The type-level abstraction is a formal way to represent molecular structures in biological practice. Graphical representations of molecular structures of biological objects are also used to identify functional processes of things. This paper will reveal that category theory is a formal mathematical language not only to visualize molecular structures of biological objects as type-level abstraction formally but also to understand how to infer biological functions from the molecular structures of biological objects. Category theory is a toolkit to understand biological knowledge (...) at the type-level formally, not individual token-level, as well as typical heuristic strategies in molecular biology. (shrink)

Benacerraf has presented two problems for the philosophy of mathematics. These are the problem of identification and the problem of representation. This paper aims to reconstruct the latter problem and to unpack its undermining bearing on the version of Ontic Structural Realism that frames scientific representations in terms of abstract structures. I argue that the dichotomy between mathematical structures and physical ones cannot be used to address the Benacerraf problem but strengthens it. I conclude by arguing that versions of OSR (...) that do not rely on mathematical frameworks for representational purposes need not be vulnerable to Benacerraf’s second problem. (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.

What is the role of the environment, and of the information it provides, in cognition? More specifically, may there be a role for certain artefacts to play in this context? These are questions that motivate "4E" theories of cognition (as being embodied, embedded, extended, enactive). In his take on that family of views, Hajo Greif first defends and refines a concept of information as primarily natural, environmentally embedded in character, which had been eclipsed by information-processing views of cognition. He continues (...) with an inquiry into the cognitive bearing of some artefacts that are sometimes referred to as 'intelligent environments'. Without necessarily having much to do with Artificial Intelligence, such artefacts may ultimately modify our informational environments. -/- With respect to human cognition, the most notable effect of digital computers is not that they might be able, or become able, to think but that they alter the way we perceive, think and act. (shrink)

This paper offers an intellectual history of the scientific realism debate during the twentieth century. The telling of the tale will explain the philosophical significance and the prospects of the scientific realism debate, through the major turns it went through. The emphasis will be on the relations between empiricism and scientific realism and on the swing from metaphysics-hostile to metaphysics-friendly versions of realism.

It is widely accepted by formal and informal logicians alike that a formal logic which, by the lights of English, gets the connectives wrong, nevertheless conspires to get entailment right—right that is, modulo English. There is a vexing problem occasioned by this semantic alienation of formal logic. It is next to impossible for formal logic to meet the expectations of realism. What, then, of informal logic?

Informational structural realism (ISR) offers a new way to understand the nature of the “structure” that structural realists claim our best scientific theories get right about the world. According to Luciano Floridi, who has given the most detailed formulation of ISR so far, this structure is composed of information representing binary differences. In this paper I assess whether ISR offers a good way to resolve the tension between the no miracle argument (often taken to support scientific realism) and the pessimistic (...) meta-induction (often taken to support antirealism). With regards to this important motivation for structural realism, I shall argue that ISR faces insurmountable difficulties. However, I agree that interpreting “structure” in terms of information can be profitable for the realist. Instead, I offer a new version of ISR that borrows from algorithmic information theory. As a result, a more realist version of ISR is provided. (shrink)

Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...) what consequences those tradeoffs have for theoretical practice. (shrink)

Many explanations in physics rely on idealized models of physical systems. These explanations fail to satisfy the conditions of standard normative accounts of explanation. Recently, some philosophers have claimed that idealizations can be used to underwrite explanation nonetheless, but only when they are what have variously been called representational, Galilean, controllable or harmless idealizations. This paper argues that such a half-measure is untenable and that idealizations not of this sort can have explanatory capacities.

In place of the traditional epistemological view of knowledge as justified true belief we argue that artificial intelligence and law needs an evidence-based epistemology according to which scientific knowledge is based on critical analysis of evidence using argumentation. This new epistemology of scientific evidence (ESE) models scientific knowledge as achieved through a process of marshaling evidence in a scientific inquiry that results in a convergence of scientific theories and research results. We show how a dialogue interface of argument from expert (...) opinion, along with its set of critical questions, provides the argumentation component of the ESE. It enables internal scientific knowledge to be translated over into a wider arena in which individual nonexpert citizens and groups can make use of it. The external component shows how evidence is presented and used in a legal procedural setting that includes fact-finding, weighing the credibility of expert witnesses, and critical questioning of arguments. The paper critically reviews the standards of admissibility of scientific evidence using the ESE. (shrink)

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based—factive—explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a more explicit specification of (...) model-world mismatches and properties imputation. (shrink)

In “The Toolbox of Science” (1995) together with Towfic Shomar we advocated a form of instrumentalism about scientific theories. We separately developed this view further in a number of subsequent works. Steven French, James Ladyman, Otavio Bueno and Newton Da Costa (FLBD) have since written at least eight papers and a book criticising our work. Here we defend ourselves. First we explain what we mean in denying that models derive from theory – and why their failure to do so should (...) be lamented. Second we defend our use of the London model of superconductivity as an example. Third we point out both advantages and weaknesses of FLBD’s techniques in comparison to traditional Anglophone versions of the semantic conception. Fourth we show that FLBD’s version of the semantic conception has not been applied to our case study. We conclude by raising doubts about FLBD’s overall project. (shrink)

The central concern of this article is whether the semantic approach has the resources to appropriately capture the core tenets of structural realism. Chakravartty (2001) has argued that a realist notion of correspondence cannot be accommodated without introducing a linguistic component, which undermines the approach itself. We suggest that this worry can be addressed by an appropriate understanding of the role of language in this context. The real challenge, however, is how to incorporate the core notion of `explanatory approximate truth' (...) in such a way that the emphasis on structure is retained. (shrink)

Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for (...) identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms. (shrink)

It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..

The epistemic conception of scientific progress equates progress with accumulation of scientific knowledge. I argue that the epistemic conception fails to fully capture scientific progress: theoretical progress, in particular, can transcend scientific knowledge in important ways. Sometimes theoretical progress can be a matter of new theories ‘latching better onto unobservable reality’ in a way that need not be a matter of new knowledge. Recognising this further dimension of theoretical progress is particularly significant for understanding scientific realism, since realism is naturally (...) construed as the claim that science makes theoretical progress. Some prominent realist positions are best understood in terms of commitment to theoretical progress that cannot be equated with accumulation of scientific knowledge. (shrink)

The epistemic conception of scientific progress equates progress with accumulation of scientific knowledge. I argue that the epistemic conception fails to fully capture scientific progress: theoretical progress, in particular, can transcend scientific knowledge in important ways. Sometimes theoretical progress can be a matter of new theories ‘latching better onto unobservable reality’ in a way that need not be a matter of new knowledge. Recognising this further dimension of theoretical progress is particularly significant for understanding scientific realism, since realism is naturally (...) construed as the claim that science makes theoretical progress. Some prominent realist positions are best understood in terms of commitment to theoretical progress that cannot be equated with accumulation of scientific knowledge. (shrink)

The Pessimistic Induction from falsity of past theories forms a perennial argument against scientific realism. This paper considers and rebuts two recent arguments—due to Lewis (2001) and Lange (2002)—to the conclusion that the Pessimistic Induction (in its best known form) is fallacious. It re-establishes the dignity of the Pessimistic Induction by calling to mind the basic objective of the argument, and hence restores the propriety of the realist program of responding to PMI by undermining one or another of its premises.

I review prominent historical arguments against scientific realism to indicate how they display a systematic overshooting in the conclusions drawn from the historical evidence. The root of the overshooting can be located in some critical, undue presuppositions regarding realism. I will highlight these presuppositions in connection with both Laudan’s ‘Old induction’ and Stanford’s New induction, and then delineate a minimal realist view that does without the problematic presuppositions.

One of the most remarkable aspects of John Maynard Smith’s work was the fact that he devoted time both to doing science and to reflecting philosophically upon its methods and concepts. In this paper I offer a philosophical analysis of Maynard Smith’s approach to modelling phenotypic evolution in relation to three main themes. The first concerns the type of scientific understanding that ESS and optimality models give us. The second concerns the causal–historical aspect of stability analyses of adaptation. The third (...) concerns the concept of evolutionary stability itself. Taken together, these three themes comprise what I call the natural philosophy of adaptation. (shrink)

Williams and J. Fraser have recently argued that effective field theory methods enable scientific realists to make more reliable ontological commitments in quantum field theory than those commonly made. In this paper, I show that the interpretative relevance of these methods extends beyond the specific context of QFT by identifying common structural features shared by effective theories across physics. In particular, I argue that effective theories are best characterized by the fact that they contain intrinsic empirical limitations, and I extract (...) from their structure one central interpretative constraint for making more reliable ontological commitments in different subfields of physics. While this is in principle good news, this constraint still raises a challenge for scientific realists in some contexts, and I bring the point home by focusing on Williams’s and J. Fraser’s defense of selective realism in QFT. (shrink)

On the basis of a case study in bioengineering, this paper proposes a novel perspective on models in science and engineering. This is done with the help of two notions: representation and design. These two notions are interpreted as referring to modeling relations between vehicles and targets that differ in their respective directions of fit. The representation relation has a vehicle-to-target direction of fit and the design relation has a target-to-vehicle direction of fit. The case study of an organ on (...) chip model illustrates that the technical device can participate in both design and representation relations. The two relations share the same relatum of the organ on chip, but they have different directions of fit. In the design relation, the chip is adjusted to a design plan, in which case we are dealing with a target-to-vehicle direction of fit. In the representation relation, the chip is adjusted to a human organ, in which case we are dealing with a vehicle-to-target direction of fit. The example shows that a conception of modeling as involving only relations with a vehicle-to-target direction of fit is too narrow to account for models in science and engineering. (shrink)

I argue that we cannot adequately characterize idealization and abstraction and the distinction between the two on the grounds that they have distinct semantic properties. By doing so, on the one hand, we focus on the conceptual products of the two processes in making the distinction and we overlook the importance of the nature of the thought processes that underlie model-simplifying assumptions. On the other hand, we implicitly rely on a sense of abstraction as subtraction, which is unsuitable for explicating (...) scientific model construction. Instead, I argue that a sense of abstraction as extraction is more suitable. Finally, I suggest a different way to distinguish the two processes that avoids these problems. Namely, that both idealization and abstraction could be understood as particular modes of application of the same cognitive process: selective attention. (shrink)

Mathematical idealizations are scientific representations that result from assumptions that are believed to be false, and where mathematics plays a crucial role. I propose a two stage account of how to rank mathematical idealizations that is largely inspired by the semantic view of scientific theories. The paper concludes by considering how this approach to idealization allows for a limited form of scientific realism. ‡I would like to thank Robert Batterman, Gabriele Contessa, Eric Hiddleston, Nicholaos Jones, and Susan Vineberg for helpful (...) discussions and encouragement. †To contact the author, please write to: Department of Philosophy, Beering Hall, Purdue University, 100 N. University Street, West Lafayette, IN 47907-2098; e-mail: [email protected]. (shrink)

Asymptotic models in which singular limits are taken are very common in physics. They are often used to investigate the general behaviour of systems undergoing rapid, discontinuous, changes. The singularities in the mathematics of these systems have no physical counterparts; these models operate by containing non-physically interpretable fictional elements. As such there is an intuition that states that asymptotics only offer descriptions of systems not explanations of them. By contrast, in different areas of science other models containing fictional elements which (...) are physically interpretable are claimed to be explanatory. I argue that both types of fictional model possess modal content, and therefore it is unclear why models that contain unphysical fictions are merely descriptions, but models that contain physical fictions are explanations. One must either reject both as unexplanatory because they use false ontology to explain, or one should accept both types as explanatory. (shrink)

Moderate realists hold that scientific theories are truthlike, rather than exactly true. Although scientific realism has been challenged by arguments such as the pessimistic induction, moderate realism hasn’t been challenged directly on the grounds that it makes scientific progress rely on inferences from theories that are only truthlike. This paper shows that moderate realism is incompatible with the claim that deductive arguments from scientific theories are reliable. Using truthlike claims as the premises of some patterns of deductive reasoning renders the (...) argument dramatically unreliable. The conclusion is not guaranteed to be true. Nor is the conclusion guaranteed to be at least as truthlike as the premises. Nor even is the conclusion shown to be likely to be true. This is because the consequences of truthlike theories are neither guaranteed to be true, nor even more likely to be truthlike than not. Truthlike theories cannot function like true theories in deductive arguments; instead they function as radically false theories would. In short, truthlike theories behave exactly like radically false theories for the purposes of their deductive consequences. And since scientists would not trust deductions from radically false theories, they should not trust deductions from truthlike theories either. Furthermore, this applies to a wide range of logics and patterns of deductive argument. The moderate realist must either reject bivalence, deny that theories are truth-apt, or accept that scientific theories are not used in deductive arguments. (shrink)

Scientific realists use the “no miracle argument” to show that the empirical and pragmatic success of science is an indicator of the ability of scientific theories to give true or truthlike representations of unobservable reality. While antirealists define scientific progress in terms of empirical success or practical problem-solving, realists characterize progress by using some truth-related criteria. This paper defends the definition of scientific progress as increasing truthlikeness or verisimilitude. Antirealists have tried to rebut realism with the “pessimistic metainduction”, but critical (...) realists turn this argument into an optimistic view about progressive science. (shrink)

How does mathematics apply to something non-mathematical? We distinguish between a general application problem and a special application problem. A critical examination of the answer that structural mapping accounts offer to the former problem leads us to identify a lacuna in these accounts: they have to presuppose that target systems are structured and yet leave this presupposition unexplained. We propose to fill this gap with an account that attributes structures to targets through structure generating descriptions. These descriptions are physical descriptions (...) and so there is no such thing as a solely mathematical account of a target system. (shrink)

Inconsistency toleration is the phenomenon of working with inconsistent information without threatening one’s rationality. Here I address the role that ignorance plays for the tolerance of contradictions in the empirical sciences. In particular, I contend that there are two types of ignorance that, when present, can make epistemic agents to be rationally inclined to tolerate a contradiction. The first is factual ignorance, understood as temporary undecidability of the truth values of the conflicting propositions. The second is what I call “ignorance (...) of theoretical structure”, which is lack of knowledge of relevant inference patterns within a specific theory. I argue that these two types of ignorance can be explanatory of the scientists’ rational disposition to be tolerant towards contradictions, and I illustrate this with a case study from neutrino physics. (shrink)

In this paper, we first examine the reasons for opposing metaphysics. While assessing these reasons, we intend to reach a plausible stance regarding the relationship between science and metaphysics and its role and importance in scientific activity. There are different views on this old question. We argue that the interaction of metaphysics and science is a complex interaction that can only be defended in the light of a critical approach. In this critical attitude, one should not only pay attention to (...) the role and importance of metaphysics in science but also should have an epistemic humility about his expectations of it. (shrink)

I show that extant attempts to capture and generalize empirical adequacy in terms of partial structures fail. Indeed, the motivations for the generalizations in the partial structures approach are better met by the generalizations via approximation sets developed in “Generalizing Empirical Adequacy I”. Approximation sets also generalize partial structures.

This paper explores the main philosophical approaches of David Hilbert’s theory of proof. Specifically, it is focuses on his ideas regarding logic, the concept of proof, the axiomatic, the concept of truth, metamathematics, the a priori knowledge and the general nature of scientific knowledge. The aim is to show and characterize his epistemological approach on the foundation of knowledge, where logic appears as a guarantee of that foundation. Hilbert supposes that the propositional apriorism, proposed by him to support mathematics, sustains (...) — on its turn — a general method for the treatment of the problem in other areas such as natural sciences. This method is axiomatic. Broadly speaking, we intend to recover and update the Hilbert’s philosophical thinking about the role of logic for scientific knowledge. (shrink)

In this article my primary intention is to engage in a discussion on the inherent constraints of models, taken as models of theories, that reaches beyond the epistemological level. Naturally the paper takes into account the ongoing debate between proponents of the syntactic and the semantic view of theories and that between proponents of the various versions of scientific realism, reaching down to the most fundamental, subjective level of discourse. In this approach, while allowing for a limited discussion of physical (...) and positive science models, I am primarily focused on the structure and ontology of mathematical models, in particular Cohen’s forcing models and to a lesser extent Gödel’s constructible universe, to the extent that these were designed to answer questions bearing on the scope, the capacity and ultimately the ontology of models themselves, therefore influencing in one or the other way the status of models in general. This status, it is argued, is largely defined by the way models subsume a set-theoretical structure whose constraints, reducible to an extra-linguistic level of discourse, may implicitly condition the epistemic status of models as representations of axiomatic theories. In the last section I deal extensively with the inner constraints of theories as subjectively originated in a less technical philosophically oriented discussion with certain prompts from phenomenology. (shrink)

Since their inception in the 1980s, complexity sciences have been described as a revolutionary new domain of research. By describing some of the practices and assumptions of its representatives, the present article shows that this field is an association of subdisciplines laying on existing disciplinary footholds. The general question guiding us here is: On what basis do complexity scientists consider their inquiry methods and results as valuable? To answer it, I describe five “epistemic argumentative regimes,” namely the ways in which (...) complexity scientists argue the credibility of their research, and five “ontological views,” that is the ways in which they interpret the material and formal causes of their study objects and models. Finally, the article proposes the term of “regime of evidence” to designate the specific combination of one ontological view with one or more epistemic argumentative regimes. (shrink)

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)

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. (shrink)

In this paper, a modest version of the Semantic View is motivated as both tenable and potentially fruitful for philosophy of science. An analysis is proposed in which the Semantic View is characterized by three main claims. For each of these claims, a distinction is made between stronger and more modest interpretations. It is argued that the criticisms recently leveled against the Semantic View hold only under the stronger interpretations of these claims. However, if one only commits to the modest (...) interpretation for all the claims, then the view obtained, the Modest Semantic View, is tenable and fruitful for the philosophy of science. (shrink)

Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view of models as being surrogate systems in too literal a sense. In contrast, the epistemic importance of robustness analysis is easy to explicate if modelling is viewed as extended cognition, as inference from assumptions to conclusions. Robustness analysis is about assessing the reliability (...) of our extended inferences, and when our confidence in these inferences changes, so does our confidence in the results. Furthermore, we argue that Sugden’s inductive account relies tacitly on robustness considerations. (shrink)

This paper intends to introduce the three issues of Principia which will appear in a sequel honoring Newton da Costa’s 80th birthday. Instead of presenting the papers one by one, as it is common in presentations such as this one, we have left the papers speak by themselves, and instead we have preferred to present to the Brazilian readers, specialty to our students, some aspects of Newton da Costa’s conception of science and of the scientific activity, grounded on the concept (...) of quasi-truth, which he contributed to develop in a rigorous way. Da Costa is known as one of the founding fathers of paraconsistent logic, but his contributions go also to the foundations of physics, theoretical computation, model theory, algebraic logic, lattice theory, applications of non-classical logics to law and technology, etc. But perhaps his main contribution was to provide a basis for the birth of a school of logic in our country, serving as teacher and inspiring new researchers for generations. It is a pleasure to have had so enthusiastic acceptation from the editors of Principia to organize these volumes. I would like to thank the contributors and the editors of Principia, specially Prof. Cezar Mortari for his help in organizing the issue. (shrink)

In Simulation and Similarity, Michael Weisberg offers a similarity-based account of the model–world relation, which is the relation in virtue of which successful models are successful. Weisberg’s main idea is that models are similar to targets in virtue of sharing features. An important concern about Weisberg’s account is that it remains silent on what it means for models and targets to share features, and consequently on how feature-sharing contributes to models’ epistemic success. I consider three potential ways of concretizing the (...) concept of shared features: as identical, quantitatively sufficiently close, and sufficiently similar features. I argue that each of these concretizations faces significant challenges, leaving unclear how Weisberg’s account substantially contributes to elucidating the relation in virtue of which successful models are successful. Against this background, I outline a pluralistic revision and argue that this revision may not only help Weisberg's account evade several of the problems that I raise, but also offers a novel perspective on the model–world relation more generally. 1Introduction 2Weisberg’s Feature-Sharing Account 3What Is a Shared Feature? 3.1Identity 3.2Sufficient closeness 3.3Sufficient similarity 4Turning Weisberg’s Account ‘Upside Down’ 5Conclusion. (shrink)

The main scientific problems of chemical bonding were solved half a century ago, but adequate philosophical understanding of chemical combination is yet to be achieved. Chemists routinely use important terms ("element," "atom," "molecule," "substance") with more than one meaning. This can lead to misunderstandings. Eliminativists claim that what seems to be a baseball breaking a window is merely the action of "atoms, acting in concert." They argue that statues, baseballs, and similar macroscopic things "do not exist." When macroscopic objects like (...) baseballs move, exceedingly large numbers (1025) of microscopic components coordinate their activities. Understanding how this happens requires attention to the interactions that link parts into larger units. Eliminativists say that everything that truly exists has causal relationships in addition to those of its components—"nonredundant causality." This paper holds that if a number of entities interact in such a way that the effect of that collection on test objects is different than it would have been in the absence of the interaction, then identification of that collection as a single composite agent is warranted, for purposes to which that difference is relevant. Ordinary "chemical substances" (both elementary materials such as dihydrogen and compounds such as water) fulfill this version of the requirement of nonredundant causality. Other sorts of chemical coherences, including chemical dissipative structures (e.g., flames), also fulfill that criterion. All these types of coherences qualify as "substances" (as that term is used in philosophy) even though they are not all "chemical substances.". (shrink)

Several philosophers of science construe models of scientific theories as set-theoretic structures. Some of them moreover claim that models should not be construed as structures in the sense of model theory because the latter are language-dependent. I argue that if we are ready to construe models as set-theoretic structures (strict semantic view), we could equally well construe them as model-theoretic structures of higher-order logic (liberal semantic view). I show that every family of set-theoretic structures has an associated language of higher-order (...) logic and an up to signature isomorphism unique model-theoretic counterpart, which is able to serve the same purposes. This allows to carry over every syntactic criterion of equivalence for theories in the sense of the liberal semantic view to theories in the sense of the strict semantic view. Taken together, these results suggest that the recent dispute about the semantic view and its relation to the syntactic view can be resolved. (shrink)

According to the semantic view of scientific theories, theories are classes of models. I show that this view -- if taken seriously as a formal explication -- leads to absurdities. In particular, this view equates theories that are truly distinct, and it distinguishes theories that are truly equivalent. Furthermore, the semantic view lacks the resources to explicate interesting theoretical relations, such as embeddability of one theory into another. The untenability of the semantic view -- as currently formulated -- threatens to (...) undermine scientific structuralism. (shrink)

Work on analogy has been done from a number of disciplinary perspectives throughout the history of Western thought. This work is a multidisciplinary guide to theorizing about analogy. It contains 1,406 references, primarily to journal articles and monographs, and primarily to English language material. classical through to contemporary sources are included. The work is classified into eight different sections (with a number of subsections). A brief introduction to each section is provided. Keywords and key expressions of importance to research on (...) analogy are discussed in the introductory material. Electronic resources for conducting research on analogy are listed as well. (shrink)

‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a structural (...) analysis of simulation developed in previous work to provide an evaluative account of the variety of ways in which simulations do fail. We expand the structural analysis in terms of the relationship between a simulation and its real-world target emphasizing the important role of aspects intended to correspond and also those specifically intended not to correspond to reality. The result is an outline both of the ways in which simulations can fail and the scientific importance of those various forms of failure. (shrink)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I first (...) contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)