the “others” and their viewpoints.1Under the umbrella of D.I.E. there exists a range of different (...) and heterogeneous concepts, objectives, and practices. The contest between drama and theater, process and product, content and free expression... (shrink)
Molecular models are typical topics of chemical research depending on the technical standards of observation, computation, and representation. Mathematically, molecular structures have been represented by means of graph theory, topology, differential equations, and numerical procedures. With the increasing capabilities of computer networks, computational models and computer-assisted visualization become an essential part of chemical research. Object-oriented programming languages create a virtual reality of chemical structures opening new avenues of exploration and collaboration in chemistry. From an epistemic point of view, (...) virtual reality is a new computer-assisted tool of human imagination and recognition. (shrink)
The word “model” is highly ambiguous, and there is no uniform terminology used by either scientists or philosophers. Here, a model is considered to be a representation of some object, behavior, or system that one wants to understand. This article presents the most common type of models found in science as well as the different relations—traditionally called “analogies”—between models and between a given model and its subject. Although once considered merely heuristic devices, they are now seen as indispensable (...) to modern science. There are many different types of models used across the scientific disciplines, although there is no uniform terminology to classify them. The most familiar are physical models such as scale replicas of bridges or airplanes. These, like all models, are used because of their “analogies” to the subjects of the models. A scale model airplane has a structural similarity or “material analogy” to the full scale version. This correspondence allows engineers to infer dynamic properties of the airplane based on wind tunnel experiments on the replica. Physical models also include abstract representations which often include idealizations such as frictionless planes and point masses. Another, but completely different type of model, is constituted by sets of equations. These mathematical models were not always deemed legitimate models by philosophers. Model-to-subject and model-to-model relations are described using several different types of analogies: positive, negative, neutral, material, and formal. (shrink)
My model-theoretic realist account of science places linguistic systems and the corresponding non-linguistic structures at different stages of the scientific process. It is shown that science and its progress cannot be analysed in terms of only one of these strata. Philosophy of science literature offers mainly two approaches; to the structure of scientific knowledge analysed in terms of theories and their models, the "statement" and the "non-statement" approaches. In opposition to the statement approach's belief that scientific knowledge is embodied (...) in theories symbolic language) with direct interpretative links---via so-called "bridge principles"---to reality, the defenders of the non-statement approach believe in an analysis where the language in which the theory is formulated plays a much smaller role than the structures which satisfy that theory. ;The model-theoretic realism expounded here retains the notion of a scientific theory as a set of sentences, while simultaneously emphasising the interpretative role of the conceptual models of these theories. My criticism against the non-statement approach is based on the fact that merely "giving" the theory "in terms of" its mathematical structures leaves out any real interpretation of the nature and role of general terms in science. Against the statement approach's "direct" linking of general theoretical terms to reality, my approach interpolates models between theories and reality in the interpretative chain. ;The links between the general terms of scientific theories and their interpretations in the various models of the theory regulate the whole referential process. The terms of a theory are "general" in the sense that they are the result of certain abstractive conceptualisations of the object of scientific investigation and subsequent linguistic formulations of these conceptualisations. Their meanings can be "given back" only by interpreting them in the limited context, of the various conceptual models of their theory and, finally, by finding an isomorphic relation between some substructure of the conceptual model in question and some empirical conceptualisation of relevant experimental data. In this sense the notion of scientific "truth" becomes inextricably linked with that of articulated reference, as it---given its model-dependent nature---should be. (shrink)
There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term ‘model’ comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. The book’s contributors are researchers active in the area of creative reasoning in science and technology.
Standard linguistic analysis of syntax uses the T-model. This model requires the ordering: D-structure > S-structure > LF, where D-structure is the sentences deep structure, S-structure is its surface structure, and LF is its logical form. Between each of these representations there is movement which alters the order of the constituent words; movement is achieved using the principles and parameters of syntactic theory. Psychological analysis of sentence production is usually either serial or connectionist. Psychological serial models do not accommodate (...) the T-model immediately so that here a new model called the P-model is introduced. The P-model is different from previous linguistic and psychological models. Here it is argued that the LF representation should be replaced by a variant of Frege'sA three qualities (sense, reference, and force), called the FregeA representation or F-representation. In the F-representation the order of elements is not necessarily the same as that in LF and it is suggested that the correct ordering is: F-representation > D-structure > S-structure. This ordering appears to lead to a more natural view of sentence production and processing. Within this framework movement originates as the outcome of emphasis applied to the sentence. The requirement that the F-representation precedes the D-structure needs a picture of the particular principles and parameters which pertain to movement of words between representations. In general this would imply that there is a preferred or optimal ordering of the symbolic string in the F-representation. The standard ordering is retained because the general way of producing such an optimal ordering is unclear. In this case it is possible to produce an analysis of movement between LF and D-structure similar to the usual analysis of movement between S-structure and LF. The necessity of analyzing corrupted data suggests that a maximal amount of information about a language's grammar and lexicon is stored. (shrink)
I am arguing that it is only by concentrating on the role of models in theory construction, interpretation and change, that one can study the progress of science sensibly. I define the level at which these models operate as a level above the purely empirical (consisting of various systems in reality) but also indeed below that of the fundamental formal theories (expressed linguistically). The essentially multi-interpretability of the theory at the general, abstract linguistic level, implies that it can (...) potentially make claims about systems in reality, other than the particular one which originally induced it. Any so-called correspondence relation between (systems in) reality and the entities and relations in some scientific theory, thus consists of two jumps or interpretations: from the theory (linguistic level) to some model of it (constructural level); and from there to some system in reality. Clearly then the level of fundamental theories cannot be ignored la Nancy Cartwright - in studying the relations between a theory and reality, because the particular features of the theory (the various systems in reality onto which the theory can be mapped) cannot be studied without the underlying knowledge that these systems have one common feature, namely that each of them is the range (or other pole) of a mapping of a context-specific model of the theory - which in itself, is a mapping, or more specifically, an interpretation of the theory. I am also claiming that the nature of these levels and the relations between them necessitate an epistemological rather than an ontological notion of truth criteria, and a referential rather than a representational link between science and reality. (shrink)
The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...) a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)
Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: (...) Can the fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory "how-possibly" model explanation from a genuine "how-actually" model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions. (shrink)
Although predictive power and explanatory insight are both desiderata of scientific models, these features are often in tension with each other and cannot be simultaneously maximized. In such situations, scientists may adopt what I term a ‘division of cognitive labor’ among models, using different models for the purposes of explanation and prediction, respectively, even for the exact same phenomenon being investigated. Adopting this strategy raises a number of issues, however, which have received inadequate philosophical attention. More specifically, (...) while one implication may be that it is inappropriate to judge explanatory models by the same standards of quantitative accuracy as predictive models, there still needs to be some way of either confirming or rejecting these model explanations. Here I argue that robustness analyses have a central role to play in testing highly idealized explanatory models. I illustrate these points with two examples of explanatory models from the field of geomorphology. (shrink)
In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While (...) idealization and approximation is key to the making of models for the observable systems, they are in fact inoperable, at least not straightforwardly so, regarding models for the unobservable. And because of this point, which is so far neglected in the literature, I speculate that factionalism may have a better chance with models for the unobservable. (shrink)
Avec l’évolution récente des modèles mathématiques vers des simulations informatiques, les formalisations du vivant sont de plus en plus intégratives, mixtes et, en un sens, réalistes. Plus généralement, les formalisations d’objets complexes deviennent assises sur et non plus seulement traitées par l’infrastructure informatique.Quelle est la véritable portée épistémologique de cette empirie simulée? Comment la distinguer de la créativité proprement interne aux mathématiques dont la philosophie des sciences a déjà su rendre compte?En se penchant sur les modèles de plantes, cette enquête (...) historique et épistémologique montre comment une telle évolution bouleverse les épistémologies contemporaines des formalisations et des modèles en renouvelant d’une part la question des rapports entre mathématiques, calcul, langage informatique et réplication, et d’autre part la question de l’intégration, dans un objet formel commun, de savoirs disciplinaires distincts. (shrink)
This paper constitutes a radical departure from the existing philosophical literature on models, modeling-practices, and model-based science. I argue that the various entities and practices called 'models' and 'modeling-practices' are too diverse, too context-sensitive, and serve too many scientific purposes and roles, as to allow for a general philosophical analysis. From this recognition an alternative view emerges that I shall dub model anarchism.
This chapter distinguishes two different modeling relations between vehicles and targets: design relation and representation relation. The relations are characterized by their different directions of fit. Three examples of modeling enterprises are discussed: a bioengineering model, called the “lung chip,” an architectural model, called the “weekend cottage,” and an engineering design model, called the “jet engine.” The two modeling relations with different directions of fit are analyzed in the three examples. The lung chip is standing in a representation relation to (...) its corresponding target and the modeling of the cottage involves a design relation to its target. With the help of these examples a basic assumption of philosophy of engineering and philosophy of technology is challenged. These examples show that it is not strictly speaking true that engineering modeling is exclusively about how things should be rather than about how things are. It is shown that a representation relation is prominently involved in the first model of the lung chip. This modeling involves reasoning about how things are rather than about how things should be. So, one modeling enterprise seems to be rather about what is than about what should be. The other two examples may be seen as confirming evidence for the basic assumption. What is common to all three models is that they involve design and representation relations. (shrink)
In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Philip Kitcher’s unificationist account supports IBE; Peter Railton’s deductive-nomological-probabilistic model, Wesley Salmon’s statistical-relevance Model, and (...) Bas van Fraassen’s erotetic account are incompatible with IBE; and Wesley Salmon’s causal-mechanical model is merely consistent with IBE. In short, many influential models of scientific explanation do not support IBE. I end by outlining three possible conclusions to draw: (1) either philosophers of science or defenders of IBE have seriously misconstrued the concept of explanation, (2) philosophers of science and defenders of IBE do not use the term ‘explanation’ univocally, and (3) the ampliative conception of IBE, which is compatible with any model of scientific explanation, deserves a closer look. (shrink)
Contemporary philosophers of science argue that models are a major vehicle of scientific knowledge. This applies to highly theoretical inquiry as well as to experimental or otherwise observational research, in both the natural and the social sciences. Making this claim is not yet very illuminating, given that there is a large variety of different kinds of model, and a number of ways in which they function in the service of science. The ambiguity of the term ‘model’ and the multiplicity (...) of kinds of model are illustrated by Pierre Duhem’s famous comparison of the mind of a continental physicist to that of an Englishman: the former strives for ‘theories’ that are formulated in ‘the clear and precise language of geometry and algebra’ and consist of abstract and idealized notions and formulae, while the latter insists on having mechanical ‘models’ that satisfy ‘his need to imagine concrete, material, visible, and tangible things’ that are familiar to ordinary experience. While the French or German physicist deals with a formalized theory in his account of electrostasis, the English account is in terms of ‘strings which move around pulleys, which roll around drums, which go through pearl beads, which carry weights; and tubes which pump water while others swell and contract; toothed wheels which are geared to one another and engage hooks. We thought we were entering the tranquil and neatly ordered abode of reason, but we find ourselves in a factory’ (Duhem 1954, pp. 70–1). Models in this sense—iconic models —employ analogies to visualize the mechanisms depicted, while formalized theories supposedly lack this property. On the other hand, it is nowadays customary to use the term ‘model’ also for such formal systems of equations, conspicuously so in the social sciences—and, much of the time, such formal systems are used for representing causal mechanisms. In relation to such a formal system, ‘model’ is also used for its various interpretations, or just for the interpretation that makes it true. The model muddle needs to be sorted out. (shrink)
In what follows, I will give examples of the sorts of step that can be taken towards spelling out the intuition that, after all, good models might be true. Along the way, I provide an outline of my account of models as ontologically and pragmatically constrained representations. And I emphasize the importance of examining models as functionally composed systems in which different components play different roles and only some components serve as relevant truth bearers. This disputes the (...) standard approach that proceeds by simply counting true and false elements in models in their entirety and concludes that models are false since they contain so many false elements. I call my alternative the functional decomposition approach. (shrink)
3 Abstract This paper is about modeling morality, with a proposal as to the best 4 way to do it. There is the small problem, however, in continuing disagreements 5 over what morality actually is, and so what is worth modeling. This paper resolves 6 this problem around an understanding of the purpose of a moral model, and from 7 this purpose approaches the best way to model morality.
Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh. This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are - balancing predictive accuracy and high level transparency in learning - scaling (...) linguistic algorithms to high-dimensional data problems - integrating linguistic expert knowledge with knowledge derived from data - identifying sound and useful inference rules - integrating fuzzy and probabilistic uncertainty in data modelling. (shrink)
The book offers condensed summaries of twenty-three major models of skill acquisition and expertise development presented by leading researchers during the last half a century of classic and new research. This book presents new researchers in learning, training, cognitive sciences or education disciplines with a big picture starting point for their literature review journey. The book presents an easy to understand taxonomy of twenty-three models which can give new researchers a good bird’s eye view of existing models (...) and theories, based on which they can decide which direction to dig further. The reviews in this book are complemented with over 200 authentic sources which a researcher read for detailed and deeper dive and set the direction for further exploration. This book would also act as an essential reference for training & learning professionals and instructional designers to design research-based training curriculum to develop skills of their staff. (shrink)
The central aim of science is to make sense of the world. To move forward as a community endeavor, sense-making must be systematic and focused. The question then is how do scientists actually experience the sense-making process? In this chapter we examine the “practice turn” in science studies and in particular how as a result of this turn scholars have come to realize that models are the “functional unit” of scientific thought and form the center of the reasoning/sense-making process. (...) This chapter will explore a context-dependent view of models and modeling in science. From this analysis we present a framework for delineating the different aspects of model-based reasoning and describe how this view can be useful in educational settings. This framework highlights how modeling supports and focuses scientific practice on sense-making. (shrink)
We used agent-based modelling to highlight the advantages and disadvantages of several management styles in biology, ranging from centralized to egalitarian ones. In egalitarian groups, all team members are connected with each other, while in centralized ones, they are only connected with the principal investigator. Our model incorporated time constraints, which negatively influenced weakly connected groups such as centralized ones. Moreover, our results show that egalitarian groups outperform others if the questions addressed are relatively simple or when the communication among (...) agents is limited. Complex epistemic spaces are explored best by centralized groups. They outperform other team structures because the individual members can develop their own ideas with less interference of the opinions of others. The optimal ratio between time spent on experimentation and dissemination varies between different organizational structures. Furthermore, if the evidence is shared only after a relevant degree of certainty is reached, all investigated groups epistemically profit. We discovered that the introduction of seminars to the model changes the epistemic performance in favour of weakly connected teams. Finally, the abilities of the principal investigator do not seem to outperform cognitive diversity, as group performances were not strongly influenced by the increase of her abilities. (shrink)
In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, (...) I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object. (shrink)
In this paper I investigate Putnam’s model-theoretic argument from a transcendent standpoint, in spite of Putnam’s well-known objections to such a standpoint. This transcendence, however, requires ascent to something more like a Tarskian meta-level than what Putnam regards as a “God’s eye view”. Still, it is methodologically quite powerful, leading to a significant increase in our investigative tools. The result is a shift from Putnam’s skeptical conclusion to a new understanding of realism, truth, correspondence, knowledge, and theories, or certain aspects (...) thereof, based on, among other things, a better understanding of what models are designed (and not designed) to do. (shrink)
Three metascientific concepts that have been object of philosophical analysis are the concepts oflaw, model and theory. The aim ofthis article is to present the explication of these concepts, and of their relationships, made within the framework of Sneedean or Metatheoretical Structuralism (Balzer et al. 1987), and of their application to a case from the realm of biology: Population Dynamics. The analysis carried out will make it possible to support, contrary to what some philosophers of science in general and of (...) biology in particular hold, the following claims: a) there are "laws" in biological sciences, b) many of the heterogeneous and different "models" of biology can be accommodated under some "theory", and c) this is exactly what confers great unifying power to biological theories. (shrink)
The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in (...) the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science. (shrink)