How do philosophers of science make use of historical case studies? Are their accounts of historical cases purpose-built and lacking in evidential strength as a result of putting forth and discussing philosophical positions? We will study these questions through the examination of three different philosophical case studies. All of them focus on modeling and on Vito Volterra, contrasting his work to that of other theoreticians. We argue that the worries concerning the evidential role of historical case studies in philosophy (...) are partially unfounded, and the evidential and hermeneutical roles of case studies need not be played against each other. In philosophy of science, case studies are often tied to conceptual and theoretical analysis and development, rendering their evidential and theoretic/hermeneutic roles intertwined. Moreover, the problems of resituating or generalizing local knowledge are not specific to philosophy of science but commonplace in many scientific practices—which show similarities to the actual use of historical case studies by philosophers of science. (shrink)

ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s (...) modelling approaches, they are largely due to two other features of contemporary model construction processes that Weisberg does not explicitly consider: the methods-drivenness and outcome-orientedness of modelling. 1Introduction 2Modelling as Indirect Representation 3The Design of the Lotka–Volterra Model by Volterra 3.1Volterra’s method of hypothesis 3.2The construction of the Lotka–Volterra model by Volterra 4The Design of the Lotka–Volterra Model by Lotka 4.1Physical biology according to Lotka 4.2Lotka’s systems approach and the Lotka–Volterra model 5Philosophical Discussion: Strategies and Tools of Modelling 5.1Volterra’s path from the method of isolation to the method of hypothesis 5.2The template-based approach of Lotka 5.3Modelling: methods-driven and outcome-oriented 6Conclusion. (shrink)

Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to (...) have different epistemic characters. Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion. (shrink)

A finer-grained delineation of a given explanandum reveals a nexus of closely related causal and non- causal explanations, complementing one another in ways that yield further explanatory traction on the phenomenon in question. By taking a narrower construal of what counts as a causal explanation, a new class of distinctively mathematical explanations pops into focus; Lange’s characterization of distinctively mathematical explanations can be extended to cover these. This new class of distinctively mathematical explanations is illustrated with the Lotka-Volterra (...) equations. There are at least two distinct ways those equations might hold of a system, one of which yields straightforwardly causal explanations, but the other of which yields explanations that are distinctively mathematical in terms of nomological strength. In the first, one first picks out a system or class of systems, finds that the equations hold in a causal -explanatory way; in the second, one starts with the equations and explanations that must apply to any system of which the equations hold, and only then turns to the world to see of what, if any, systems it does in fact hold. Using this new way in which a model might hold of a system, I highlight four specific avenues by which causal and non- causal explanations can complement one another. (shrink)

In a (2016) paper in this journal, I defuse allegations that theoretical ecological research is problematic because it relies on teleological metaphysical assumptions. Mark Sagoff offers a formal reply. In it, he concedes that I succeeded in establishing that ecologists abandoned robust teleological views long ago and that they use teleological characterizations as metaphors that aid in developing mechanistic explanations of ecological phenomena. Yet, he contends that I did not give enduring criticisms of theoretical ecology a fair shake in my (...) paper. He says this is because enduring criticisms center on concerns about the nature of ecological networks and forces, the instrumentality of ecological laws and theoretical models, and the relation between theoretical and empirical methods in ecology that that paper does not broach. Below I set apart the distinct criticisms Sagoff presents in his commentary and respond to each in turn. (shrink)

Michael Weisberg has argued that robustness analysis is useful in evaluating both scientific models and their implications and that robustness analysis comes in three types that share their form and aim. We argue for three cautionary claims regarding Weisberg's reconstruction: robustness analysis may be of limited or no value in evaluating models and their implications; the unificatory reconstruction conceals that the three types of robustness differ in form and role; there is no confluence of types of robustness. We (...) illustrate our central first claim with a case study: the application of Lotka-Volterra models to technology diffusion. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements of (...) truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

Although Fuzzy logic and Fuzzy Mathematics is a widespread subject and there is a vast literature about it, yet the use of Fuzzy issues like Fuzzy sets and Fuzzy numbers was relatively rare in time concept. This could be seen in the Fuzzy time series. In addition, some attempts are done in fuzzing Turing Machines but seemingly there is no need to fuzzy time. Throughout this article, we try to change this picture and show why it is helpful to consider (...) the instants of time as Fuzzy numbers. In physics, though there are revolutionary ideas on the time concept like B theories in contrast to A theory also about central concepts like space, momentum… it is a long time that these concepts are changed, but time is considered classically in all well-known and established physics theories. Seemingly, we stick to the classical time concept in all fields of science and we have a vast inertia to change it. Our goal in this article is to provide some bases why it is rational and reasonable to change and modify this picture. Here, the central point is the modified version of “Unexpected Hanging” paradox as it is described in "Is classical Mathematics appropriate for theory of Computation".This modified version leads us to a contradiction and based on that it is presented there why some problems in Theory of Computation are not solved yet. To resolve the difficulties arising there, we have two choices. Either “choosing” a new type of Logic like “Para-consistent Logic” to tolerate contradiction or changing and improving the time concept and consequently to modify the “Turing Computational Model”. Throughout this paper, we select the second way for benefiting from saving some aspects of Classical Logic. In chapter 2, by applying quantum Mechanics and Schrodinger equation we compute the associated fuzzy number to time. (shrink)

Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: (...) Can the fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory "how-possibly" model explanation from a genuine "how-actually" model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions. (shrink)

In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Kitcher’s unificationist account supports IBE; Railton’s deductive–nomological–probabilistic model, Salmon’s statistical-relevance model, and van Fraassen’s erotetic (...) account are incompatible with IBE; and Salmon’s causal–mechanical model is merely consistent with IBE. In short, many influential models of scientific explanation do not support IBE. I end by outlining three possible conclusions to draw: (1) either philosophers of science or defenders of IBE have seriously misconstrued the concept of explanation, (2) philosophers of science and defenders of IBE do not use the term ‘explanation’ univocally, and (3) the ampliative conception of IBE, which is compatible with any model of scientific explanation, deserves a closer look. (shrink)

The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...) a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)

Cloud computing (ClC) has become a more popular computer paradigm in the preceding few years. Quality of Service (QoS) is becoming a crucial issue in service alteration because of the rapid growth in the number of cloud services. When evaluating cloud service functioning using several performance measures, the issue becomes more complex and non-trivial. It is therefore quite difficult and crucial for consumers to choose the best cloud service. The user's choices are provided in a quantifiable manner in the current (...) methods for choosing cloud services. Hence, this study attempts to achieve this objective through construction. decision-making framework so-called cloud services evaluator (CSsEv). The main indicator and its sub-indicators are formed as nodes at levels(n) in tree soft sets (TSSs). Thereafter Single Value Neutrosophic Sets (SVNSs) as branch of neutrosophic sets which conjunction with the Multi-Criteria Decision Making (MCDM) technique to facilitate analysis and evaluation process for the available Cloud services providers. Hence, entropy is employed to obtain indicators and sub_indicators weights and Complex Proportional Assessment utilizes these weights to facilitate the decision process of selecting optimal ClSPs. (shrink)

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not a measure of (...) its epistemic reliability. Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved ‘vicariously’, such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate for particular purposes. (shrink)

[This is an edited version of a keynote talk at the conference on Neural Information Processing Systems (NeurIPS) on November 28, 2022, with some minor additions and subtractions.] -/- There has recently been widespread discussion of whether large language models might be sentient or conscious. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for (...) example, their lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future. (shrink)

Models are indispensable tools of scientific inquiry, and one of their main uses is to improve our understanding of the phenomena they represent. How do models accomplish this? And what does this tell us about the nature of understanding? While much recent work has aimed at answering these questions, philosophers' focus has been squarely on models in empirical science. I aim to show that pure mathematics also deserves a seat at the table. I begin by presenting two (...) cases: Cramér’s random model of the prime numbers and the function field model of the integers. These cases show that mathematicians, like empirical scientists, rely on unrealistic models to gain understanding of complex phenomena. They also have important implications for some much-discussed theses about scientific understanding. First, modeling practices in mathematics confirm that one can gain understanding without obtaining an explanation. Second, these cases undermine the popular thesis that unrealistic models confer understanding by imparting counterfactual knowledge. (shrink)

This paper constructs a model of metaphysical indeterminacy that can accommodate a kind of ‘deep’ worldly indeterminacy that arguably arises in quantum mechanics via the Kochen-Specker theorem, and that is incompatible with prominent theories of metaphysical indeterminacy such as that in Barnes and Williams (2011). We construct a variant of Barnes and Williams's theory that avoids this problem. Our version builds on situation semantics and uses incomplete, local situations rather than possible worlds to build a model. We evaluate the resulting (...) theory and contrast it with similar alternatives, concluding that our model successfully captures deep indeterminacy. (shrink)

This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must (...) target sets of models, their multiplicity of functions within science, and their scientific context and history and for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z. (shrink)

Simulation models of the Reiterated Prisoner's Dilemma (in the following: RPD-models) are since 30 years considered as one of the standard tools to study the evolution of cooperation (Rangoni 2013; Hoffmann 2000). A considerable number of such simulation models has been produced by scientists. Unfortunately, though, none of these models has empirically been verified and there exists no example of empirical research where any of the RPD-models has successfully been employed to a particular instance of (...) cooperation. Surprisingly, this has not kept scientists from continuing to produce simulation models in the same tradition and from writing their own history as a history of success. In a recent simulation study -- which does not make use of the RPD but otherwise follows the same pattern of research -- Robert Axelrod's (1984) original role model for this kind of simulation studies is praised as ``an extremely effective means for investigating the evolution of cooperation'' and considered as ``widely credited with invigorating that field'' (Rendell et al. 2010). (shrink)

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

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

Should models of consciousness be detailed _mechanistic_ models of particular types of systems, or should they be _minimal_ models that abstract away from the underlying mechanistic details and provide generalisations? Detailed mechanistic models may afford a complete and precise account of consciousness in human beings and other, physiologically similar mammals. But they do not provide a good model of consciousness in other animals, such as non-vertebrates, let alone artificial systems. Minimal models can be applicable to (...) a wide range of different conscious systems. But do they provide genuine explanations that are autonomous from explanations by detailed mechanistic models? This paper provides a taxonomy of minimal models and measures of consciousness, clarifies their relation to detailed mechanistic models, and highlights benefits of different minimalist approaches to consciousness. (shrink)

Simulation models of the Reiterated Prisoner's Dilemma have been popular for studying the evolution of cooperation since more than 30 years now. However, there have been practically no successful instances of empirical application of any of these models. At the same time this lack of empirical testing and confirmation has almost entirely been ignored by the modelers community. In this paper, I examine some of the typical narratives and standard arguments with which these models are justified by (...) their authors despite the lack of empirical validation. I find that most of the narratives and arguments are not at all compelling. None the less they seem to serve an important function in keeping the simulation business running despite its empirical shortcomings. (shrink)

This paper discusses critically what simulation models of the evolution of cooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” (1984) and the modeling tradition it has inspired. Hardly any of the many simulation models in this tradition have been applicable empirically. Axelrod’s role model suggested a research design that seemingly allowed to draw general conclusions from simulation models even if the mechanisms that drive the simulation could not be identified empirically. But this research design was (...) fundamentally flawed. At best such simulations can claim to prove logical possibilities, i.e. they prove that certain phenomena are possible as the consequence of the modeling assumptions built into the simulation, but not that they are possible or can be expected to occur in reality. I suggest several requirements under which proofs of logical possibilities can nevertheless be considered useful. Sadly, most Axelrod-style simulations do not meet these requirements. It would be better not to use this kind of simulations at all. (shrink)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides insight into the common (...) but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

The following considerations concern modelling Belief Dynamics (BD) not just in the sense of a formalization, but rather in the sense of building a computational model and implementing the corresponding data structures and algorithms of recomputing beliefs. The purpose of such a project is to illustrate some ideas about belief changes in a Web of Beliefs (WoB) to explore and deepen one's understanding of belief changes by trying to implement or improve corresponding algorithms.

This paper discusses critically what simulation models of the evolution ofcooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” and the modeling tradition it has inspired. Hardly any of the many simulation models of the evolution of cooperation in this tradition have been applicable empirically. Axelrod’s role model suggested a research design that seemingly allowed to draw general conclusions from simulation models even if the mechanisms that drive the simulation could not be identified empirically. But this (...) research design was fundamentally flawed, because it is not possible to draw general empirical conclusions from theoretical simulations. At best such simulations can claim to prove logical possibilities, i.e. they prove that certain phenomena are possible as the consequence of the modeling assumptions built into the simulation, but not that they are possible or can be expected to occur in reality I suggest several requirements under which proofs of logical possibilities can nevertheless be considered useful. Sadly, most Axelrod-style simulations do not meet these requirements. I contrast this with Schelling’s neighborhood segregation model, thecore mechanism of which can be retraced empirically. (shrink)

People often assume that everyone can be divided by sex/gender (that is, by physical and social characteristics having to do with maleness and femaleness) into two tidy categories: male and female. Careful thought, however, leads us to reject that simple ‘binary’ picture, since not all people fall precisely into one group or the other. But if we do not think of sex/gender in terms of those two categories, how else might we think of it? Here I consider four distinct (...) class='Hi'>models; each model correctly captures some features of sex/gender, and so each is appropriate in some contexts. But the first three models are inadequate when tough questions arise, like whether trans women should be admitted as students at a women’s college or when it is appropriate for intersex athletes to compete in women’s athletic events. (‘Trans’ refers to the wide range of people who have an atypical gender identity for someone of their birth-assigned sex, and ‘intersex’ refers to people whose bodies naturally develop with markedly different physical sex characteristics than are paradigmatic of either men or women.) Such questions of inclusion and exclusion matter enormously to the people whose lives are affected by them, but ordinary notions of sex/gender offer few answers. The fourth model I describe is especially designed to make those hard decisions easier by providing a process to clarify what matters. (shrink)

Games of chance are developed in their physical consumer-ready form on the basis of mathematical models, which stand as the premises of their existence and represent their physical processes. There is a prevalence of statistical and probabilistic models in the interest of all parties involved in the study of gambling – researchers, game producers and operators, and players – while functional models are of interest more to math-inclined players than problem-gambling researchers. In this paper I present a (...) structural analysis of the knowledge attached to mathematical models of games of chance and the act of modeling, arguing that such knowledge holds potential in the prevention and cognitive treatment of excessive gambling, and I propose further research in this direction. (shrink)

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

his paper is an excerpt from a larger project that aims to open a new pathway into Spinoza's Ethics by formally reconstructing an initial fragment of this text. The semantic backbone of the project is a custom-made Spinozian model theory that lays out some of the formal prerequisites for more ne-grained investigations into Spinoza's fundamental ontology and modal metaphysics. We implement Spinoza's theory of attributes using many-sorted models with a rich system of identity that allows us to clarify the (...) puzzling status of such logical principles as the Substitution of Identicals and Transitivity of Identity in Spinoza's thought. The intensional structure of our Spinozian models also captures his proposal that states of a airs can be necessitated or excluded by the essences of particular things, an essence-relative modality that should be of interest to philosophers who have sought to rehabilitate the concept of essence in contemporary analytic metaphysics. (shrink)

In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, (...) I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object. (shrink)

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

Although some previous studies have investigated the relationship between moral foundations and moral judgment development, the methods used have not been able to fully explore the relationship. In the present study, we used Bayesian Model Averaging (BMA) in order to address the limitations in traditional regression methods that have been used previously. Results showed consistency with previous findings that binding foundations are negatively correlated with post-conventional moral reasoning and positively correlated with maintaining norms and personal interest schemas. In addition to (...) previous studies, our results showed a positive correlation for individualizing foundations and post-conventional moral reasoning. Implications are discussed as well as a detailed explanation of the novel BMA method in order to allow others in the field of moral education to be able to use it in their own studies. (shrink)

It has been stated that the notion of cause and effect is one object of study that sciences and engineering revolve around. Lately, in software engineering, diagrammatic causal inference methods (e.g., Pearl’s model) have gained popularity (e.g., analyzing causes and effects of change in software requirement development). This paper concerns diagrammatical (graphic) models of causal relationships. Specifically, we experiment with using the conceptual language of thinging machines (TMs) as a tool in this context. This would benefit works on causal (...) relationships in requirements engineering, enhance our understanding of the TM modeling, and contribute to the study of the philosophical notion of causality. To specify the causality in a system’s description is to constrain the system’s behavior and thus exclude some possible chronologies of events. The notion of causality has been studied based on tools to express causal questions in diagrammatic and algebraic forms. Causal models deploy diagrammatic models, structural equations, and counterfactual and interventional logic. Diagrammatic models serve as a language for representing what we know about the world. The research methodology in the paper focuses on converting causal graphs into TM models and contrasts the two types of representation. The results show that the TM depiction of causality is more complete and therefore can provide a foundation for causal graphs. (shrink)

I provide a theory of causation within the causal modeling framework. In contrast to most of its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn't cause) E in a causal model, M, then it will continue to say that C caused (didn't cause) E once we've removed an inessential variable from M. I suggest that, if this theory is true, then we should understand a cause as something which transmits deviant or (...) non-inertial behavior to its effect. (shrink)

We argue that concerns about double-counting—using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate—deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood approach to (...) incremental confirmation. According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. 1 Introduction2 Remarks about Models and Adequacy-for-Purpose3 Evidence for Calibration Can Also Yield Comparative Confirmation3.1 Double-counting I3.2 Double-counting II4 Climate Science Examples: Comparative Confirmation in Practice4.1 Confirmation due to better and worse best fits4.2 Confirmation due to more and less plausible forcings values5 Old Evidence6 Doubts about the Relevance of Past Data7 Non-comparative Confirmation and Catch-Alls8 Climate Science Example: Non-comparative Confirmation and Catch-Alls in Practice9 Concluding Remarks. (shrink)

This paper constitutes a radical departure from the existing philosophical literature on models, modeling-practices, and model-based science. I argue that the various entities and practices called 'models' and 'modeling-practices' are too diverse, too context-sensitive, and serve too many scientific purposes and roles, as to allow for a general philosophical analysis. From this recognition an alternative view emerges that I shall dub model anarchism.

In a recent special issue dedicated to Dani Rodrik’s (2015) influential monograph Economics Rules, Grüne-Yanoff and Marchionni (2018) raise a potentially damning problem for Rodrik’s suggestion that progress in economics should be understood and measured laterally, by a continuous expansion of new models. They argue that this could lead to an “embarrassment of riches”, i.e. the rapid expansion of our model library to such an extent that we become unable to choose between the available models, and thus needs (...) to be solved to make ‘model pluralism’ viable. Drawing on Veit’s (2019a) ‘model pluralism’ account, this paper argues that model pluralism as a thesis about the relationship between science and nature undermines the very idea of a general model selection framework for policy making. (shrink)

One striking feature of the contemporary modelling practice is its interdisciplinary nature. The same equation forms, and mathematical and computational methods, are used across different disciplines, as well as within the same discipline. Are there, then, differences between intra- and interdisciplinary transfer, and can the comparison between the two provide more insight on the challenges of interdisciplinary theoretical work? We will study the development and various uses of the Ising model within physics, contrasting them to its applications to socio-economic systems. (...) While the renormalization group methods justify the transfer of the Ising model within physics – by ascribing them to the same universality class – its application to socio-economic phenomena has no such theoretical grounding. As a result, the insights gained by modelling socio-economic phenomena by the Ising model may remain limited. (shrink)

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present (...) climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists’ discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. (shrink)

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles of (...) counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)

The aim of this article is to investigate the relevance and implications of synthetic models for the study of the interactive dimension of minimal life and cognition, by taking into consideration how the use of artificial systems may contribute to an understanding of the way in which interactions may affect or even contribute to shape biological identities. To do so, this article analyzes experimental work in synthetic biology on different types of interactions between artificial and natural systems, more specifically: (...) between protocells and between biological living cells and protocells. It discusses how concepts such as control, cognition, communication can be used to characterize these interactions from a theoretical point of view, which criteria of relevance and evaluation of synthetic models can be applied to these cases, and what are their limits. (shrink)

“Critical thinking in higher education” is a phrase that means many things to many people. It is a broad church. Does it mean a propensity for finding fault? Does it refer to an analytical method? Does it mean an ethical attitude or a disposition? Does it mean all of the above? Educating to develop critical intellectuals and the Marxist concept of critical consciousness are very different from the logician’s toolkit of finding fallacies in passages of text, or the practice of (...) identifying and distinguishing valid from invalid syllogisms. Critical thinking in higher education can also encompass debates about critical pedagogy, i.e., political critiques of the role and function of education in society, critical feminist approaches to curriculum, issues related to what has become known as critical citizenship, or any other education-related topic that uses the appellation “critical”. Equally, it can, and usually does, refer to the importance and centrality of developing general skills in reasoning—skills that we hope all graduates possess. Yet, despite more than four decades of dedicated scholarly work “critical thinking” remains as elusive as ever. As a concept, it is, as Raymond Williams has noted, a ‘most difficult one’ (Williams, 1976, p. 74). (shrink)

I analyse three most interesting and extensive approaches to theoretical models: classical ones—proposed by Peter Achinstein and Michael Redhead, and the rela-tively rareanalysed approach of Ryszard Wójcicki, belonging to a later phase of his research where he gave up applyingthe conceptual apparatus of logical semantics. I take into consideration the approaches to theoretical models in which they are qualified as models representing the reality. That is why I omit Max Black’s and Mary Hesse’s concepts of such (...) class='Hi'>models, as those two concepts belong to the analogue model group if we consider the main function of the model of a given class as its classification criterion. My main focus is on theoretical models with representative functions as these very models and, in a broader context, the question of representation. (shrink)

Experiments in particle physics have hitherto failed to produce any significant evidence for the many explicit models of physics beyond the Standard Model (BSM) that had been proposed over the past decades. As a result, physicists have increasingly turned to model-independent strategies as tools in searching for a wide range of possible BSM effects. In this paper, we describe the Standard Model Effective Field Theory (SM-EFT) and analyse it in the context of the philosophical discussions about models, theories, (...) and (bottom-up) effective field theories. We find that while the SM-EFT is a quantum field theory, assisting experimentalists in searching for deviations from the SM, in its general form it lacks some of the characteristic features of models. Those features only come into play if put in by hand or prompted by empirical evidence for deviations. Employing different philosophical approaches to models, we argue that the case study suggests not to take a view on models that is overly permissive because it blurs the lines between the different stages of the SM-EFT research strategies and glosses over particle physicists' motivations for undertaking this bottom-up approach in the first place. Looking at EFTs from the perspective of modelling does not require taking a stance on some specific brand of realism or taking sides in the debate between reduction and emergence into which EFTs have recently been embedded. (shrink)

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

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

How does our language relate to reality? This is a question that is especially pertinent in set theory, where we seem to talk of large infinite entities. Based on an analogy with the use of models in the natural sciences, we argue for a threefold correspondence between our language, models, and reality. We argue that so conceived, the existence of models can be underwritten by a weak notion of existence, where weak existence is to be understood as (...) existing in virtue of language. (shrink)

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows (...) how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations. -/- . (shrink)