Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models concerns what the epistemic goal of toy modelling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this article is to precisely articulate and to defend this (...) claim. In particular, we will distinguish between autonomous and embedded toy models, and then argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied. _1_ Introduction _2_ Embedded and Autonomous Toy Models _2.1_ Embedded toy models _2.2_ Autonomous toy models _2.3_ Qualification _3_ A Theory of Understanding for Toy Models _3.1_ Preliminaries and requirements _3.2_ The refined simple view _4_ Two Kinds of Understanding with Toy Models _4.1_ Embedded toy models and how-actually understanding _4.2_ Against a how-actually interpretation of all autonomous toy models _4.3_ The how-possibly interpretation of some autonomous toy models _5_ Conclusion. (shrink)

In this paper we explore the constraints that our preferred account of scientific representation places on the ontology of scientific models. Pace the Direct Representation view associated with Arnon Levy and Adam Toon we argue that scientific models should be thought of as imagined systems, and clarify the relationship between imagination and representation.

Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...) precisely articulate and to defend this claim. In particular, we will distinguish between autonomous and embedded toy models, and, then, argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied. (shrink)

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell (...) which idealisations are truly harmful. (shrink)

Science provides us with representations of atoms, elementary particles, polymers, populations, genetic trees, economies, rational decisions, aeroplanes, earthquakes, forest fires, irrigation systems, and the world’s climate. It's through these representations that we learn about the world. This entry explores various different accounts of scientific representation, with a particular focus on how scientific models represent their target systems. As philosophers of science are increasingly acknowledging the importance, if not the primacy, of scientific models as representational units of science, it's important to (...) stress that how they represent plays a fundamental role in how we are to answer other questions in the philosophy of science. This entry begins by disentangling ‘the’ problem of scientific representation, before critically evaluating the current options available in the literature. (shrink)

This paper presents an artifactual approach to models that also addresses their fictional features. It discusses first the imaginary accounts of models and fiction that set model descriptions apart from imagined-objects, concentrating on the latter :251–268, 2010; Frigg and Nguyen in The Monist 99:225–242, 2016; Godfrey-Smith in Biol Philos 21:725–740, 2006; Philos Stud 143:101–116, 2009). While the imaginary approaches accommodate surrogative reasoning as an important characteristic of scientific modeling, they simultaneously raise difficult questions concerning how the imagined entities are related (...) to actual representational tools, and coordinated among different scientists, and with real-world phenomena. The artifactual account focuses, in contrast, on the culturally established external representational tools that enable, embody, and extend scientific imagination and reasoning. While there are commonalities between models and fictions, it is argued that the focus should be on the fictional uses of models rather than considering models as fictions. (shrink)

Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides a selective and critical overview (...) of the available strategies for solving this puzzle, mainly focusing on idealized models and how they explain. (shrink)

The epistemic value of models has traditionally been approached from a representational perspective. This paper argues that the artifactual approach evades the problem of accounting for representation and better accommodates the modal dimension of modeling. From an artifactual perspective, models are viewed as erotetic vehicles constrained by their construction and available representational tools. The modal dimension of modeling is approached through two case studies. The first portrays mathematical modeling in economics, while the other discusses the modeling practice of synthetic biology, (...) which exploits and combines models in various modes and media. Neither model intends to represent any actual target system. Rather, they are constructed to study possible mechanisms through the construction of a model system with built-in dependencies. (shrink)

Drawing on ‘interpretational’ accounts of scientific representation, I argue that the use of so-called ‘toy models’ provides no particular philosophical puzzle. More specifically; I argue that once one gives up the idea that models are accurate representations of their targets only if they are appropriately similar, then simple and highly idealized models can be accurate in the same way that more complex models can be. Their differences turn on trading precision for generality, but, if they are appropriately interpreted, toy models (...) should nevertheless be considered accurate representations. A corollary of my discussion is a novel way of thinking about idealization more generally: idealized models may distort features of their targets, but they needn’t misrepresent them. (shrink)

We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that (...) our account could be fruitfully used in appraising other models in philosophy and science. (shrink)

This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the features of their real-world target system. My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.

Science is often said to aim at truth. And much of science is heavily dependent on the construction and use of theoretical models. But the notion of model has an uneasy relationship with that of truth. -/- Not so long ago, many philosophers held the view that theoretical models are different from theories in that they are not accompanied by any ontological commitments or presumptions of truth, whereas theories are (e.g. Achinstein 1964). More recently, some have thought that models are (...) not truth-valued at all, but truth-valued claims can be made about similarity relations between models and real systems (e.g. Giere 1988). Others suggest that models are instruments that can be used for attaining truths, for example that models are false means for true theories (e.g. Wimsatt 2007). At the same time, philosophers and others keep talking about models being ‘correct’ and ‘incorrect’, ‘accurate’ and ‘inaccurate’ or getting facts ‘right’ or ‘wrong’. Among practicing scientists, one can find both the notion of a ‘true model’ and the idea that ‘it is in the nature of models that they are false’. There seems to be enough variety of views and confusion around them to invite a little bit of further investigation (see also Mäki 1992, 1994, 2004, 2009a,b,c). (shrink)

Veritism, the position that truth is necessary for epistemic acceptability, seems to be in tension with the observation that much of our best science is not, strictly speaking, true when interpreted literally. This generates a paradox: truth is necessary for epistemic acceptability; the claims of science have to be taken literally; much of what science produces is not literally true and yet it is acceptable. We frame Elgin’s project in True Enough as being motivated by, and offering a particular resolution (...) to, this paradox. We discuss the paradox with a focus on scientific models and argue that there is another resolution available which is compatible with retaining veritism: rejecting the idea that scientific models should be interpreted literally. (shrink)

My philosophical intuitions are those of a scientific realist. In addition to being realist in its philosophical outlook, my philosophy of economics also aspires to be realistic in the sense of being descriptively adequate, or at least normatively non-utopian, about economics as a scientific discipline. The special challenge my philosophy of economics must meet is to provide a scientific realist account that is realistic of a discipline that deals with a complex subject matter and operates with highly unrealistic models. Unrealisticness (...) in economic models must not constitute an obstacle to realism about those models. What follows is a selective and somewhat abstract summary of my thinking about economics, outlined from two perspectives: first historical and autobiographical, then systematic and comparative. The first angle helps understand motives and trajectories of ideas against their backgrounds in intellectual history. My story turns out to have both unique and generalizable aspects. The second approach outlines some of the key concepts and arguments as well as their interrelations in my philosophy of economics, with occasional comparisons to other views. More space will be devoted to this second perspective than to the first. (shrink)

The concept of linguistic infinity has had a central role to play in foundational debates within theoretical linguistics since its more formal inception in the mid-twentieth century. The conceptualist tradition, marshalled in by Chomsky and others, holds that infinity is a core explanandum and a link to the formal sciences. Realism/Platonism takes this further to argue that linguistics is in fact a formal science with an abstract ontology. In this paper, I argue that a central misconstrual of formal apparatus of (...) recursive operations such as the set-theoretic operation merge has led to a mathematisation of the object of inquiry, producing a strong analogy with discrete mathematics and especially arithmetic. The main product of this error has been the assumption that natural, like some formal, languages are discretely infinite. I will offer an alternative means of capturing the insights and observations related to this posit in terms of scientific modelling. My chief aim will be to draw from the larger philosophy of science literature in order to offer a position of grammars as models compatible with various foundational interpretations of linguistics while being informed by contemporary ideas on scientific modelling for the natural and social sciences. (shrink)

This is a comprehensive anthology of works concerning the nature of economics as a science, including classic texts and essays exploring specific branches and schools of economics. Apart from the classics, most of the selections in the third edition are new, as are the introduction and bibliography. No other anthology spans the whole field and offers a comprehensive introduction to questions about economic methodology.

Modeling involves the use of false idealizations, yet there is typically a belief or hope that modeling somehow manages to deliver true information about the world. The paper discusses one possible way of reconciling truth and falsehood in modeling. The key trick is to relocate truth claims by reinterpreting an apparently false idealizing assumption in order to make clear what possibly true assertion is intended when using it. These include interpretations in terms of negligibility, applicability, tractability, early-step, and more. Elaborations (...) are suggested about their precise formulations, mutual relationships, and truth-aptness. (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 (from J. H. von Thünen, J. S. Mill, and Max Weber to Milton Friedman and Thomas Schelling), recent philosophy of science analyzing scientific modeling, and the debate over ideal and non-ideal theory in political philosophy (since John Rawls). (...) I will offer a set of concepts and principles to improve transparency about the precise contents of idealizations (in terms of negligibility, applicability, tractability, and early-step status) and their distinct functions (such as contributing to minimal modeling, benchmark modeling, and how-possibly modeling). (shrink)

Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively (...) higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism. (shrink)

This paper argues for two propositions. (I) Large asymmetries of power, status and influence exist between economists. These asymmetries constitute a hierarchy that is steeper than it could be and steeper than hierarchies in other disciplines. (II) This situation has potentially significant epistemic consequences. I collect data on the social organization of economics to show (I). I then argue that the hierarchy in economics heightens conservative selection biases, restricts criticism between economists and disincentivizes the development of novel research. These factors (...) together constrain economics’ capacity to develop new beliefs and reduce the likelihood that its outputs will be true. (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)

This paper aims (a) to provide characterizations of realism and instrumentalism that are philosophically interesting and applicable to economics; and (b) to defend instrumentalism against realism as a methodological stance in economics. Starting point is the observation that ‘all models are false’, which, or so I argue, is difficult to square with the realist's aim of truth, even if the latter is understood as ‘partial’ or ‘approximate’. The three cheers in favour of instrumentalism are: (1) Once we have usefulness, truth (...) is redundant. (2) There is something disturbing about causal structure. (3) It's better to do what one can than to chase rainbows. (shrink)

In Economics Rules, Dani Rodrik (2015) argues that what makes economics powerful despite the limitations of each and every model is its diversity of models. Rodrik suggests that the diversity of models in economics improves its explanatory capacities, but he does not fully explain how. I offer a clearer picture of how models relate to explanations of particular economic facts or events, and suggest that the diversity of models is a means to better economic explanations.

This paper argues that recent arguments to the effect that the debate between presentism and eternalism lacks any metaphysical substance ultimately fail, although important lessons can be gleaned from them in how to formulate a non-vacuous version of presentism. It suggests that presentism can best be characterized in the context of spacetime theories. The resulting position is an ersatzist version of presentism that admits merely non-present entities as abstracta deprived of physical existence. Ersatzist presentism both escapes the charges of triviality (...) and promises to offer a route to solving the grounding problem which befalls its more traditional cousins. (shrink)

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)

Starting from the sixties of the past century theory change has become a main concern of philosophy of science. Two of the best known formal accounts of theory change are the post-Popperian theories of verisimilitude (PPV for short) and the AGM theory of belief change (AGM for short). In this paper, we will investigate the conceptual relations between PPV and AGM and, in particular, we will ask whether the AGM rules for theory change are effective means for approaching the truth, (...) i.e., for achieving the cognitive aim of science pointed out by PPV. First, the key ideas of PPV and AGM and their application to a particular kind of propositional theories - the so called "conjunctive propositions" - will be illustrated. Afterwards, we will prove that, as far as conjunctive propositions are concerned, AGM belief change is an effective tool for approaching the truth. (shrink)

Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called evolutionary economics and has been a source of much debate among economists. Although philosophers have developed literatures (...) on the nature of models and modeling, the unique issues surrounding this kind of interdisciplinary model building have yet to be independently investigated. In this paper, we utilize evolutionary economics as a case study in the investigation of more general issues concerning interdisciplinary modeling. We begin by critiquing the distinctions currently used within the evolutionary economics literature and propose an alternative carving of the conceptual terrain. We then argue that the three types of evolutionary economics we distinguish capture distinctions that will be important whenever resources of model-based theorizing are borrowed across distinct scientific domains. Our analysis of these model-building strategies identifies several of the unique methodological and philosophical issues that confront interdisciplinary modeling. (shrink)

It is argued that Reiss (2012) fails to refute attempts to resolve the paradox of false explanatory models. His article fails to provide an articulate conception of what exactly the presumed paradox is, it suffers from uncontrolled ambiguities and inconsistencies, and it fails to adequately address accounts of economic models that might contribute to reconciling their apparent falsehood and explanatoriness. Some details in my account of how apparently false models may explain are clarified.

This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, (...) defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general. (shrink)

Beliefs come in different strengths. What are the norms that govern these strengths of belief? Let an agent's belief function at a particular time be the function that assigns, to each of the propositions about which she has an opinion, the strength of her belief in that proposition at that time. Traditionally, philosophers have claimed that an agent's belief function at any time ought to be a probability function (Probabilism), and that she ought to update her belief function upon obtaining (...) new evidence by conditionalizing on that evidence (Conditionalization). Until recently, the central arguments for these claims have been pragmatic. But these putative justifications fail to identify what is epistemically irrational about violating Probabilism or Conditionalization. A new approach, which I will call epistemic utility theory, attempts to remedy this. It treats beliefs as epistemic acts; and it appeals to the notion of an epistemic utility function, which measures of how epistemically valuable a particular belief function is for a particular way the world might be. It then formulates fundamental epistemic norms that are analogous to the fundamental practical norms that underlie decision theory. I survey the results obtained so far in this young research project, and present a sustained critique of certain assumptions that have been made by a number of philosophers working in this area. (shrink)

In this essay, I examine the role of dissimilarity in scientific representation. After briefly reviewing some of the philosophical literature which places a strong emphasis on the role of similarity, I turn to examine some work from Carroll and Borges which demonstrates that perfect similarity is not valuable in the representational use of maps. Expanding on this insight, I go on to argue that this shows that dissimilarity is an important part of the representational use of maps—a point I then (...) extend to the case of scientific representation. Relying on some work from Latour, I argue that dissimilarity plays an essential role in representational practice, by providing novel forms of manipulation and use which affords the achievement of various epistemic and nonepistemic aims. After showing how this point connects to some other literature on scientific representation, I discuss some examples of the value of dissimilarity in the use of representational vehicles. Overall, I argue that to understand scientific representation, we will need to consider more than just similarity. We will need to explore dissimilarities as well. (shrink)

This comment argues that there is an explanation paradox in economics, as Julian Reiss maintains, only if models in economics succeed in explaining even though they are not approximately true, fail to identify the causes of what they purport to explain, and misdescribe the mechanism by which the causes lead to the effects to be explained. Reiss provides no reason to believe that models that do not describe the causes and mechanisms at work are nevertheless explanatory.

The description of how individual models in families of models are related to each other is crucial for the general philosophical understanding of model-based scientific practice. We focus on the Capital Asset Pricing Models (CAPM) family, a cornerstone in financial economics, to provide a descriptive analysis of model relations within a family. We introduce the concepts of theoretical and empirical complementarity to characterise model relations. Our complementarity analysis of model relations has two types of payoff. Specifically regarding the CAPM, our (...) analysis reveals why this model family, which has been empirically contested, has yet remained popular and important: the different models that have been added over time have made new empirically and theoretically complementary contributions to the model family. More generally, our analysis reveals the dynamic character of model-based scientific practice. Characterising relations between models as theoretically and/or empirically complementary yields three hitherto underappreciated lessons: (i) actual modelling purposes are not always primarily epistemic, (ii) a model's individual import is relative, not absolute, and (iii) there is an important interplay between theory and data models. Faithfully characterising scientific modelling in this way facilitates subsequent analysis of models' epistemic import. (shrink)

Philosophers of science have often favoured reductive approaches to how-possibly explanation. This article identifies three alternative conceptions making how-possibly explanation an interesting phenomenon in its own right. The first variety approaches “how possibly X?” by showing that X is not epistemically impossible. This can sometimes be achieved by removing misunderstandings concerning the implications of one’s current belief system but involves characteristically a modification of this belief system so that acceptance of X does not result in contradiction. The second variety offers (...) a potential how-explanation of X. It is usually followed by a range of further potential how-explanations of the same phenomenon. In recent literature the factual claims implied by the second variety have been downplayed whereas the heuristic role of mapping the space of conceptual possibilities has been emphasized. I will focus especially on this truth-bracketing sense of potentiality when looking closer at the second variety in the paper. The third variety has attracted less interest. It presents a partial how-explanation of X. Typically it aims to establish the existence of a mechanism by which X could be and was generated. The third conception stands out as the natural alternative for the advocate of ontic how-possibly explanations. This article transfers Salmon’s view that explanation-concepts can be broadly divided into epistemic, modal, and ontic to the context of how-possibly explanations. Moreover, it is argued that each of the three above-mentioned varieties of how-possibly explanation occurs in science. To recognize this may be especially relevant for philosophers. We are often misled by the promises of various why-explanation accounts, and seem to have forgotten nearly everything about the diversity of how-possibly explanations. (shrink)

Much recent discussion in social epistemology has focussed on the question of whether peers can rationally sustain a disagreement. A growing number of social epistemologists hold that the answer is negative. We point to considerations from the history of science that favor rather the opposite answer. However, we also explain how the other position can appear intuitively attractive.

In this paper, I address the issue of scientific modelling in contemporary linguistics, focusing on the generative tradition. In so doing, I identify two common varieties of linguistic idealisation, which I call determination and isolation respectively. I argue that these distinct types of idealisation can both be described within the remit of Weisberg’s :639–659, 2007) minimalist idealisation strategy in the sciences. Following a line set by Blutner :27–35, 2011), I propose this minimalist idealisation analysis for a broad construal of the (...) generative linguistic programme and thus cite examples from a wide range of linguistic frameworks including early generative syntax, Minimalism, the parallel architecture and optimality theory. Lastly, I claim that from a modelling perspective, the dynamic turn in syntax can be explained as a continuation, as opposed to a marked shift, of the generative modelling paradigm. Seen in this light, my proposal is an even broader construal of the generative tradition, along scientific modelling lines. Thus, I offer a lens through which to appreciate the scientific contribution of generative grammar, amid an increased resistance to some of its core theoretical posits, in terms of a brand of structural realism in the philosophy of science and specifically scientific modelling. (shrink)

According to the environment-induced approach to decoherence, the split of the Universe into the degrees of freedom which are of direct interest to the observer and the remaining degrees of freedom is absolutely essential for decoherence. However, the EID approach offers no general criterion for deciding where to place the “cut” between system and environment: the environment may be “external” or “internal”. The main purpose of this paper is to argue that decoherence is a relative phenomenon, better understood from a (...) closed-system perspective according to which the split of a closed quantum system into an open subsystem and its environment is just a way of selecting a particular space of relevant observables of the whole closed system. In order to support this claim, we shall consider the results obtained in a natural generalization of the simple spin-bath model usually studied in the literature. Our main thesis will lead us to two corollaries. First, the problem of identifying the system that decoheres is actually a pseudo-problem, which vanishes as soon as one acknowledges the relative nature of decoherence. Second, the usually supposed link between decoherence and energy dissipation is misguided. As previously pointed out, energy dissipation and decoherence are different phenomena, and we shall argue for this difference on the basis of the relative nature of decoherence. (shrink)

This paper discusses three shortcomings of the current state of the debate regarding historical evidence against scientific realism. Attending to these issues will direct the debate away from over-generalising wholesale arguments.

In what follows, I will give examples of the sorts of step that can be taken towards spelling out the intuition that, after all, good models might be true. Along the way, I provide an outline of my account of models as ontologically and pragmatically constrained representations. And I emphasize the importance of examining models as functionally composed systems in which different components play different roles and only some components serve as relevant truth bearers. This disputes the standard approach that (...) proceeds by simply counting true and false elements in models in their entirety and concludes that models are false since they contain so many false elements. I call my alternative the functional decomposition approach. (shrink)

Stopping rules — rules dictating when to stop accumulating data and start analyzing it for the purposes of inferring from the experiment — divide Bayesians, Likelihoodists and classical statistical approaches to inference. Although the relationship between Bayesian philosophy of science and stopping rules can be complex (cf. Steel 2003), in general, Bayesians regard stopping rules as irrelevant to what inference should be drawn from the data. This position clashes with classical statistical accounts. For orthodox statistics, stopping rules do matter to (...) what inference should be drawn from the data. "The dispute over stopping rule is far from being a marginal quibble, but is instead a striking illustration of the divergence of fundamental aims and standards separating Bayesians and advocates of orthodox statistical methods" (Steel 2004, 195) ... (shrink)

The old antagonism between the Quinean and the Duhemian view on underdetermination is reexamined. In this respect, two theses will be defended. First, it is argued that the main differences between Quine's and Duhem's versions of underdetermination derive from a different attitude towards the history of science. While Quine considered underdetermination from an ahistorical, a logical point of view, Duhem approached it as a distinguished historian of physics. On this basis, a logical and a historical version of the underdetermination thesis (...) can be distinguished. The second thesis of the article is that the main objections against underdetermination are fatal only to the logical rendering. Taken together, the two theses constitute a defence of underdetermination. (shrink)

Suppose that several observations “coincide,” meaning that they are similar in some interesting respect. Is this coinciding a mere coincidence, or does it derive from a common cause? Those who reason about this kind of question—whether they embrace the first answer or the second—often deploy a mode of inference that I call probabilistic modus tollens. In this chapter I criticize probabilistic modus tollens and consider likelihood and Bayesian frameworks for reasoning about coincidences. I also consider the perspective offered by model (...) selection theory (including the Akaike information criterion), and argue that model selection often provides important insights. (shrink)

For a long time, the accepted explanatory model of language acquisition was the so-called Principles and Parameters framework (P&P). P&P seemingly provides an elegant answer to the central puzzle of generative linguistics: How do children acquire their native language given the limited time and input resources available to them? Yet P&P tells a story that is evolutionarily implausible, and for this reason it has since been abandoned. I argue that this is an unwarranted move, and that it could and should (...) be avoided by reassessing the epistemic status of P&P. In particular, I argue that contrary to extant accounts, P&P ought to be retrospectively construed as a highly idealized (toy) model of language acquisition. The proposed reinterpretation is vindicated, I argue, insofar as it paves the way for a reconciliation of the two central explanatory challenges of modern generative linguistics. (shrink)

Scientific models are important, if not the sole, units of science. This thesis addresses the following question: in virtue of what do scientific models represent their target systems? In Part i I motivate the question, and lay out some important desiderata that any successful answer must meet. This provides a novel conceptual framework in which to think about the question of scientific representation. I then argue against Callender and Cohen’s attempt to diffuse the question. In Part ii I investigate the (...) ideas that scientific models are ‘similar’, or structurally morphic, to their target systems. I argue that these approaches are misguided, and that at best these relationships concern the accuracy of a pre-existing representational relationship. I also pay particular attention to the sense in which target systems can be appropriately taken to exhibit a ‘structure’, and van Fraassen’s recent argument concerning the pragmatic equivalence between representing phenomena and data. My next target is the idea that models should not be seen as objects in their own right, but rather what look like descriptions of them are actually direct descriptions of target systems, albeit not ones that should be understood literally. I argue that these approaches fail to do justice to the practice of scientific modelling. Finally I turn to the idea that how models represent is grounded, in some sense, in their inferential capacity. I compare this approach to anti-representationalism in the philosophy of language and argue that analogous issues arise in the context of scientific representation. Part iii contains my positive proposal. I provide an account of scientific representation based on Goodman and Elgin’s notion of representation-as. The result is a highly conventional account which is the appropriate level of generality to capture all of its instances, whilst remaining informative about the notion. I illustrate it with reference to the Phillips-Newlyn machine, models of proteins, and the Lotka-Volterra model of predator-prey systems. These examples demonstrate how the account must be understood, and how it sheds light on our understanding of how models are used. I finally demonstrate how the account meets the desiderata laid out at the beginning of the thesis, and outline its implications for further questions from the philosophy of science; not limited to issues surrounding the applicability of mathematics, idealisation, and what it takes for a model to be ‘true’. (shrink)

John Norton’s argument that all formal theories of induction fail raises substantive questions about the philosophical analysis of scientific reasoning. What are the criteria of adequacy for philosophical theories of induction, explanation, or theory structure? Is more than one adequate theory possible? Using a generalized version of Norton’s argument, I demonstrate that the competition between formal and material theories in philosophy of science results from adhering to different criteria of adequacy. This situation encourages an interpretation of “formal” and “material” as (...) indicators of divergent criteria that accompany different philosophical methodologies. I characterize another criterion of adequacy associated with material theories, the avoidance of imported problems, and conclude that one way to negotiate between conflicting criteria is to adopt a pluralist stance toward philosophical theories of scientific reasoning. (shrink)

According to a wrong interpretation of the Bell theorem, it has been repeatedly claimed in recent times that we are forced by experiments to drop any possible form of realism in the foundations of quantum mechanics. In this paper I defend the simple thesis according to which the above claim cannot be consistently supported: the Bell theorem does not concern realism, and realism per se cannot be refuted in itself by any quantum experiment. As a consequence, realism in quantum mechanics (...) is not something that can be simply explained away once and for all on the basis of experiments, but rather something that must be conceptually characterized and discussed in terms of its foundational virtues and vices. To assess it, we cannot rely on experimentation but rather on philosophical discussion: realism is not a phlogiston-like notion, despite the efforts of the contemporary quantum orthodoxy to conceive it in Russellian terms as the relics of a bygone age. (shrink)