According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers...

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and (...) equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry. (shrink)

We analyze the usage of computer simulation at the LHC and derive seven jointly necessary requirements for a simulation to be considered ’trustworthy’, such that it can be used as proxy for experiments. We show that these requirements can also be applied to systems without direct experimental access and discuss their validity for properties that have not yet been probed. While being necessary, these requirements are not sufficient. Such trustworthy simulations will be analyzed for the relative epistemic statuses of simulation (...) and material measurements, from which we argue that claims of their parity are unfounded. Instead, using credibility as a measure for epistemic status, and in view of the temporal and epistemic dependence of simulation on material measurements, we argue that the latter have a higher epistemic status than the former. We further argue that suggestions to qualify the epistemic status by ’defocussing’ on the material connection to the target system of either human or natural experiments are misleading. (shrink)

This paper provides a conceptual framework that allows for distinguishing between different kinds of generalisation and applicability. It is argued that generalising models may bring epistemic benefits. They do so if they show that restrictive and unrealistic assumptions do not threaten the credibility of results derived from models. There are two different notions of applicability, generic and specific, which give rise to three different kinds of generalizations. Only generalising a result brings epistemic benefits concerning the truth of model components or (...) results. Abstracting the model and applying the model into new systems are not intrinsically epistemically beneficial in this way. The Dixit-Stiglitz model of monopolistic competition is used as an illustration. (shrink)

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

The debate between ΛCDM and MOND is often cast in terms of competing gravitational theories. However, recent philosophical discussion suggests that the ΛCDM–MOND debate demonstrates the challenges of multiscale modeling in the context of cosmological scales. I extend this discussion and explore what happens when the debate is thought to be about modeling rather than about theory, offering a model-focused interpretation of the ΛCDM–MOND debate. This analysis shows how a model-focused interpretation of the debate provides a better understanding of challenges (...) asso- ciated with extension to a different scale or domain, which are tied to commitments about explanatory fit. (shrink)

Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method. Then I consider whether Weisberg’s account could capture the judgments involved (...) in this practice. I argue that his account fails for three reasons. First, his account is simply too abstract to capture what is going on in MLE. Second, it implies an atomistic conception of similarity, while MLE operates in a holistic manner. Third, Weisberg’s atomistic conception of similarity can be traced back to a problematic set-theoretic approach to the structure of models. Finally, I tentatively suggest how these problems might be solved by a holistic approach in which models and targets are compared in a non-set-theoretic fashion. (shrink)

This article advertises a new account of computational implementation. According to the resemblance account, implementation is a matter of resembling a computational architecture. The resemblance account departs from previous theories by denying that computational architectures are exhausted by their formal, mathematical features. Instead, they are taken to be permeated with causality, spatiotemporality, and other nonmathematical features. I argue that this approach comports well with computer scientific practice and offers a novel response to so-called triviality arguments.

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)

Previously, I (Boesch 2017) described a notion called “representational licensing”—the set of activities of scientific practice by which scientists establish the intended representational use of a vehicle. In this essay, I expand and develop this concept of representational licensing. I begin by showing how the concept is of value for both pragmatic and substantive approaches to scientific representation. Then, through the examination of a case study of the Mississippi River Basin Model, I point out and explain some of the activities (...) of representational licensing that help to establish the representational nature of this model. Throughout the exploration of the case study, I pause to identify some important lessons which apply more generally about the nature of representational licensing in science. (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)

A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...) models. I present and critically assess two leading accounts of model assessment, arguing that neither is sufficient to provide a complete understanding of model evaluation. I introduce a more powerful framework incorporating elements of the two views, which can help answer these three questions: What is the target of evaluation in model assessment? How does that evaluation proceed? What licenses us in making inferences about the real world, based on the evaluation of our models as successful? The framework identifies two distinct targets of model evaluation: representational similarity between the model and target system, and the adequacy of the model as a tool to answer questions. Both assessments must be relativized to a purpose, of which there are three general kinds: descriptive, predictive, and explanatory. These purposes differ in the way they inform the similarity relation, which is relevant for the similarity assessment, and the output they produce, which is relevant for the adequacy assessment. Any model can be assessed relative to any purpose, however a model encodes certain decisions made during the model’s construction, which impact its ability to be applied to a new purpose or new domain. My framework shows that extending a model, and drawing inferences from it, depends on its representational similarity. I apply this framework to several examples taken from astrophysics showing in detail how it can help illuminate the structure of the models, as well as make the justification for inferences made from them clear. The final chapter is a detailed analysis of a contemporary debate surrounding the use of models in astrophysics, between proponents of MOND and the standard ΛCDM model. (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)

This paper proposes a holistic approach to the model-world relationship, suggesting that the model-world relationship be viewed as an overall structural fit where one organized whole fits another organized whole. This approach is largely motivated by the implausibility of Michael Weisberg’s weighted feature-matching account of the model-world relationship, where a set-theoretic conception of the structures of models is assumed. To show the failure of Weisberg’s account and the plausibility of my approach, a concrete model, i.e. the San Francisco Bay model, (...) is discussed. (shrink)