This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.

Many philosophers have drawn parallels between scientific models and fictions. In this paper I will be concerned with a recent version of the analogy, which compares models to the imagined characters of fictional literature. Though versions of the position differ, the shared idea is that modeling essentially involves imagining concrete systems analogously to the way that we imagine characters and events in response to works of fiction. Advocates of this view argue that imagining concrete systems plays an (...) ineliminable role in the practice of modeling that cannot be captured by other accounts. The approach thus leaves open what we should say about the ontological status of model-systems, and here advocates differ among themselves, defending a variety of realist or anti-realist positions. I argue that this debate over the ontological status of model-systems is misguided. If model-systems are the kinds of objects fictional realists posit, they can play no role in explaining the epistemology of modeling for an advocate of this approach. So they are at best superfluous. Defenders of the approach should focus on developing an account of the epistemological role of imagining model-systems. (shrink)

The practice of scientific modelling often resorts to hypothetical, false, idealised, targetless, partial, generalised, and other types of modelling that appear to have at least partially non-actual targets. In this paper, I will argue that we can avoid a commitment to non-actual targets by sketching a framework where models are understood as having networks of possibilities as their targets. This raises a further question: what are the truthmakers for the modal claims that we can derive from models? (...) I propose that we can find truthmakers for the modal claims derived from models in actuality, even in the case of supposedly non-actual targets. I then put this framework to use by examining a case study concerning the modelling of superheavy elements. (shrink)

We address the need for a model by considering two competing theories regarding the origin of life: (i) the Metabolism First theory, and (ii) the RNA World theory. We discuss two interrelated points, namely: (i) Models are valuable tools for understanding both the processes and intricacies of origin-of-life issues, and (ii) Insights from models also help us to evaluate the core objection to origin-of-life theories, called “the inefficiency objection”, which is commonly raised by proponents of both the Metabolism (...) First theory and the RNA World theory against each other. We use Simpson’s Paradox (SP) as a tool for challenging this objection. We will use models in various senses, ranging from taking them as representations of reality to treating them as theories/accounts that provide heuristics for probing reality. In this paper, we will frequently use models and theories interchangeably. Additionally, we investigate Conway’s Game of Life and contrast it with our SP-based approach to emergence-of-life issues. Finally, we discuss some of the consequences of our view. A scientific model is testable in three senses: (i) a logical sense, (ii) a nomological sense, and (iii) a current technological sense. The SP-based model is testable in the first two senses but it is not feasible to test it using current technology. (shrink)

My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...) be determined by the forces they will be subjected to between the moment the toboggan will be released at the top of the hill and the moment it will reach its highest speed. The problem is that, throughout their downhill journey, my daughters and the toboggan will be subjected to an incredibly large number of forces—from the gravitational pull of any massive object in the universe to the weight of the snowflake that is sitting on the tip of one of my youngest daughter’s hairs—so that any attempt to apply the theory directly to the real-world system in all its complexity seems to be doomed to failure. (shrink)

يناقش الكتاب دور النماذج الفكرية والمادية في اكتساب وتشكيل كافة أنماط المعارف الإنسانية، بداية من المعرفة العادية التي يسعى بها عامة الناس إلى فهم ما يدور حولهم من أمور الحياة، ومرورًا بالمعارف الفلسفية والدينية والفنية التي تحكم توجهات الإنسان العقلانية والوجدانية، ووصولاً إلى المعرفة العلمية الرامية إلى فهم ظواهر الكون وترويضها وفقًا لقوانين حاكمة. ويطرح الكتاب فرضًا أساسيًا مؤداه أن ما يتلفظ به العلماء من كلمات مثل «الفرض» و«القانون» و«النظرية» ما هي إلا أسماء مترادفة لشيء واحد يصب في خانة «النموذج»، (...) وإذا كان ثمة فرق بين ما نعنيه بهذه الكلمات، فهو في الحقيقة فرقٌ في الدرجة لا في النوع، أو هو فرق بين نموذج أولي في مرحلة الفرض، وبين ذات النموذج في مرحلة القانون أو النظرية، وعلى هذا فمن الأجدر بنا ألا نتحدث عن فرض أو قانون أو نظرية، وإنما عن «نموذج علمي» يمر بمراحل متعددة حتى يتم تأكيده وقبوله، وهو في الوقت ذاته عُرضة لأن يحل محله نموذج آخر إذا اتسعت دائرة البحث لتشمل وقائع أخرى لا يحتويها النموذج أو يعجز عن تفسيرها. (shrink)

Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare (...) the positions of these two views with respect to four points regarding ontological assumptions: (1) the origin of ontological assumptions, (2) the relation of such assumptions to the formal models of the same theory, (3) their use in integrating and negotiating different formal models of distinct theories, and (4) their employment in explanatory activity. Dialectical is here used in both its Hegelian–Marxist sense of opposition and tension between alternative positions and in its Platonic sense of dialogue between advocates of distinct theories. I investigate three case studies, from Levins and Lewontin as well as from a recent paper of mine, that show the relevance and power of the dialectical understanding of theorizing and modeling. (shrink)

This paper defends an approach to modeling and models in science that is against model fictionalism of a recent stripe (the “new fictionalism” that takes models to be abstract entities that are analogous to works of fiction). It further argues that there is a version of fictionalism on models to which my approach is neutral and which only makes sense if one adopts a special sort of antirealism (e.g. constructive empiricism). Otherwise, my approach strongly suggests that one (...) stays away from fictionalism and embraces realism directly. (shrink)

Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.

Giere’s analysis of the epistemic role of fiction in science and literature is the representative of antifictionists. Our research finds the three inconsistencies in his main paper regarding the comparison of fiction in scientific models and literary works. We analyze his argument and offer our solution to the issue favoring the perspective of fictionalism. Further, we support a typological differentiation of false representation in science into fictional and fictitious. The value of this differentiation we demonstrate by giving the (...) example of digital organisms in system biology. The paper aims to help better understanding of fiction in science and to avoid the oversimplification of literary fiction. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise (...) algorithmic computation and to offer foundations for scientific computing. -/- The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. -/- In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. -/- In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (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 Kitcher’s unificationist account supports IBE; Railton’s deductive–nomological–probabilistic model, Salmon’s statistical-relevance model, and van (...) Fraassen’s erotetic account are incompatible with IBE; and 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)

General Morphological Analysis (GMA) is a method for structuring a conceptual problem space – called a morphospace – and, through a process of existential combinatorics, synthesizing a solution space. As such, it is a basic modelling method, on a par with other scientific modelling methods including System Dynamics Modelling, Bayesian Networks and various types graph-based “influence diagrams”. The purpose of this article is 1) to present the theoretical and methodological basics of morphological modelling; 2) to situate GMA within a (...) broader modelling theoretical framework by developing a (morphological) model representing different modelling methods, and 3) to demonstrate some of the basic modelling techniques that can be carried out with GMA using dedicated computer support. (shrink)

In this article, I argue that the use of scientific models that attribute intentional content to complex systems bears a striking similarity to the way in which statistical descriptions are used. To demonstrate this, I compare and contrast an intentional model with a statistical model, and argue that key similarities between the two give us compelling reasons to consider both as a type of phenomenological model. I then demonstrate how intentional descriptions play an important role in scientific (...) methodology as a type of phenomenal model, and argue that this makes them as essential as any other model of this type. (shrink)

This article discusses Sven Ove Hansson’s corpus model for the influence of values (in particular, non-epistemic ones) in the hypothesis acceptance/rejection phase of scientific inquiry. This corpus model is based on Hansson’s concepts of scientific corpus and science ‘in the large sense’. I first present Hansson’s corpus model of value influence with some introductory comments about its origins, a detailed presentation of the model with a new terminology, an analysis of its limits, and an appreciation of its handling (...) of controversial non-epistemic values. I conclude that it is a very good candidate for managing value influence, because contrary to other models in the literature it is fairly simple, it provides a universal and systematic procedure for dealing with values, and it syste- matically preserves the epistemic integrity of science. I then study some difficulties associated with the model’s central feature of taking the maximum level of evidence required by the applications of a claim in order for the latter to enter the scientific corpus: the difficulty to identify this maximum requirement; the non-optimality of this requirement with respect to other less demanding requirements; the potentially detrimental consequences of the preference for false negatives over false positives on which it relies; and the issue of whether there can nevertheless be requirements higher than this maximum. I show that these issues may potentially challenge the corpus model. Finally, I call for empirical work (in particular, an investigation of scientists’ and engineers’ own normative views) in order to better assess these issues. (shrink)

Upholders of fictionalism about scientific models have not yet successfully explained how scientists can learn about the real world by making comparisons between models and the real phenomena they stand for. In this paper I develop an account of model-world comparisons in terms of what I take to be the best antirealist analyses of comparative claims that emerge from the current debate on fiction.

Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) (...) they propose to answer the three questions cannot answer them, at least not by itself. Second, I argue that answers to the three questions are unnecessary to account for the explanatoriness of their minimal models. Finally, I argue that a common features account, what I call the ‘generalized ontic conception of explanation’, can capture the explanatoriness of minimal models. (shrink)

In the past few years, social epistemologists have developed several formal models of the social organisation of science. While their robustness and representational adequacy has been analysed at length, the function of these models has begun to be discussed in more general terms only recently. In this article, I will interpret many of the current formal models of the scientific community as representing the latest development of what I will call the ‘Kuhnian project’. These models (...) share with Kuhn a number of questions about the relation between individuals and communities. At the same time, they also inherit some of Kuhn’s problematic characterisations of the scientific community. In particular, current models of the social organisation of science represent the scientific community as essentially value-free. This may put into question both their representational adequacy and their normative ambitions. In the end, it will be shown that the discussion on the formal models of the scientific community may contribute in fruitful ways to the ongoing debates on value judgements in science. (shrink)

The book attempts to build a bridge across three cultures: mathematical statistics, quantum theory and chemometrical methods.

Otto Neurath’s empiricist methodology of economics and his contributions to politi- cal economy have gained increasing attention in recent years. We connect this research with contemporary debates regarding the epistemological status of thought experiments by reconstructing Neurath’s utopias as linchpins of thought experiments. In our three reconstructed examples of different uses of utopias/dystopias in thought experiments we employ a reformulation of Häggqvist’s model for thought experiments and we argue that: (1) Our reformulation of Häggqvist’s model more adequately complies with many (...) uses of thought experiments, especially with the open-ended discussions of utopias and dys- topias in thought experiments. (2) As a strict logical empiricist, Neurath is committed to a strictly empiricist account of thought experiments. John Norton’s empiricist argument view can indeed account for the justifications of empirical beliefs and genuine discoveries targeted by scientific utopianism in three distinct (yet connected) ways, all of which Neur- ath already contemplated: (2.I) Dealing with utopias and thought experiments on a regular basis increases creativity and inventiveness. (2.II) Particular ways of presenting knowl- edge facilitate scientific discovery and social progress. (2.III) The use of utopias in thought experiments can prompt conceptual change and allow access to new phenomena. We con- clude by highlighting that, even though thought experiments support a positive attitude for exploring new social possibilities, Neurath points out that active decisions are unavoidable. The exploration of alternatives and the awareness of a need for decisions in policy discus- sion avert a technocratic outlook in social science. (shrink)

Prediction Error Minimization theory (PEM) is one of the most promising attempts to model perception in current science of mind, and it has recently been advocated by some prominent philosophers as Andy Clark and Jakob Hohwy. Briefly, PEM maintains that “the brain is an organ that on aver-age and over time continually minimizes the error between the sensory input it predicts on the basis of its model of the world and the actual sensory input” (Hohwy 2014, p. 2). An interesting (...) debate has arisen with regard to which is the more adequate epistemological interpretation of PEM. Indeed, Hohwy maintains that given that PEM supports an inferential view of perception and cognition, PEM has to be considered as conveying an internalist epistemological perspective. Contrary to this view, Clark maintains that it would be incorrect to interpret in such a way the indirectness of the link between the world and our inner model of it, and that PEM may well be combined with an externalist epistemological perspective. The aim of this paper is to assess those two opposite interpretations of PEM. Moreover, it will be suggested that Hohwy’s position may be considerably strengthened by adopting Carlo Cellucci’s view on knowledge (2013). (shrink)

Just before the Scientific Revolution, there was a "Mathematical Revolution", heavily based on geometrical and machine diagrams. The "faculty of imagination" (now called scientific visualization) was developed to allow 3D understanding of planetary motion, human anatomy and the workings of machines. 1543 saw the publication of the heavily geometrical work of Copernicus and Vesalius, as well as the first Italian translation of Euclid.

Evolutionary anthropologists and archaeologists have been considerably successful in modelling the cumulative evolution of culture, of technological skills and knowledge in particular. Recently, one of these models has been introduced in the philosophy of science by De Cruz and De Smedt (Philos Stud 157:411–429, 2012), in an attempt to demonstrate that scientists may collectively come to hold more truth-approximating beliefs, despite the cognitive biases which they individually are known to be subject to. Here we identify a major shortcoming in (...) that attempt: De Cruz & De Smedt’s mathematical model makes one particularly strong tractability assumption that causes the model to largely miss its target (namely, truth accumulation in science), and that moreover conflicts with empirical observations. The second, more constructive part of the paper presents an alternative, agent-based model, which allows one to much better examine the conditions for scientific progress and decline. (shrink)

Open peer commentary on the article “Ethics: A Radical-constructivist Approach” by Andreas Quale. Upshot: The first of my two main goals in this commentary is to establish thinking of ethics as concepts rather than as non-cognitive knowledge. The second is to argue that establishing models of individuals’ ethical concepts is a scientific enterprise that is quite similar to establishing models of individuals’ mathematical concepts. To accomplish these two primary goals, I draw from my experience of working scientifically (...) with von Glasersfeld for 25 years while he was developing radical constructivism as a coherent model of knowing, and appeal to several of his basic insights to establish constructing models of ethical concepts as a scientific enterprise. (shrink)

In considering relations between science and theology, the discussion of the Big Bang model plays a significant role. Amongst the sources of this model there are not only scientific achievements of recent decades taken as objective knowledge as seen in modern methodology, but also many non-scientific factors. The latter is connected with the quite obvious fact that the authors, as well as the recipients of the Model, are people who are guided in their activity - including obtaining their (...) rational knowledge - by non-rational motives. Those motives appear on the one hand in the very process of creation of the Model. Different scientific theories as well as unverified hypotheses are being joined in one "picture" called The Standard Model. It seems that it is being done on the grounds of various factors that lie outside the field of science. Among them there are the different convictions of the persons constructing this view of the world. However, those convictions, commonly shared by the authors and recipients of the Model, are not based on the rational criterion of scientific knowledge. On the other hand, the Big Bang model may be interpreted in opposite ways by its recipients. The influences of religious and other beliefs are so essential, that they may lead to extremely different conclusions though based on the same ground. It is demonstrated that, due to the epistemological status of cosmology, such a situation is inevitable, and no final verdict with regard to the idea of the cause of the world can be reached. (shrink)

Most philosophical accounts of scientific models assume that models represent some aspect, or some theory, of reality. They also assume that interpretation plays only a supporting role. This paper challenges both assumptions. It proposes that models can be used in science to interpret reality. (a) I distinguish these interpretative models from representational ones. They find new meanings in a target system’s behaviour, rather than fit its parts together. They are built through idealisation, abstraction and recontextualisation. (...) (b) To show how interpretative models work, I offer a case study on the scientific controversy over foetal pain. It highlights how pain scientists use conflicting models to interpret the human foetus and its behaviour, and thereby to support opposing claims about whether the foetus can feel pain. (c) I raise a sceptical worry and a methodological challenge for interpretative models. To address the latter, I use my case study to compare how interpretative and representational models ought to be evaluated. (shrink)

This paper challenges premises regarding the ‘Kuhn vs Popper debate’ which is often introduced to students at a university level. Though I acknowledge the disagreements between Kuhn and Popper, I argue that their models of science are greatly similar. To begin, some preliminary context is given to point out conceptual and terminological barriers within this debate. The remainder of paper illuminates consistencies between the influential books The Logic of Scientific Discoveries (by Popper, abbreviated as Logic) and The Structure (...) of Scientific Revolutions (by Kuhn, abbreviated as Structure). The central purpose of this comparison is to synthesize a shared model of scientific change. The broader implication of this approach is appreciating common ground in discussions that are defined by their disagreements (particularly in philosophy of science). (shrink)

The paper presents some thoughts about how an account of rationality might be recovered from what might have first appeared as anti-rationalistic ideas in the work of Kuhn and Feyerabend. The paper draws inspiration from some suggestions of Bernstein and Rorty, as well well as Brown's theory of rationality.

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

Traditionally, a scientific model is thought to provide a good scientific explanation to the extent that it satisfies certain scientific goals that are thought to be constitutive of explanation. Problems arise when we realize that individual scientific models cannot simultaneously satisfy all the scientific goals typically associated with explanation. A given model’s ability to satisfy some goals must always come at the expense of satisfying others. This has resulted in philosophical disputes regarding which of (...) these goals are in fact necessary for explanation, and as such which types of models can and cannot provide explanations. Explanatory monists argue that one goal will be explanatory in all contexts, while explanatory pluralists argue that the goal will vary based on pragmatic considerations. In this paper, I argue that such debates are misguided, and that both monists and pluralists are incorrect. Instead of any goal being given explanatory priority over others in a given context, the different goals are all deeply dependent on one another for their explanatory power. Any model that sacrifices some explanatory goals to attain others will always necessarily undermine its own explanatory power in the process. And so when forced to choose between individual scientific models, there can be no explanatory victors. Given that no model can satisfy all the goals typically associated with explanation, no one model in isolation can provide a good scientific explanation. Instead we must appeal to collections of models. Collections of models provide an explanation when they satisfy the web of interconnected goals that justify the explanatory power of one another. (shrink)

I explore a challenge that idealisations pose to scientific realism and argue that the realist can best accommodate idealisations by capitalising on certain modal features of idealised models that are underwritten by laws of nature.

The introspective devices framework proposed by Kammerer and Frankish (this issue) offers an attractive conceptual tool for evaluating and developing accounts of introspection. However, the framework assumes that different views about the nature of introspection can be easily evaluated against a set of common criteria. In this paper, I set out to test this assumption by analysing two formal models of introspection using the introspective device framework. The question I aim to answer is not only whether models developed (...) outside of philosophy can be successfully evaluated against the set of conceptual criteria proposed by Kammerer and Frankish, but also whether this kind of evaluation can reveal some limitations inherent to the framework. (shrink)

[penultimate draft; prepared for publication in Aristotle’s Parts of Animals: A Critical Guide, ed. Sophia Connell – please cite final version] -/- Parts of Animals II.10 makes a new beginning in Aristotle’s study of animals. In it, Aristotle proposes to “now speak as if we are once more at an origin, beginning first with those things that are primary” (655b28-9). This is the start of his account of the non-uniform parts of blooded animals: parts such as eyes, noses, mouths, etc., (...) as opposed to uniform parts like blood and flesh. PA II.10 proposes a new strategy for studying these parts: “one ought to speak about the human kind first” (656a10). Beginning “first” with the “primary” things thus amounts to beginning with humans (655b28-9). -/- Why does Aristotle think this strategy is appropriate for his project? One answer is that it reflects a fundamental anthropocentrism. Lloyd, for instance, has raised the possibility that the basic assumptions that guide Aristotle’s biology may to some extent reflect the anthropocentrism of his culture, where the relation of humans to animals was “a preoccupation of popular beliefs”. In this paper, I develop an interpretation that both builds on and challenges this suggestion. I do so by investigating how Aristotle thinks this strategy works in theory and in practice: his justifications for adopting it and its interaction with his scientific commitments. I argue that Aristotle adopts it in part because he thinks that humans are such that his scientific concepts apply to them in a particularly clear way. (This too is a form of anthropocentrism: one that might be resisted by arguing that humans are not the best illustrations of these concepts—that Aristotle ought to look elsewhere for models with the features he desires.) -/- More specifically, Aristotle holds that starting with humans helps establish the causal explanations of the parts of other animals, particularly when they are recalcitrant. What makes humans suitable for this role is a special teleological relation between their parts and the ends they serve: in humans, he supposes, this connection is particularly tight (in a way I explain below). Consequently, Aristotle thinks he can use humans to illuminate which sorts of features tend to be for the sake of which ends, and then extend the results of this inquiry to the parts of the other animals. (shrink)

Theoretical models are widely held as sources of knowledge of reality. Imagination is vital to their development and to the generation of plausible hypotheses about reality. But how can imagination, which is typically held to be completely free, effectively instruct us about reality? In this paper I argue that the key to answering this question is in constrained uses of imagination. More specifically, I identify make-believe as the right notion of imagination at work in modelling. I propose the first (...) overarching taxonomy of types of constraints on scientific imagination that enables knowledge of reality. And I identify two main kinds of knowledge enabled by models, knowledge of the imaginary scenario specified by models and knowledge of reality. (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)

It is plausible to think that, in order to actively employ models in their inquiries, scientists should be aware of their existence. The question is especially puzzling for realists in the case of abstract models, since it is not obvious how this is possible. Interestingly, though, this question has drawn little attention in the relevant literature. Perhaps the most obvious choice for a realist is appealing to intuition. In this paper, I argue that if scientific models (...) were abstract entities, one could not be aware of them intuitively. I deploy my argumentation by building on Chudnoff’s elaboration on intuitive awareness. Furthermore, I shortly discuss some other options to which realists could turn in order to address the question of awareness. (shrink)

Scientific models typically contain idealizations, or assumptions that are known not to be true. Philosophers have long questioned the nature of idealizations: Are they heuristic tools that will be abandoned? Or rather fictional representations of reality? And how can we reconcile them with realism about knowledge of nature? Immanuel Kant developed an account of scientific investigation that can inspire a new approach to the contemporary debate. Kant argued that scientific investigation is possible only if guided by (...) ideal assumptions—what he calls “regulative ideas”. These ideas are not true of objects of nature, and yet they are not heuristic tools or fictional represen- tations. They are necessary rules governing the construction and assessment of scientific explanations. In this paper, I suggest that some idealizations can be interpreted as having necessary regulative value and as being compatible with scientific realism. I first analyze the puzzle of the nature of idealization and present the main approaches to this topic in the literature. Second, I reconsider the puzzle vis-a-vis a restricted, Kantian definition of idealization and a novel characterization of the relation between idealization and truth. Finally, I discuss in detail an example of idealization (the Hardy-Weinberg equilibrium) along the suggested Kantian lines. (shrink)

This book defends a two-tiered account of epistemic representation--the sort of representation relation that holds between representations such as maps and scientific models and their targets. It defends a interpretational account of epistemic representation and a structural similarity account of overall faithful epistemic representation.

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)

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)

In this article, I argue that intentional psychology (i.e. the interpretation of human behaviour in terms of intentional states and propositional attitudes) plays an essential role in the sciences of the mind. However, this role is not one of identifying scientifically respectable states of the world. Rather, I argue that intentional psychology acts as a type of phenomenological model, as opposed to a mechanistic one. I demonstrate that, like other phenomenological models in science, intentional psychology is a methodological tool (...) with its own benefits and insights that complements our mechanistic understanding of systems. As a result, intentional psychology's distinctive scientific benefit is its ability to model systems in unique, non-mechanistic, ways. This allows us to generate predictions that we cannot otherwise generate using the mechanistic models of neuroscience and cognitive psychology necessary for various scientific tasks. (shrink)

In this book, Nugayev makes a clear case against Kuhnian and Lakatosian models. For him the origin of scientific revolutions lies in the clash of theories which are already mature and have triumphed in their respective spheres of action.

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

Recently, a number of authors have suggested that we understand scientific models in the same way as fictional characters, like Sherlock Holmes. The biggest challenge for this approach concerns the ontology of fictional characters. I consider two responses to this challenge, given by Roman Frigg, Ronald Giere and Peter Godfrey-Smith, and argue that neither is successful. I then suggest an alternative approach. While parallels with fiction are useful, I argue that models of real systems are more aptly (...) compared to works that portray real people, like the Emperor Claudius. This approach will allow us to avoid problems with fictional characters. (shrink)

Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some have also (...) drawn parallels between models and fiction. This chapter examines these approaches to scientific modeling and considers the challenges they face. (shrink)

In this paper I will introduce a problem for at least those Humeans who believe that the future is open. More particularly, I will argue that the following aspect of scientific practice cannot be explained by openfuture- Humeanism: There is a distinction between states that we cannot bring about (which are represented in scientific models as nomologically impossible) and states that we merely happen not to bring about. Open-future-Humeanism has no convincing account of this distinction. Therefore it (...) fails to explain why we cannot bring about certain states of affairs, it cannot explain what I call the “recalcitrance of nature”. (shrink)

If we consider modeling not as a heap of contingent structures, but (where possible) as evolving coordinated systems of models, then we can reasonably explain as "direct representations" even some very complicated model-based cognitive situations. Scientific modeling is not as indirect as it may seem. "Direct theorizing" comes later, as the result of a successful model evolution.

We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and (...) analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures. (shrink)

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