This paper distinguishes between causal isolation robustness analysis and independent determination robustness analysis and suggests that the triangulation of the results of different epistemic means or activities serves different functions in them. Circadian clock research is presented as a case of causal isolation robustness analysis: in this field researchers made use of the notion of robustness to isolate the assumed mechanism behind the circadian rhythm. However, in contrast to the earlier philosophical case studies on causal isolation robustness analysis (Weisberg and (...) Reisman in Philos Sci 75:106–131, 2008 ; Kuorikoski et al. in Br J Philos Sci 61:541–567, 2010 ), robustness analysis in the circadian clock research did not remain in the level of mathematical modeling, but it combined it with experimentation on model organisms and a new type of model, a synthetic model. (shrink)

Chemistry is the study of the structure and transformation of matter. When Aristotle founded the field in the 4th century BCE, his conceptual grasp of the nature of matter was tailored to accommodate a relatively simple range of observable phenomena. In the 21st century, chemistry has become the largest scientific discipline, producing over half a million publications a year ranging from direct empirical investigations to substantial theoretical work. However, the specialized interest in the conceptual issues arising in chemistry, hereafter Philosophy (...) of Chemistry, is a relatively recent addition to philosophy of science. Philosophy of chemistry has two major parts. In the first, conceptual issues arising within chemistry are carefully articulated and analyzed. Such questions which are internal to chemistry include the nature of substance, atomism, the chemical bond, and synthesis. In the second, traditional topics in philosophy of science such as realism, reduction, explanation, confirmation, and modeling are taken up within the context of chemistry. (shrink)

Here, I take alethic views of understanding to be all views that hold that whether an explanation is true or false matters for whether that explanation provides understanding. I then argue that there is (as yet) no naturalistic defence of alethic views of understanding in cognitive science, because there is no agreement about the correct descriptions of the content of cognitive scientific explanations. I use this claim to argue for the provisional acceptance of afactivism in cognitive science, which is the (...) view that the truth or falsity of an explanation of cognition is irrelevant to whether that explanation provides understanding. I conclude by discussing the relation between understanding in cognitive science and understanding in other domains. (shrink)

This paper defends the viability of de-idealization strategies in economic modeling against recent criticism. De-idealization occurs when an idealized assumption of a theoretical model is replaced with a more realistic one. Recently, some scholars have raised objections against the possibility or fruitfulness of de-idealizing economic models, suggesting that economists do not employ this kind of strategy. We present a detailed case study from the theory of industrial organization, discussing three different models, two of which can be construed as de-idealized versions (...) of the first. We conclude that recent pessimism about de-idealization in economics is largely unfounded, and that de-idealization strategies are not only possible but also widely employed in economics. (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)

The Darwinian theory of evolution is itself evolving and this book presents the details of the core of modern Darwinism and its latest developmental directions. The authors present current scientific work addressing theoretical problems and challenges in four sections, beginning with the concepts of evolution theory, its processes of variation, heredity, selection, adaptation and function, and its patterns of character, species, descent and life. The second part of this book scrutinizes Darwinism in the philosophy of science and its usefulness in (...) understanding ecosystems, whilst the third section deals with its application in disciplines beyond the biological sciences, including evolutionary psychology and evolutionary economics, Darwinian morality and phylolinguistics. The final section addresses anti-Darwinism, the creationist view and issues around teaching evolution in secondary schools. The reader learns how current experimental biology is opening important perspectives on the sources of variation, and thus of the very power of natural selection. This work examines numerous examples of the extension of the principle of natural selection and provides the opportunity to critically reflect on a rich theory, on the methodological rigour that presides in its extensions and exportations, and on the necessity to measure its advantages and also its limits. Scholars interested in modern Darwinism and scientific research, its concepts, research programs and controversies will find this book an excellent read, and those considering how Darwinism might evolve, how it can apply to the human sciences and other disciplines beyond its origins will find it particularly valuable. Originally produced in French (Les Mondes Darwiniens), the scope and usefulness of the book have led to the production of this English text, to reach a wider audience. This book is a milestone in the impressive penetration by Francophone scholars into the world of Darwinian science, its historiography and philosophy over the last two decades. Alex Rosenberg, R. Taylor Cole Professor of Philosophy, Duke University Until now this useful and comprehensive handbook has only been available to francophones. Thanks to this invaluable new translation, this collection of insightful and original essays can reach the global audience it deserves. Tim Lewens, University of Cambridge. (shrink)

Robert Batterman and others have argued that certain idealizing explanations have an asymptotic form: they account for a state of affairs or behavior by showing that it emerges “in the limit”. Asymptotic idealizations are interesting in many ways, but is there anything special about them as idealizations? To understand their role in science, must we augment our philosophical theories of idealization? This paper uses simple examples of asymptotic idealization in population genetics to argue for an affirmative answer and proposes a (...) general schema for asymptotic idealization, drawing on insights from Batterman’s treatment and from John Norton’s subsequent critique. (shrink)

In his 1966 paper “The Strategy of model-building in Population Biology”, Richard Levins argues that no single model in population biology can be maximally realistic, precise and general at the same time. This is because these desirable model properties trade-off against one another. Recently, philosophers have developed Levins’ claims, arguing that trade-offs between these desiderata are generated by practical limitations on scientists, or due to formal aspects of models and how they represent the world. However this project is not complete. (...) The trade-offs discussed by Levins had a noticeable effect on modelling in population biology, but not on other sciences. This raises questions regarding why such a difference holds. I claim that in order to explain this finding, we must pay due attention to the properties of the systems, or targets modelled by the different branches of science.Keywords: Trade-offs; Population biology; Scientific models; Generality; Precision. (shrink)

Psychological essentialism is a folk theory characterized by the belief that a causal internal essence or force gives rise to the common outward behaviors or attributes of a category's members. In two studies, we investigated whether 4- to 7-year-old children evidenced essentialist reasoning about heart transplants by asking them to predict whether trading hearts with an individual would cause them to take on the donor's attributes. Control conditions asked children to consider the effects of trading money with an individual. Results (...) indicated that children reasoned according to essentialism, predicting more transfer of attributes in the transplant condition versus the non-bodily money control. Children also endorsed essentialist transfer of attributes even when they did not believe that a transplant would change the recipient's category membership. This finding runs counter to predictions from a strong interpretation of the “minimalist” position, an alternative to essentialism. (shrink)

In this paper we consider mechanistic explanations for biologic malfunctions. Drawing on Lipton’s work on difference making, we offer three reasons why one should distinguish i) mechanistic features that only make a difference to the malfunction one aims to explain, from ii) features that make a difference to both the malfunction and normal functioning. Recognition of the distinction is important for a) repair purposes, b) mechanism discovery, and c) understanding. This analysis extends current mechanistic thinking, which fails to appreciate the (...) distinction. We illustrate our contribution with a case on sleeping disorders as arising from disruptions of circadian rhythms. (shrink)

Philosophers of science have developed several accounts of how consideration of scientific models can prompt learning about real-world targets. In recent years, various authors advocated the thesis that consideration of so-called minimal models can prompt learning about such targets. In this paper, I draw on the philosophical literature on scientific modelling and on widely cited illustrations from economics and biology to argue that this thesis fails to withstand scrutiny. More specifically, I criticize leading proponents of such thesis for failing to (...) explicate in virtue of what properties or features minimal models supposedly prompt learning and for substantially overstating the epistemic import of minimal models. I then examine and refute several arguments one may put forward to demonstrate that consideration of minimal models can prompt learning about real-world targets. In doing so, I illustrate the implications of my critique for the wider debate on the epistemology of scientific modelling. (shrink)

In scientific modeling, continuum idealizations bridge scales but at the cost of fundamentally misrepresenting the microstructure of the system. This engenders a mystery. If continuum idealizations are dispensable in principle, this de-problematizes their representational inaccuracy, since continuum properties reduce to lower-scale properties, but the mystery of how this reduction could be carried out endures. Alternatively, if continuum idealizations are indispensable in principle, this is consistent with their explanatory and predictive success but renders their representational inaccuracy mysterious. I argue for a (...) deflationary solution, enlisting the applied scientific method of upscaling as demonstrated in a case from soil hydrology. (shrink)

In 1959, mathematician Mark Kac introduced a model, called the Kac ring, in order to elucidate the classical solution of Boltzmann to the problem of macroscopic irreversibility. However, the model is far from being a realistic representation of something. How can it be of any help here? In philosophy of science, it is often argued that models can provide explanations of the phenomenon they are said to approximate, in virtue of the truth they contain, and in spite of the idealisations (...) they are made of. On this view, idealisations are not supposed to contribute to any explaining, and should not affect the global representational function of the model. But the Kac ring is a toy model that is only made of idealisations, and is still used trustworthily to understand the treatment of irreversible phenomena in statistical mechanics. In the paper, my aim is to argue that each idealisation ingeniously designed by the mathematician maintains the representational function of the Kac ring with the general properties of macroscopic irreversibility under scrutiny. Such an active role of idealisations in the representing has so far been overlooked and reflects the art of modelling. (shrink)

Facts about explanation are often taken as a guide to facts about metaphysics. Such inferences from explanation to metaphysics typically rely on two elements: explanatory realism, the view that it is a characteristic and necessary aspect of explanation to give information about metaphysical structure, and a backing model of explanation, according to which explanations are backed by supporting relations, such as causation. Combining explanatory realism with a backing model permits conclusions about metaphysics to follow straightforwardly from facts about explanation, and (...) those who endorse backing models of explanation have typically endorsed explanatory realism. In light of recent critiques of explanatory realism, in this paper I explore the prospects for a backing model without explanatory realism. I articulate a non-realist backing model and argue that this model can satisfy most of the motivations for a realist backing model, and that it can also play a central and illuminating role in the practice of metaphysics. (shrink)

Scientific models are frequently discussed in philosophy of science. A great deal of the discussion is centred on approximation, idealisation, and on how these models achieve their representational function. Despite the importance, distinct nature, and high presence of toy models, they have received little attention from philosophers. This paper hopes to remedy this situation. It aims to elevate the status of toy models: by distinguishing them from approximations and idealisations, by highlighting and elaborating on several ways the Kac ring, a (...) simple statistical mechanical model, is used as a toy model, and by explaining why toy models can be used to successfully carry out important work without performing a representational function. (shrink)

In this article we defend the inferential view of scientific models and idealisation. Models are seen as “inferential prostheses” (instruments for surrogative reasoning) construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure (also analysed in inferential terms). The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to (...) correct and improve our models. This provides us with an argument against Sugden’s account of credible models: the likelihood or realisticness (their “credibility”) is not always a good measure of their acceptability. As opposed to “credibility” we propose the notion of “enlightening”, which is the capacity of giving us understanding in the sense of an inferential ability. (shrink)

This paper supports the literature which argues that derivational robustness can have epistemic import in highly idealized economic models. The defense is based on a particular example from mathematical economic theory, the dynamic Walrasian general equilibrium model. It is argued that derivational robustness first increased and later decreased the credibility of the Walrasian model. The example demonstrates that derivational robustness correctly describes the practices of a particular group of influential economic theorists and provides support for the arguments of philosophers who (...) have offered a general epistemic justification of such practices. (shrink)

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...) related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

Current philosophical theorizing about technical functions is mainly focused on specifying conditions under which agents are justified in ascribing functions to technical artifacts. Yet, assessing the precise explanatory relevance of such function ascriptions is, by and large, a neglected topic in the philosophy of technical artifacts and technical functions. We assess the explanatory utility of ascriptions of technical functions in the following three explanation-seeking contexts: why was artifact x produced?, why does artifact x not have the expected capacity to ϕ?, (...) how does artifact x realize its capacity to ϕ? We argue that while function ascriptions serve a mere heuristic role in the first context, they have substantial explanatory leverage in the second and third context. In addition, we assess the relevance of function ascriptions in the context of engineering redesign. Here, function ascriptions also play a relevant role: they enable normative statements of the sort that component b functions better than component a. We unpack these claims by considering philosophical theories of technical functions, in particular the ICE theory, and engineering work on function ascription and explanation. We close the paper by relating our analysis to current debates on the explanatory power of mechanistic vis-à-vis functional explanations. (shrink)

. In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth’s climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model. I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (...) and climate dynamic exploration. MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. (shrink)

Turing patterns are a class of minimal mathematical models that have been used to discover and conceptualize certain abstract features of early biological development. This paper examines a range of these minimal models in order to articulate and elaborate a philosophical analysis of their epistemic uses. It is argued that minimal mathematical models aid in structuring the epistemic practices of biology by providing precise descriptions of the quantitative relations between various features of the complex systems, generating novel predictions that can (...) be compared with experimental data, promoting theory exploration, and acting as constitutive parts of empirically adequate explanations of naturally occurring phenomena, such as biological pattern formation. Focusing on the roles that minimal model explanations play in science motivates the adoption of a broader diachronic view of scientific explanation. (shrink)

. Philosophers and scientists often cite ontic factors when explaining the methods and success of scientific inquiry. That is, the adoption of a method or approach is explained in reference to the kind of system in which the scientist is interested: these are explanations of why scientists do what they do, that appeal to properties of their target systems. We present a framework for understanding such “Opticks to his Principia. Newton’s optical work is largely experiment-driven, while the Principia is primarily (...) mathematical, so usually, each work is taken to exemplify a different kind of science. However, Newton himself often presented them in terms of a largely consistent method. We use our framework to articulate an original and plausible position: that the differences between the Opticks and the Principia are due to the kinds of systems targeted. That is, we provide an ontic-driven explanation of methodological differences. We suspect that ontic factors should have a more prominent role in historical explanations of scientific method and development. (shrink)

Some recent arguments for paternalist government interventions have been based in empirical results in psychology and behavioral economics that would seem to show that adult human beings are far removed from the ideals of rationality presupposed by much of philosophical and economic theory. In this paper it is argued that we need to move to a different conception of human decision-making competence than the one that lies behind that common line of philosophical and economic thinking, and which actually still lies (...) in the background of some of these recent approaches to paternalist interventions. An alternative picture of human decision-making competence is outlined and four criteria for identifying areas where paternalist interventions have a basic moral and political legitimacy are then identified on the basis of this picture. (shrink)

This paper examines two recent approaches to the nature and functioning of economic models: models as isolating representations and models as credible constructions. The isolationist view conceives of economic models as surrogate systems that isolate some of the causal mechanisms or tendencies of their respective target systems, while the constructionist approach treats them rather like pure constructions or fictional entities that nevertheless license different kinds of inferences. I will argue that whereas the isolationist view is still tied to the representationalist (...) understanding of models that takes the model-target dyad as the basic unit of analysis, the constructionist perspective can better accommodate the way we actually acquire knowledge through them. Using the example of Tobin’s ultra-Keynesian model I will show how many of the epistemic characteristics of modelling tend to go unrecognised if too much focus is placed on the model-target dyad. (shrink)

This article examines the issue of whether consideration of so-called minimal models can prompt learning about real-world targets. Using a widely cited example as a test case, it argues against the increasingly popular view that consideration of minimal models can prompt learning about such targets. The article criticizes influential defenses of this view for failing to explicate by virtue of what properties or features minimal models supposedly prompt learning. It then argues that consideration of minimal models cannot prompt learning about (...) real-world targets unless one supplements these models with additional information or presuppositions concerning such targets. (shrink)

I argue that fictional models, construed as models that misrepresent certain ontological aspects of their target systems, can nevertheless explain why the latter exhibit certain behaviour. They can do this by accurately representing whatever it is that that behaviour counterfactually depends on. However, we should be sufficiently sensitive to different explanatory questions, i.e., ‘why does certain behaviour occur?’ versus ‘why does the counterfactual dependency invoked to answer that question actually hold?’. With this distinction in mind, I argue that whilst fictional (...) models can answer the first sort of question, they do so in an unmysterious way. Moreover, I claim that the second question poses a dilemma for the defender of the idea that fictions can explain: either these models cannot answer these sorts of explanatory questions, precisely because they are fictional; or they can, but in a way that requires reinterpreting them such that they end up accurately representing the ontological basis of the counterfactual dependency, i.e., reinterpreting them so as to rob them of their fictional status. Thus, the existence of explanatory fictions does not put pressure on the idea that accurate representation of some aspect of a target system is a necessary condition on explaining that aspect. (shrink)

On standard accounts of scientific theorizing, the role of idealizations is to facilitate the analysis of some real world system by employing a simplified representation of the target system, raising the obvious worry about how reliable knowledge can be obtained from inaccurate descriptions. The idealizations involved in the Aharonov–Bohm effect do not, it is claimed, fit this paradigm; rather the target system is a fictional system characterized by features that, though physically possible, are not realized in the actual world. The (...) point of studying such a fictional system is to understand the foundations of quantum mechanics and how its predictions depart from those of classical mechanics. The original worry about the use of idealizations is replaced by a new one; namely, how can actual world experiments serve to confirm the AB effect if it concerns the behavior of a fictional system? Struggle with this issue helps to account for the fact that almost three decades elapsed before a consensus emerged that the predicted AB effect had received solid experimental support. Standard accounts of idealizations tout the role they play in making tractable the analysis of the target system; by contrast, the idealizations involved in the AB effect make its analysis both conceptually and mathematically challenging. The idealizations required for the AB effect are also responsible for the existence of unitarily inequivalent representations of the canonical commutation relations and of the current algebra, representations which an observer confined to the electron’s configuration space could invoke to ‘explain’ AB-type effect without the need to posit a hidden magnetic field. The goal of this paper is to bring to the attention of the philosophers of science these and other aspects of the AB effect which are neglected or inadequately treated in literature. (shrink)

In this paper I argue, first, that ecologists have routinely treated humans—or more specifically, anthropogenic causal factors—as disturbing conditions. I define disturbing conditions as exogenous variables, variables “outside” a model, that when present in a target system, inhibit the applicability or accuracy of the model. This treatment is surprising given that humans play a dominant role in many ecosystems and definitions of ecology contain no fundamental distinction between human and natural. Second, I argue that the treatment of humans as disturbing (...) conditions is an idealization: since it is, and has long been, known that humans are pervasive, this treatment amounts to an intentionally introduced theoretical distortion. Finally, characterizing this treatment as idealization forces us to confront the question of its justification, and so, drawing on three different kinds of idealization, I evaluate how this treatment may be justified. (shrink)

The multiple-models approach, which has its origins in Levins’s work, is gaining broader acceptance among philosophers. Levins asserted that there is a trade-off between modeling desiderata, which justified the multiple-models approach through two separate justificatory paths. Some attention has been paid to the trade-off thesis, culminating in a paper by Matthewson and Weisberg. However, no attention has been paid to how the trade-off is supposed to justify the multiple-models approach. I argue that a trade-off between generality and precision cannot support (...) one of Levins’s justificatory paths, and I consider what that might mean for the multiple-models approach. (shrink)

Bruineberg and colleagues' critique of Friston blankets relies on what we call the “literalist fallacy”: the assumption that in order for Friston blankets to represent real boundaries, biological systems must literally possess or instantiate Markov blankets. We argue that it is important to distinguish a realist view of Friston blankets from the literalist view of Bruineberg and colleagues’ critique.

This undergraduate textbook introduces some fundamental issues in philosophy of science for students of philosophy and science students. The book is divided into two parts. Part 1 deals with knowledge and values. Chap. 1 presents the classical conception of knowledge as initiated by the ancient Greeks and elaborated during the development of science, introducing the central concepts of truth, belief and justification. Aspects of the quest for objectivity are taken up in the following two chapters. Moral issues are broached in (...) Chap. 4, which discusses some aspects of the use and abuse of science, taking up the responsibilities of scientists in properly conducting their business and decision-makers in their concerns with the import of science for society. Part 2 contrasts the view of scientific progress as the rejecting of old hypotheses and theories and replacing them with new ones, represented by Karl Popper, with the conception of progress as accumulating knowledge, saving as much as possible from older theories, represented by Pierre Duhem. A concluding chapter defends the natural attitude of taking the theories of modern science to be literally true, i.e. realism, in the face of arguments drawn partly from the history of scientific progress in criticism of this stance. (shrink)

Every model leaves out or distorts some factors that are causally connected to its target phenomenon -- the phenomenon that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to describe fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomenon. There are several (...) ways to understand differencemaking. This paper advances a view as to which is the most relevant to the forecaster and the decision-maker. It turns out that the right notion of differencemaking for thinking about idealization in prediction is also the right notion for thinking about idealization in explanation; this suggests a carefully circumscribed version of Hempel's famous thesis that there is a symmetry between explanation and prediction. (shrink)

Empirical agreement is often used as an important criterion when assessing the validity of scientific models. However, it is by no means a sufficient criterion as a model can be so adjusted as to fit available data even though it is based on hypotheses whose plausibility is known to be questionable. Our aim in this paper is to investigate into the uses of empirical agreement within the process of model validation.

Choice modellers are frequently criticized for failing to provide accurate representations of the neuro-psychological substrates of decisions. Several authors maintain that recent neuro-psychological findings enable choice modellers to overcome this alleged shortcoming. Some advocate a realistic interpretation of neuro-psychological models of choice, according to which these models posit sub-personal entities with specific neuro-psychological counterparts and characterize those entities accurately. In this article, I articulate and defend three complementary arguments to demonstrate that, contrary to emerging consensus, even the best available neuro-psychological (...) models of choice cannot be justifiably given this realistic interpretation. Moreover, I explicate what challenges will continue to hamper neuro-psychological modellers’ attempts to substantiate a realistic interpretation of their models. In doing so, I draw on the literature on scientific modelling to advance the ongoing philosophical discussion concerning the ontological status of the sub-personal entities posited in distinct decision sciences, the resemblance relations that supposedly connect choice models and the neuro-psychological substrates of decisions, and what conditions these models must satisfy to be regarded as realistic representations of their target systems. (shrink)

Phylogenetic trees are meant to represent the genealogical history of life and apparently derive their justification from the existence of the tree of life and the fact that evolutionary processes are treelike. However, there are a number of problems for these assumptions. Here it is argued that once we understand the important role that phylogenetic trees play as models that contain idealizations, we can accept these criticisms and deny the reality of the tree while justifying the continued use of trees (...) in phylogenetic theory and preserving nearly all of what defenders of trees have called the “importance of tree thinking.”. (shrink)

This paper combines naturalized metaphysics and a philosophical reflection on a recently evolving interdisciplinary branch of quantum chemistry, ab initio molecular dynamics. Bridging the gaps among chemistry, physics, and computer science, this cutting-edge research field explores the structure and dynamics of complex molecular many-body systems through computer simulations. These simulations are allegedly crafted solely by the laws of fundamental physics, and are explicitly designed to capture nature as closely as possible. The models and algorithms employed, however, involve many approximations and (...) significant degrees of idealization of their target systems. Therefore, for philosophers of science the pivotal question of whether relying only on the fundamental laws of physics supports a reductionist or realist stance arises. One conceivable answer to this question is that the irreducible approximations and idealizations support rather anti-realist positions. After reviewing an influential attitude in the philosophy of computer simulations and the debate concerning scientific realism, I offer a fair interpretation of such ab initio modelling in quantum chemistry within a naturalistic metaphysical framework that gives rise to a specific type of ontic structural realism. (shrink)

In this paper, by means of a novel use of insights from the literature on scientific modelling, I will argue in favour of an instrumentalist approach to the models that are crucially involved in the study of adaptive systems within the Free-Energy Principle (FEP) framework. I will begin (§2) by offering a general, informal characterisation of FEP. Then (§3), I will argue that the models involved in FEP-theorising are plausibly intended to be isomorphic to their targets. This will allow (§4) (...) to turn the criticisms moved against isomorphism-based accounts of representation towards the FEP modelling practice. Since failure to establish an isomorphism between model and target would result in the former’s failure to represent the latter, and given that it is highly unlikely that FEP-models are ever isomorphic to their targets, maintaining that FEP-models represent their targets as they are, in a realist sense, is unwarranted. Finally (§5), I will consider what implications my argument in favour of an instrumentalist reading of FEP-models has for attempts at making use of the FEP to elaborate an account of what cognition exactly is. My conclusion is that we should not dismiss FEP-based accounts of cognition, as they would still be informative and would further our understanding of the nature of cognition. Nonetheless, the prospects of settling the philosophical debates that sparked the interest in having a “mark of the cognitive” are not good. (shrink)

In this paper, I show how idealizations contribute to social activities in science, such as the recruitment of experts to a research project. These contributions have not been explicitly discussed by recent philosophical accounts of scientific idealization. These accounts have focused on how idealizations influence activities like scientific theorization, explanation, and modeling. Other accounts focus on how idealizations influence policy-making and science communication. I expand these accounts by exploring the uses of idealized phylogenetic trees in science. Trees are not only (...) useful for improving our understanding and public communication of evolutionary history, but they also help with the organization of laboratories and collaboration among scientists. Attending to the relation between idealizations and these social practices in science matters. It can help us understand why idealized models become entrenched and why certain social practices change over time. (shrink)

Idealization is ubiquitous in human cognition, and so is the inclination to be puzzled by it: what to make of ideal gas, infinitely large populations, homo economicus, perfectly just society, known to violate matters of fact? This is apparent in social science theorizing, recent philosophy of science analyzing scientific modeling, and the debate over ideal and non-ideal theory in political philosophy. I will offer a set of concepts and principles to improve transparency about the precise contents of idealizations and their (...) distinct functions. (shrink)

There are many important connections between epistemic justification and moral justification. A recent example of such connections is offered by Sosa’s AAA model for the normative evaluation of epistemic performances. In order to count as knowledge, a belief has to be Accurate in attaining the truth, the subject has to be Adroit or competent for such task, and the belief has to be Apt in the sense that the accuracy of the belief has to manifest the adroitness of the subject. (...) In addition, full knowledge would require full-aptness, which is obtained when the aim of aptness is achieved in an apt way. We have here a clear definition of the model. But the application of the model to particular performances is not so clear. Both the identification of a particular performance as a case of epistemic performance and the assessment of its aptness, or full-aptness, are strongly relative to intentional descriptions paying attention to contextual factors and personal aspects. Here is where we can find close proximity with ethics. An epistemic analog to the ethical problem of double effects appears. In our paper, we analyze that problem in detail, extracting some consequences. (shrink)

The question of how to frame agential preferences in economics finds one caught between Scylla and Charybdis. If preferences are framed in as minimal and deflationary a manner as revealed preference theory recommends, the theory falls prey to objections about its predictiveness and explanatory power. Alternatively, if too many cognitive and causal intricacies are incorporated into the preference concept, revealed preference models will violate pragmatic norms of model construction, surrendering model simplicity and generality. This paper charts a middle course, arguing (...) that the path to salvation lies through an understanding of revealed preference models as program explanations. (shrink)

Proponents of neuroeconomics often argue that better knowledge of the human neural architecture enables economists to improve standard models of choice. In their view, these improvements provide compelling reasons to use neural findings in constructing and evaluating economic models. In a recent article, I criticized this view by pointing to the trade-offs between the modeling desiderata valued by neuroeconomists and other economists, respectively. The present article complements my earlier critique by focusing on three modeling desiderata that figure prominently in economic (...) and neuroeconomic modeling. For each desideratum, I examine findings that neuroeconomists deem to be especially relevant for economists and argue that neuroeconomists have failed to substantiate their calls to use these findings in constructing and evaluating economic models. In doing so, I identify methodological and evidential constraints that will continue to hinder neuroeconomists’ attempts to improve such models. Moreover, I draw on the literature on scientific modeling to advance the ongoing philosophical discussion regarding the prospects of interdisciplinary models of choice. (shrink)

In a recent article in this Journal, Fumagalli argues that economists are provisionally justified in resisting prominent calls to integrate neural variables into economic models of choice. In other articles, various authors engage with Fumagalli’s argument and try to substantiate three often-made claims concerning neuroeconomic modelling. First, the benefits derivable from neurally informing some economic models of choice do not involve significant tractability costs. Second, neuroeconomic modelling is best understood within Marr’s three-level of analysis framework for information-processing systems. And third, (...) neural findings enable choice modellers to confirm the causal relevance of variables posited by competing economic models, identify causally relevant variables overlooked by existing models, and explain observed behavioural variability better than standard economic models. In this paper, I critically examine these three claims and respond to the related criticisms of Fumagalli’s argument. Moreover, I qualify and extend Fumagalli’s account of how trade-offs between distinct modelling desiderata hamper neuroeconomists’ attempts to improve economic models of choice. I then draw on influential neuroeconomic studies to argue that even the putatively best available neural findings fail to substantiate current calls for a neural enrichment of economic models. (shrink)

Karl Popper (2002) once instructed a group of physics students to carefully write down what they observed. Popper relates that the students asked what he wanted them to observe and said that the sole instruction to “observe” was absurd. This story motivated Popper’s claim that, especially in science: Observation is always selective. It needs a chosen object, a definite task, an interest, a point of view, a problem. And its description presupposes a descriptive language . . . , which in (...) its turn presupposes interests, points of view, and problems. (2002, p. 62) In Discovering the Human Connectome, the problem is how the brain works, and from Olaf Sporns’ point of view, the brain can be understood as a network (p. ix). One of Sporns’ main goals in his previous book (2011) was to introduce neuroscientists to the theory and methods of network science. That book was Sporns’ attempt to answer the “how?” questions of integrating the methods of network science with neuroscience. The current book builds on the previous one and attempts to answer more theoretical questions such as why network science is an appropriate framework for investigating the brain. (shrink)