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)

Diagrams can serve as representational models in scientific research, yet important questions remain about how they do so. I address some of these questions with a historical case study, in which diagrams were modified extensively in order to elaborate an early hypothesis of protein synthesis. The diagrams’ modelling role relied mainly on two features: diagrams were modified according to syntactic rules, which temporarily replaced physico-chemical reasoning, and diagram-to-target inferences were based on semantic interpretations. I then explore the lessons for (...) the relative roles of syntax, semantics, external marks, and mental images, for justifying diagram-to-target inferences, and for the “artefactual approach” to scientific models. (shrink)

It is argued that complexity is not attributable directly to systems or processes but rather to the descriptions of their `best' models, to reflect their difficulty. Thus it is relative to the modelling language and type of difficulty. This approach to complexity is situated in a model of modelling. Such an approach makes sense of a number of aspects of scientific modelling: complexity is not situated between order and disorder; noise can be explicated by approaches to excess modelling error; (...) and simplicity is not truth indicative but a useful heuristic when models are produced by a being with a tendency to elaborate in the face of error. (shrink)

The Covid-19 global pandemic had a profound effect on scientific practice. During this time, officials crucially relied on the work done by modellers. This raises novel questions for the philosophy of science. Here, I investigate the possibility of ‘natural models’ in predicting the virus’ trajectory for epidemiological purposes. I argue that to the extent that these can be consideredscientific models, they support the possibility of a continuum from scientific models to natural models differing in artifactual commitment. In making (...) my case, I draw from work on model organisms and natural experiments as well as recent work in epidemiology. (shrink)

Nicole C. Karafyllis and Gotlind Ulshöfer (Eds): Sexualised Brains, Scientific Modelling of Emotional Intelligence from a Cultural Perspective Content Type Journal Article Category Book Review Pages 407-408 DOI 10.1007/s12376-009-0035-3 Authors Antje Kampf, School of Medicine of the Johannes Gutenberg University Mainz Institute for the History, Philosophy and Ethics of Medicine Am Pulverturm 13 55131 Mainz Germany Journal Medicine Studies Online ISSN 1876-4541 Print ISSN 1876-4533 Journal Volume Volume 1 Journal Issue Volume 1, Number 4.

In this paper, I critique a recent claim made by Stokhof and van Lambalgen (2011) (hereafter S&vL) that linguistics and science are at odds as to the models and constructions they employ. I argue that their distinction between abstractions and idealisations, the former belonging to the methodology of science and the latter to linguistics, is not a real one. I show that the majority of their arguments are flawed and evidence they cite misleading. Contrary to this distinction, I argue that (...) linguistics, like some variants of the scientific enterprise, uses a minimalist method of idealisation (Weisberg, 2007b), one which includes abstractions (as defined by S&vL) and other idealisations not uncommon to scientific model-building. Finally, I offer an alternative account of the problems cited by S&vL as a direct result of the modelling choices of linguists as opposed to the methods they use to define such models. I do so through the use of the specific example of the treatment of tense and aspect in the mainstream literature. (shrink)

How do scientific models represent in a way that enables us to discover new truths about reality and draw inferences about it? Contemporary accounts of scientific discovery answer this question by focusing on the cognitive mechanisms involved in the generation of new ideas and concepts in terms of a special sort of reasoning—or model-based reasoning—involving imagery. Alternatively, I argue that answering this question requires that we recognise the crucial role of the propositional imagination in the construction and development (...) of models for the purpose of generating hypotheses that are plausible candidates for truth. I propose simple fictionalism as a new account of models as Waltonian games of make-believe and suggest that models can lead to genuine scientific discovery when they are used as representations that denote real world phenomena and generate two main kinds of theoretical hypotheses, model-world comparisons and direct attributions. (shrink)

This monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also presents (...) a comprehensive statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups. (shrink)

The recognition that models and simulations play a central role in the epistemology of science is about fifteen years old. Although models had long been discussed as possible foundational units in the logical analysis of scientific knowledge, the philosophical study of modelling as a distinct epistemic practice really got going in the wake of the Models as Mediators anthology edited by Margaret Morrison and Mary Morgan. In spite of the broad agreement that in fact much of science is model-based, (...) however, there is still little agreement on pretty much anything else. What are models? Are they representations or fictions, abstract entities or concrete artifacts? Which functions do they play? Can they explain.. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise (...) algorithmic computation and to offer foundations for scientific computing. The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

Modeling is a relatively new topic in biblical and related subjects—it was first introduced in the 1970s—and it is controversial because the application of social-scientific models raises the difficult question of the cultural gap between the present societies, where the models are usually developed, and the ancient cultural context to which the models are applied.Because biblical and related studies may not belong to the most familiar scholarly fields of the readers of this journal, I first sketch an overall (...) picture of the development of the discipline and its main methods (Section 2). The subsequent sections summarize the arguments presented for and against modeling in biblical studies (Section 3), and discuss .. (shrink)

Loop analysis is a method of qualitative modeling anticipated by Sewall Wright and systematically developed by Richard Levins. In Levins’ (1966) distinctions between modeling strategies, loop analysis sacrifices precision for generality and realism. Besides criticizing the clarity of these distinctions, Orzack and Sober (1993) argued qualitative modeling is conceptually and methodologically problematic. Loop analysis of the stability of ecological communities shows this criticism is unjustified. It presupposes an overly narrow view of qualitative modeling and underestimates the (...) broad role models play in scientific research, especially in helping scientists represent and understand complex systems. (shrink)

This article aims to answer what I call the “constitution question of engineering modeling”: in virtue of what does an engineering model model its target system? To do so, I will offer a category-theoretic, structuralist account of design, using the olog framework. Drawing on this account, I will conclude that engineering and scientific models are not only cognitively but also representationally indistinguishable. I will finally propose an axiological criterion for distinguishing scientific from engineering modeling.

Complex systems are used, studied and instantiated in science, with what con-sequences? To be clear and systematic in response it is necessary to distin-guish the consequences, for science, of science using and studying complex systems, for philosophy of science, of science using and studying complex systems, for philosophy of science, of philosophy of science modelling sci-ence as a complex system. Each of these is explored in turn, especially. While has been least studied, it will be shown how modelling science as (...) a complex process may change our conception of science and thereby query what a philosophy of science adequate to this complexity might look like. (shrink)

If we consider modeling not as a heap of contingent structures, but (where possible) as evolving coordinated systems of models, then we can reasonably explain as "direct representations" even some very complicated model-based cognitive situations. Scientific modeling is not as indirect as it may seem. "Direct theorizing" comes later, as the result of a successful model evolution.

In recent years, there has been increasing interest in investigating science communication. Some studies that address this issue attempt to develop a model to determine the level of confidence that an author or a scientific community has at a given time towards a theory or a group of theories. A well-established approach suggests that, in order to determine the level of certainty authors have with regard to the statements they make, one can identify specific lexical and morphosyntactical markers which (...) indicate their epistemic attitudes. This method is considered particularly appealing because it permits the development of an algorithmic model based on the quantitative analysis of the occurrence of these markers to assess automatically and objectively the opinion of an author or the predominant opinion of a scientific community on a topic at a given time. In this contribution we show that this line of research presents many kinds of problems especially when it is applied to research articles. To this aim, we propose two main lines of reasoning. The first one relies generally on the argumentative structure of scientific articles and shows that certainty/uncertainty markers are used differently in different argument forms and that therefore their number/frequency of use does not offer reliable indications for determining whether the topic at issue is considered by the authors to be more or less factual/speculative. The second one is based on the analysis of a sample of psychiatric research articles on homosexuality written over a long time span and taken from The British Journal of Psychiatry. Since the psychiatric perspective on homosexuality changed radically during the decades in which these articles were published, they offer an inventory of various kinds of argumentative strategies directed both at defending and confuting dominant as well as marginal positions. We focus especially on uncertainty markers and show that frequently the positions stated using expressions indicating uncertainty are actually not considered as conjectural or speculative by their authors, but that the use of uncertainty markers is motivated by a number of different and often incongruent rhetorical strategies. (shrink)

The field of epistemic logic developed into an interdisciplinary area focused on explicating epistemic issues in, for example, artificial intelligence, computer security, game theory, economics, multiagent systems and the social sciences. Inspired, in part, by issues in these different ‘application’ areas, in this paper I propose an epistemic logic T for metadata extracted from scientific papers on COVID-19. More in details, I introduce a structure S to syntactically and semantically modelling metadata extracted with systems for extracting structured metadata from (...) scientific articles in a born-digital form. These systems will be considered, in the logical model created, as ‘Metadata extraction agents’. In this case MEA taken into consideration are CERMINE and TeamBeam. In an increasingly data-driven world, modelling data or metadata means to help systematise existing information and support the research community in building solutions to the COVID-19 pandemic. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise (...) algorithmic computation and to offer foundations for scientific computing. -/- The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. -/- In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. -/- In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. (...) Some have also drawn parallels between models and fiction. This chapter examines these approaches to scientific modeling and considers the challenges they face. (shrink)

In this paper I examine the epistemic function of agent-based models of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni’s classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the last category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, (...) but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman’s ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and an argumentation-based ABM, which provides insights into potential mechanisms underlying the efficiency of scientific inquiry. (shrink)

Evolutionary anthropologists and archaeologists have been considerably successful in modelling the cumulative evolution of culture, of technological skills and knowledge in particular. Recently, one of these models has been introduced in the philosophy of science by De Cruz and De Smedt (Philos Stud 157:411–429, 2012), in an attempt to demonstrate that scientists may collectively come to hold more truth-approximating beliefs, despite the cognitive biases which they individually are known to be subject to. Here we identify a major shortcoming in that (...) attempt: De Cruz & De Smedt’s mathematical model makes one particularly strong tractability assumption that causes the model to largely miss its target (namely, truth accumulation in science), and that moreover conflicts with empirical observations. The second, more constructive part of the paper presents an alternative, agent-based model, which allows one to much better examine the conditions for scientific progress and decline. (shrink)

Counterfactual reasoning has been used to account for many aspects of scientific reasoning. More recently, it has also been used to account for the scientific practice of modeling. Truth in a model is truth in a situation considered as counterfactual. When we reason with models, we reason with counterfactuals. Focusing on selected models like Bohr’s atom model or models of population dynamics, I present an account of how the imaginative development of a counterfactual supposition leads us from (...) reality to interesting model assumptions; how it guides our reasoning from these assumptions to interesting consequences for the model scenario via counterfactual entailment; and how it leads us back to conclusions on real target phenomena. (shrink)

Just before the Scientific Revolution, there was a "Mathematical Revolution", heavily based on geometrical and machine diagrams. The "faculty of imagination" (now called scientific visualization) was developed to allow 3D understanding of planetary motion, human anatomy and the workings of machines. 1543 saw the publication of the heavily geometrical work of Copernicus and Vesalius, as well as the first Italian translation of Euclid.

Scientific modelling is a value-laden process: the decisions involved can seldom be made using ‘scientific’ criteria alone, but rather draw on social and ethical values. In this paper, we draw on a body of philosophical literature to analyze a COVID-19 vaccination model, presenting a case study of social and ethical value judgments in health-oriented modelling. This case study urges us to make value judgments in health-oriented models explicit and interpretable by non-experts and to invite public involvement in making (...) them. (shrink)

Nelson Goodman argued convincingly that in order to understand the representation relation one should dissociate it from the relation of resemblance because of the logical differences between the two concepts. Resemblance is reflexive and symmetric whereas representation is not. Furthermore, Goodman suggested that what lies at the core of representation is denotation. According to Goodman, if X represents Y then X must denote Y, but he recognized that by opting for an analysis of representation only based on this idea of (...) denotation we run into problems. There are two reasons for this. The first reason is that X could be considered to be a representation of Y by mere stipulation. The second reason is that in many cases X represents Y but Y does not exist and thus there is nothing that X denotes. Both of these problems are important when addressing questions about scientific representation. First, we do not think that we do justice to scientific practices by thinking that scientific models represent by mere stipulation. Second, some of our scientific models represent what we often label ideal systems or ideal states of affairs, and if such systems do not exist in the actual world then it would also not make much sense to claim that our models denote such systems. I argue in this paper that there is a way to overcome these two problems and explicate “representation” by means of “denotation”. (shrink)

Quantitative assessments of when infectious disease outbreaks end are crucial, as resources targeted towards outbreak responses typically remain in place until outbreaks are declared over. Recent improvements and innovations in mathematical approaches for determining when outbreaks end provide public health authorities with more confidence when making end-of-outbreak declarations. Although quantitative analyses of outbreaks have a long history, more complex mathematical and statistical methodologies for analysing outbreak data were developed early in the 20th century and continue to be refined. Historically, such (...) methodologies focused primarily on factors affecting the early and middle phases of an outbreak, with less attention given to determining how and when outbreaks end. This review discusses mathematical modelling methods from the last 20 years that have been developed for determining the ends of infectious disease outbreaks, and considers factors that affect the accuracy of such determinations. When disease surveillance systems provide timely and representative data to inform models, the timings of end-of-outbreak declarations can be fine-tuned to allow outbreaks to be declared over quickly and with a low risk of being incorrect. Premature declarations that outbreaks are over can undermine earlier achievements in disease control and may result in a resurgence of cases, but unnecessary delays in declaring outbreaks over can cause significant economic and social harm. Appropriate declarations that balance the benefits of relaxing control measures against the risk of a surge in cases allow public health resources to be conserved (and economic and social pressures to be reduced) while limiting the potential for additional transmission. (shrink)

ion is one of the important processes in scientific modeling. It has always been implied that abstraction is an agent-centric activity that involves the cognitive processes of scientists in model building. I contend that there is an autonomous aspect of abstraction in many modeling activities. I argue that the autonomous process of abstraction is continuous with the agent-centric abstraction but capable of evolving independently from the modeler’s abstraction activity.

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on (...) the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

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

Understanding what is distinctive about the role of models in science requires characterizing broad patterns in how these models evolve in the face of experimental results. That is, we must examine not just model statics—how the model relates to theory, or represents the world, at some point in time—but also model dynamics—how the model both generates new experimental results and is modified in response to them. Visual representations of structure play a central role in the theoretical reasoning of organic chemists. (...) Not surprisingly, these representations have changed in important ways–in response to experimental and theoretical developments– throughout the history of organic chemistry. In many cases it is appropriate to understand the visual representations used by organic chemists as models. The evolution of the structural representations of organic chemists therefore provides a clear example of the dynamics of scientific modeling. In this paper I use a conception of scientific modeling drawn from the work of Mary Hesse to examine how the concept of a molecular conformation was incorporated into the visual representations of structure used by organic chemists. (shrink)

In this paper, we argue that symbols are conventional vehicles whose chief function is denotation, while models are epistemic vehicles, and their chief function is to show what their targets are like in the relevant aspects. And we explain why this is incompatible with the deflationary view on scientific modeling. Although the same object may serve both functions, the two vehicles are conceptually distinct and most models employ both elements. With the clarification of this point we offer an (...) alternative account to the deflationary view – the Hybrid Account; and we defend our account in contrast with deflationism. (shrink)

What are theoretical models and how do they contribute to a scientific understanding of reality? In this chapter, I will argue that models are akin to fictional stories in that they are human-made artifacts created through the imaginative activities of scientists. And I will suggest that the sort of imagination involved in modeling is make-believe and that this is constrained in three main ways which, together, enable knowledge of reality. I will conclude by addressing recent criticisms against the (...) fiction view of models and the relevance of scientific imagination in modeling put forward by Weisberg and Knuuttila. (shrink)

Scientists confronted with multiple explanatory hypotheses as a result of their abductive inferences, generally want to reason further on the different hypotheses one by one. This paper presents a modal adaptive logic MLA s that enables us to model abduction in such a way that the different explanatory hypotheses can be derived individually. This modelling is illustrated with a case study on the different hypotheses on the origin of the Moon.

I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are (...) characterised as modelling idealisations motivated normatively. I then survey the landscape of methodological options: what might formal epistemologists be up to? I argue the choice is essentially binary: modelling or theorising. If NFE is theorising it is doing very poorly: generating false claims with no clear methodology for separating out what is to be taken seriously. Modelling, by contrast, is a successful methodology precisely suited to the management of useful falsehoods. Regarding NFE as modelling is not costless, however. First, our normative inferences are less direct and are muddied by the presence of descriptive idealisations. Second, our models are purpose-specific and limited in their scope. I close with suggestions for how to adapt our practice. (shrink)

Scientific representation is a currently booming topic, both in analytical philosophy and in history and philosophy of science. The analytical inquiry attempts to come to terms with the relation between theory and world; while historians and philosophers of science aim to develop an account of the practice of model building in the sciences. This article provides a review of recent work within both traditions, and ultimately argues for a practice-based account of the means employed by scientists to effectively achieve (...) representation in the modelling sciences. (shrink)

La variedad de metodologías científicas dedicadas a obtener conocimiento, generar creencias y motivarla acción es amplia. La filosofía de la ciencia y de la educación ha valorado críticamente las virtudes de los diversos métodos científicos, en especial de los inductivos y deductivos. Sin embargo, la aparición de nuevos procedimientos vinculados a ciencias no académicas ha promovido el desarrollo de nuevas perspectivas reflexivas que analicen dichas virtudes. Desde los métodos controlados aleatorios hasta los procedimientosepidemiológicos o clínicos, la filosofía ha examinado las (...) virtudes y también los defectos de su puesta en práctica. El presente artículo asume que la modelación basada en evidencias empíricas es una práctica de alto interés en linguistica. Con el fin de sustanciar tal asunción, se comparan dos enfoques filosóficos de la modelación científicadistinguidos por sus respectivas líneas de investigación en torno a la noción de representación: el representacionaly el pragmático. Los enfoques se ilustran posteriormente con un caso de la linguística denominado análisissintáctico del lenguaje, dirigido a examinar muestras particulares recogidas como evidencias en fases iniciales de la modelación experimental. Como conclusión, se enfatiza que ambos enfoques filosóficos aportan elementosanalíticos realmente pertinentes para el tipo de razonamiento científico que pivota en torno a modelos y cuyo alcance en la enseñanza de las ciencias puede resultar de gran interés práctico. (shrink)

Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the (...) hierarchical nature of mechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem can be solved by combining two sorts of solution strategy in a judicious way. (shrink)

This is a paper about the methodology of normative ethics. I claim that much work in normative ethics can be interpreted as modelling, the form of inquiry familiar from science, involving idealised representations. I begin with the anti-theory debate in ethics, and note that the debate utilises the vocabulary of scientific theories without recognising the role models play in science. I characterise modelling, and show that work with these characteristics is common in ethics. This establishes the plausibility of my (...) interpretation. Taking methodological inspiration from modelling in science gives us new tools for managing idealisations, and a new perspective on pluralism. I demonstrate why this interpretation is a fruitful way of interpreting ethics, by looking at three case studies. First, I return to the anti-theory debate and argue that modelling opens up a new middle ground. Second, I argue that a modelling lens offers a new way of understanding impossibility theorems in population ethics, and their bearing on ethics as a whole. Finally, I show how viewing our work as modelling can be deployed in debates within ethics, using the debate over prioritarianism as an example. I close with further methodological suggestions for those who choose to see themselves as modellers. I discuss the role of counterexamples, our responses to moral disagreement, and the training of new ethicists. (shrink)

We used agent-based modelling to highlight the advantages and disadvantages of several management styles in biology, ranging from centralized to egalitarian ones. In egalitarian groups, all team members are connected with each other, while in centralized ones, they are only connected with the principal investigator. Our model incorporated time constraints, which negatively influenced weakly connected groups such as centralized ones. Moreover, our results show that egalitarian groups outperform others if the questions addressed are relatively simple or when the communication among (...) agents is limited. Complex epistemic spaces are explored best by centralized groups. They outperform other team structures because the individual members can develop their own ideas with less interference of the opinions of others. The optimal ratio between time spent on experimentation and dissemination varies between different organizational structures. Furthermore, if the evidence is shared only after a relevant degree of certainty is reached, all investigated groups epistemically profit. We discovered that the introduction of seminars to the model changes the epistemic performance in favour of weakly connected teams. Finally, the abilities of the principal investigator do not seem to outperform cognitive diversity, as group performances were not strongly influenced by the increase of her abilities. (shrink)

Making sense of why something succeeded or failed is central to scientific practice: it provides an interpretation of what happened, i.e. an hypothesized explanation for the results, that informs scientists’ deliberations over their next steps. In philosophy, the realism debate has dominated the project of making sense of scientists’ success and failure claims, restricting its focus to whether truth or reliability best explain science’s most secure successes. Our aim, in contrast, will be to expand and advance the practice-oriented project (...) sketched by Arthur Fine in his work on the Natural Ontological Attitude. An important obstacle to articulating a positive program, we suggest, has been overlooking how scientists adopt standardized rules and procedures in order to define and operationalize meanings for success and failure relative to their situated goals. To help fill this gap, we introduce two new ideas, design specifications and track records, and show how they advance our ability to make sense of scientific modeling practices while maintaining a deflationary stance toward the realism debate. (shrink)

How climate models came to gain and exercise epistemic authority has been a key concern of recent climate change historiography. Using newly released archival materials and recently conducted interviews with key actors, we reconstruct negotiations between UK climate scientists and policymakers which led to the opening of the Hadley Centre for Climate Prediction and Research in 1990. We historicize earlier arguments about the unique institutional culture of the Hadley Centre, and link this culture to broader characteristics of UK regulatory practice (...) and environmental politics. A product of a particular time and place, the Hadley Centre was shaped not just by scientific ambition, but by a Conservative governmental preference for ‘sound science’ and high evidential standards in environmental policymaking. Civil servants sought a prediction programme which would appeal to such sensibilities, with transient and regional climate simulation techniques seemingly offering both scientific prestige and persuasive power. Beyond the national level, we also offer new insights into the early role of the Intergovernmental Panel on Climate Change and an evolving international political context in the shaping of scientific practices and institutions. (shrink)

ABSTRACTThis is a critical discussion and proposed refinement of the inspiring account of the successes and failures of economic modelling sketched in Dani Rodrik’s Economics Rules. The refinements make use of a systematic framework of the structure of scientific modelling. The issues include distinguishing the discipline of economics from the behaviour and attitudes of economists as targets of normative assessment; nature and sources of success and failure in modelling; the key role of model commentary; model transparency; purposes and audiences (...) of modelling; the nature of critical assumptions; negligibility, applicability, and tractability in modelling; the possibility of generally applicable models; and economics fundamentalism. The proposed refinements submit sharper formulations for some of Rodrik’s ideas and supplement others, but they also reveal tensions and relieve some – not yet all – of these. (shrink)