Transdisciplinarity includes the assumption that within new institutional settings, scientific research becomes more closely responsive to practical problems and user needs and is therefore often subject to considerable application pressure. This raises the question whether transdisciplinarity affects the epistemic standards and the fruitfulness of research. Case studies show how user-orientation and epistemic innovativeness can be combined. While the modeling involved in all cases under consideration was local and focused primarily on features of immediate practical relevance, it was informed by theoretical (...) insights from basic research. Conversely, industrial research turns out sometimes to produce theoretical understanding. These findings highlight an interactive relationship between science and technology (moderate emergentism), which is distinct from the traditional view of a one-sided dependence of technology on science (cascade model) and from the newly received independence account (emergentism). (shrink)
Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, (...) and the social values which justify those rules. Having given our view of these relationships, we present our formal model of them, and explain how theories can be constructed, compared and evaluated. We then show how previous work can be described in terms of our model, and discuss extensions to the basic model to accommodate particular features of previous work. We conclude by identifying some directions for future work. (shrink)
According to the semantic view, a theory is characterized by a class of models. In this paper, we examine critically some of the assumptions that underlie this approach. First, we recall that models are models of something. Thus we cannot leave completely aside the axiomatization of the theories under consideration, nor can we ignore the metamathematics used to elaborate these models, for changes in the metamathematics often impose restrictions on the resulting models. Second, based on a parallel between van Fraassen’s (...) modal interpretation of quantum mechanics and Skolem’s relativism regarding set-theoretic concepts, we introduce a distinction between relative and absolute concepts in the context of the models of a scientific theory. And we discuss the significance of that distinction. Finally, by focusing on contemporary particle physics, we raise the question: since there is no general accepted unification of the parts of the standard model (namely, QED and QCD), we have no theory, in the usual sense of the term. This poses a difficulty: if there is no theory, how can we speak of its models? What are the latter models of? We conclude by noting that it is unclear that the semantic view can be applied to contemporary physical theories. (shrink)
Nancy Cartwright is one of the most distinguished and influential contemporary philosophers of science. Despite the profound impact of her work, until now there has not been a systematic exposition of Cartwright's philosophy of science nor a collection of articles that contains in-depth discussions of the major themes of her philosophy. This book is devoted to a critical assessment of Cartwright's philosophy of science and contains contributions from Cartwright's champions and critics. Broken into three parts, the book begins by addressing (...) Cartwright's views on the practice of model building in science and the question of how models represent the world before moving on to a detailed discussion of methodologically and metaphysically challenging problems. Finally, the book addresses Cartwright's original attempts to clarify profound questions concerning the metaphysics of science. With contributions from leading scholars, such as Ronald N. Giere and Paul Teller, this unique volume will be extremely useful to philosophers of science the world over. (shrink)
http://dx.doi.org/10.5007/1808-1711.2008v12n1p73 This paper aims at discussing from the point of view of a pragmatic stance the concept of model as an abstract replica. According to this view, scientific models are abstract structures different from set-theoretic models. The view of models argued for here stems from the conceptions of some important philosophers of science who elaborated on the notion of model, such as Suppe, Cartwright, Hempel, and Nagel. Differently from all those authors, however, the conception of model argued for here is (...) typically pragmatic, not semantic, i.e. it has not to do with the interpretation of scientific theories, but with the explanation and construction of given circumstances (both abstract and concrete), from the point of view of the theory. (shrink)
Models are of central importance in many scientific contexts. The roles the MIT bag model of the nucleon, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka- Volterra model of predator-prey interaction, agent-based and evolutionary models of social interaction, or general equilibrium models of markets play in their respective domains are cases in point.
Special issue. With contributions by Anouk Barberouse, Sarah Francescelli and Cyrille Imbert, Robert Batterman, Roman Frigg and Julian Reiss, Axel Gelfert, Till Grüne-Yanoff, Paul Humphreys, James Mattingly and Walter Warwick, Matthew Parker, Wendy Parker, Dirk Schlimm, and Eric Winsberg.
This paper contrasts and compares strategies of model-building in condensed matter physics and biology, with respect to their alleged unequal susceptibility to trade-offs between different theoretical desiderata. It challenges the view, often expressed in the philosophical literature on trade-offs in population biology, that the existence of systematic trade-offs is a feature that is specific to biological models, since unlike physics, biology studies evolved systems that exhibit considerable natural variability. By contrast, I argue that the development of ever more sophisticated experimental, (...) theoretical, and computational methods in physics is beginning to erode this contrast, since condensed matter physics is now in a position to measure, describe, model, and manipulate sample-specific features of individual systems – for example at the mesoscopic level – in a way that accounts for their contingency and heterogeneity. Model-building in certain areas of physics thus turns out to be more akin to modeling in biology than has been supposed and, indeed, has traditionally been the case. (shrink)
After a brief presentation of what I take to be the representational démarche in science, I stress the fundamental role of true judgements in model construction. The success and correctness of a representation rests on the truth of judgements which attribute properties to real targeted entities, called “ontic judgements”. I then present what van Fraassen calls “the Loss of Reality objection”. After criticizing his dissolution of the objection, I offer an alternative way of answering the Loss of Reality objection by (...) showing that the contact of our models with reality is grounded on the truth of ontic judgements. I conclude by examining. doi: 10.5007 / 1808-1711.2011v15n3p461. (shrink)
Modeling and simulation clearly have an upside. My discussion here will deal with the inevitable downside of modeling — the sort of things that can go wrong. It will set out a taxonomy for the pathology of models — a catalogue of the various ways in which model contrivance can go awry. In the course of that discussion, I also call on some of my past experience with models and their vulnerabilities.
Many scientific models are non-representational in that they refer to merely possible processes, background conditions and results. The paper shows how such non-representational models can be appraised, beyond the weak role that they might play as heuristic tools. Using conceptual distinctions from the discussion of how-possibly explanations, six types of models are distinguished by their modal qualities of their background conditions, model processes and model results. For each of these types, an actual model example – drawn from economics, biology, psychology (...) or sociology – is discussed. For each case, contexts and purposes are identified in which the use of such a model offers a genuine opportunity to learn – i.e. justifies changing one’s confidence in a hypothesis about the world. These cases then offer novel justifications for modelling practices that fall between the cracks of standard representational accounts of models. (shrink)
Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we show how it (...) is possible to achieve a compact representation of extended causal models. (shrink)
Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)
Simulation (von lat. simulare, engl. simulation, franz. simulation, ital. simulazione), Bezeichnung für die Nachahmung eines Prozesses durch einen anderen Prozeß. Beide Prozesse laufen auf einem bestimmten System ab. Simuliertes u. simulierendes System (der Simulator in der Kybernetik) können dabei auf gleichen oder unterschiedlichen Substraten realisiert sein.
Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. (...) Scientists spend a great deal of time building, testing, comparing and revising models, and much journal space is dedicated to introducing, applying and interpreting these valuable tools. In short, models are one of the principal instruments of modern science. -/- Philosophers are acknowledging the importance of models with increasing attention and are probing the assorted roles that models play in scientific practice. The result has been an incredible proliferation of model-types in the philosophical literature. Probing models, phenomenological models, computational models, developmental models, explanatory models, impoverished models, testing models, idealized models, theoretical models, scale models, heuristic models, caricature models, didactic models, fantasy models, toy models, imaginary models, mathematical models, substitute models, iconic models, formal models, analogue models and instrumental models are but some of the notions that are used to categorize models. While at first glance this abundance is overwhelming, it can quickly be brought under control by recognizing that these notions pertain to different problems that arise in connection with models. For example, models raise questions in semantics (what is the representational function that models perform?), ontology (what kind of things are models?), epistemology (how do we learn with models?), and, of course, in philosophy of science (how do models relate to theory?; what are the implications of a model based approach to science for the debates over scientific realism, reductionism, explanation and laws of nature?). (shrink)
I think van Fraassen is right to see the development of quantum mechanics as a turning point for physical science with a profound moral for philosophy, and not just for the philosophy of science. But the moral is not that even a completely successful physical theory may fail to account for the appearances by showing how they arise within the reality it represents. The moral is more radical: it is that a physical theory – even a fundamental theory – may (...) be completely successful in all its applications without offering a representation of reality at all. (shrink)
My scientific field is theoretical physics. My philosophical orientation is phenomenology, especially hermeneutical phenomenology, as modified and extended under the influence of Bernard Lonergan's cognitional theory. In fact, I was already deeply under the influence of Bernard Lonergan's workbefore I went to Louvain/Leuven to study phenomenology as a propaedeutic to my preparation in the philosophy of science. The specific topic of this paper is one close to the center of Philip's interest, namely, to articulate the right balance among theory, experiment, (...) and what Husserl called 'die Sache selbst' or the 'givenness' of scientific objects as experienced and understood. The method I shall adopt is that of Husserl's phenomenology of perception, as modified by Lonergan's method of 'self-appropriation.' I will be concerned then with the 'constitution' of experimental data in science - any science. (shrink)
In this paper we argue for the thesis that theories are to be considered as representations. The term "representation" is used in a sense inspired by its mathematical meaning. Our main thesis asserts that theories of empirical theories can be conceived as geometrical representations. This idea may be traced back to Galileo. The geometric format of empirical theories should not be simply considered as a clever device for displaying a theory. Rather, the geometrical character deeply influences the theory s ontology. (...) We argue that it would be desastrous for philosophy if it followed Rorty s "neo-pragmatic" proposal to discard the concept of representation from philosophical discourse. (shrink)
Idealizing conditions are scapegoats for scientific hypotheses, too often blamed for falsehood better attributed to less obvious sources. But while the tendency to blame idealizations is common among both philosophers of science and scientists themselves, the blame is misplaced. Attention to the nature of idealizing conditions, the content of idealized hypotheses, and scientists’ attitudes toward those hypotheses shows that idealizing conditions are blameless when hypotheses misrepresent. These conditions help to determine the content of idealized hypotheses, and they do so in (...) a way that prevents those hypotheses from being false by virtue of their constituent idealizations. (shrink)
This paper describes in some detail a pattern of justification which seems to be part of common sense logic and also part of the logic of scientific investigations. Calling this pattern abduction, the paper lays out an abduction-prediction model of scientific inference as an update to the traditional hypothetico-deductive model. According to this newer model, scientific theories receive their claims for acceptance and belief from the abductive arguments that support them, and the processes of scientific discovery aim to develop theories (...) with strong abductive support. It is suggested that the study of diagnosis presents a good opportunity for studying abduction under somewhat simpler and more reproducible conditions than occur in scientific discovery. A computer-based diagnostic system is described which provides a small-scale validation of the abduc-tion-prediction model by showing that a version of it can be made precise enough to be implemented and to perform correctly for diagnosis. (shrink)
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)
Abstract Three main concepts of model in science are distinguished: (1) semantical model of a theory; (2) real model of another real thing; (3) mathematical model of a real thing. The last concept is the most important for the empirical sciences. The mathematical model is not identical with a theory: it is an ideal object which is directly described by the theory. We have here an intermediate level between reality and theory.
Economic theories are systems of beliefs about the world. Models formalize parts or aspects of theories but leave much of their content out. An example of a component of theories not contained in models are the instructions for how to proceed when a model fails (in Lakatos? terms the ?positive heuristic?). Mathematization gives precision of statement but not of empirical reference. The emphasis on explicit models as the main vehicle for journal communication among economists is questioned. The tendency for top (...) talent to cluster in a few universities is much stronger in economics than in most other fields. The reason is the need to talk and listen and not just write and read. (shrink)
This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...) the characteristics of the communities who use these two types of models, including their research goals, disciplinary affiliations, and preferred practices to show how these have contributed to the conceptualization of a model organism. We conclude that model organisms are a specific subgroup of organisms that have been standardized to fit an integrative and comparative mode of research, and that it must be clearly distinguished from the broader class of experimental organisms. In addition, we argue that model organisms are the key components of a unique and distinctively biological way of doing research using models. (shrink)
Read argues that modeling cultural idea systems serves to make explicit the cultural rules through which "cultural idea systems" frame behaviors that are culturally meaningful. Because cultural rules are typically "invisible" to us, one of the anthropologists' tasks is to elicit these rules, make them explicit and then use them to build explanations for patterns in cultural phenomena. The main example of Read's approach to cultural idea systems is the formal modeling of kinship terminologies. I reconstruct Read's modeling strategy as (...) comprising the following steps:From the way in which culture-bearers compute kin relations a data model is construed that makes explicit the cultural theory embedded in a kinship .. (shrink)
The contrastive approach to explanation is employed to shed light on the issue of the unrealisticness of models and their assumptions in economics. If we take explanations to be answers to contrastive questions of the form, then unrealistic elements such as omissions and idealizations are (at least partly) dependent on the selected contrast. These contrast?dependent assumptions are shown to serve the function of fixing the shared causal background between the fact and the foil. It is argued that looking at the (...) explanations offered by economic models in contrastive terms helps to be precise about their explanatory potential, and hence, to assess the adequacy of their unrealistic assumptions. I apply the insights of the contrastive approach to the ?new economic geography? models, and to a selection of criticisms directed at them. This case illustrates how a contrastive analysis can help the solution of disputes concerning the unrealisticness of particular models. (shrink)
A comparison of models and experiments supports the argument that although both function as mediators and can be understood to work in an experimental mode, experiments offer greater epistemic power than models as a means to investigate the economic world. This outcome rests on the distinction that whereas experiments are versions of the real world captured within an artificial laboratory environment, models are artificial worlds built to represent the real world. This difference in ontology has epistemic consequences: experiments have greater (...) potential to make strong inferences back to the world, but also have the power to isolate new phenomena. This latter power is manifest in the possibility that whereas working with models may lead to ?surprise?, experimental results may be unexplainable within existing theory and so ?confound? the experimenter. (shrink)
Philosophers of science studying scientific practice often consider it a methodological requirement that their conceptualization of "model" closely connects with the understanding and use of models by practicing scientists. Occasionally, this connection has been explicitly made (Hutten 1954, Suppes 1961, Morgan and Morrison 1999, Bailer-Jones 2002, Lehtinen and Kuorikoski 2007, Kuorikoski 2007, Morgan 2012a). These studies have been dominated by a focus on the—relatively similar forms of—mathematical models in physics and economics. Yet it has become increasingly evident that the way (...) models are conceptualized is very different in some other sciences, where philosophers' accounts of models' characteristics and .. (shrink)
The paper examines philosophical issues that arise in contexts where one has many different models for treating the same system. I show why in some cases this appears relatively unproblematic (models of turbulence) while others represent genuine difficulties when attempting to interpret the information that models provide (nuclear models). What the examples show is that while complementary models needn’t be a hindrance to knowledge acquisition, the kind of inconsistency present in nuclear cases is, since it is indicative of a lack (...) of genuine theoretical understanding. It is important to note that the differences in modeling do not result directly from the status of our knowledge of turbulent flows as opposed to nuclear dynamics—both face fundamental theoretical problems in the construction and application of models. However, as we shall, the ‘problem context(s)’ in which the modeling takes plays a decisive role in evaluating the epistemic merit of the models themselves. Moreover, the theoretical difficulties that give rise to inconsistent as opposed to complementary models (in the cases I discuss) impose epistemic and methodological burdens that cannot be overcome by invoking philosophical strategies like perspectivism, paraconsistency or partial structures. (shrink)
According to the commonsensical model of educating researchers, young researchers must first acquire the knowledge achieved thus far and then solve new problems by developing applications of the accepted theory. This model, which presupposes a positivist theory of science, is incapable of explaining why the major breakthroughs in science have been carried out by young researchers. On the idealizational view of science, it becomes clear that commonsensical model must be rejected and replaced with an alternative, according to which the primary (...) duty of young researchers is to revise the existing theories. It is the young researchers who are usually creative enough, ignorant enough, and exhibit a sufficient degree of nonconformism, to be capable of developing really new scientific theories. (shrink)
En este trabajo me propongo desarrollar un estudio crítico de la concepción mecanicista de la explicación científica. En primer lugar, argumento que la caracterización mecanicista de los modelos fenoménicos (no explicativos) es inadecuada, pues no ofrece un análisis aceptable de los conceptos de modelo científico y similitud, que son fundamentales para la propuesta. En segundo lugar, sostengo que la caracterización de los modelos mecanicistas (explicativos) es igualmente inadecuada, pues los análisis disponibles de la relación explicativa de relevancia constitutiva implican una (...) tesis metafísica que es rechazada por los mismos mecanicistas. Concluyo que el mecanicismo no ofrece todavía una elucidación aceptable de la explicación científica. In this paper, I offer a critical assessment of the mechanicist approach to scientific explanation. Firstly, I argue that the mechanicist characterization of (non explanatory) phenomenological models is inadequate, since it does not develop an explication of the concepts of scientific model and similarity, which are indispensable to the approach. Secondly, I claim that the mechanicist conception of (explanatory) mechanicist models is inadequate as well, since all the available analices of the explanatory relation of constitutive relevance imply a metaphysical thesis that is rejected by the mechanicists themselves. I conclude that mechanicism needs to be emended if it aims to be considered as a genuinely illuminating approach to scientific explanation. (shrink)
Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is conceptual (...) clarification, with a redefinition of concepts central to the understanding of the phenomenon and the conditions of its occurrence. (shrink)
A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system(s). Furthermore, in many contexts, it is in virtue of (...) their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach. (shrink)
Progress in the last few decades in what is widely known as “Chaos Theory” has plainly advanced understanding in the several sciences it has been applied to. But the manner in which such progress has been achieved raises important questions about scientific method and, indeed, about the very objectives and character of science. In this presentation, I hope to engage my audience in a discussion of several of these important new topics.
The purpose of this paper is to point out the logical priority of the existential grounds of picturing reality by means of scientific representations, hypotheses as such. Also, to clarify the meaning of the inscribing and reading of the picture in terms of the existential conditions and facts of the human being who acts and reacts for survival, and who interprets its surroundings in connection with the train of consequences that connects up with this human action. The surrounding world thus (...) is recognized and interpreted in terms of playing and operating with signs, the significations of which make up the horizons of the world of the human being. This clarification is needed to throw light on how concepts mean in the application of words in language. And the clarity reached at this stage helps for us to clarify further the meaning of thinking and its relation to language-use in terms of playing and operating with signs in the conditions of the surrounding world, the action of the human body in its existential situation. Hence, the logical priority of the human condition in terms of the use and application of signs in the existential world of human being differs from the analytical representations of the world in science for scientific purposes. Which means that the representations of science are tools of the language, and that they are to be treated and interpreted as signs used to represent reality only in the scientific contexts, for the purposes of the language of science and scientific culture. Without such clarity, representations of science, scientific descriptions of reality are open to misinterpretation even by scientists and philosophers, let alone layman, to be so generalized to extend the bounds of its meaningful application in the scientific context of explaining or describing phenomena experimented, or observed under certain experimental conditions. (shrink)
In his article “Constructive Empiricism Now” van Fraassen chooses an extremely interesting example to defend his thesis that scientific theories are only representations, so that the aim of science is to give us reliable, empirically adequate, descriptions of the observable aspects of the world. For him, there is no continuum of observable/unobservable, as he draws a line of distinction at a point that eliminates from his ontology such cases as fields of forces and sub-atomic particles. As a result, he puts (...) forward the position that electronic images in the microscope and subatomic particles are “public hallucinations” and not “real things”. What I thus propose to do is to examine van Fraassen’s anti-realism through the looking class of realism, my aim being to defend a realist view of science: To this purpose, I will focus on two main issues: (a) the question of representations in science and in particular of images we “see” through a microscope and (b) the question of the criteria for defining physical reality. In this context, I will argue that van Fraassen’s definition of the “real” is an anti-realist version of the positivist trend, which cannot fit in the picture of science that emerges today. To understand, thus, the world of physics we need to re-examine our definition of reality and make space for an ontology that goes beyond the well-defined spatio-temporal existence of what van Fraassen calls a “real thing”. (shrink)
1. Preliminary Reconnaissance: Realism, Instrumentalism, and Interpretation On the one hand, I think it is fair to say that philosophers recognize a special problem or question about how we are to “interpret” scientific theories only in light of their concerns about whether we are really entitled to believe what those theories say when they are interpreted in what we see as the most natural or straightforward or intuitive way. On the other hand, this fundamental worry reaches all the way back (...) to the inception of scientific inquiry itself, no matter how liberally we conceive of that enterprise. Before the relatively recent professionalization of academic fields, such concerns were well-represented among the figures who served simultaneously as both the leading practitioners and the leading philosophers of science. This is nicely illustrated by the strident debates throughout this community in the 18th and 19th centuries concerning whether only pure inductive methods were legitimate for scientific inquiry and/or whether the competing “method of hypothesis” could produce any genuine knowledge of nature (see Laudan 1981 Ch. 8). (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)