Christoph Baumberger

Model evaluation for long term climate predictions must be done on quantities other than the actual prediction, and a comprehensive uncertainty quantification is impossible. An ad hoc alternative is provided by coordinated model intercomparisons which typically use a “one model one vote” approach. The problem with such an approach is that it treats all models as independent and equally plausible. Reweighting all models of the ensemble for performance and dependence seems like an obvious way to improve on model democracy, yet there are open questions on what constitutes a “good” model, how to define dependency, how to interpret robustness, and how to incorporate background knowledge. Under¬standing those issues has the potential to increase confidence in model predictions in modeling efforts outside of climate science where similar challenges exist.

In recent years, the ability to gather and store information has increased dramatically, and the ability to make use of these increasing volumes of data has improved. This advent of big data has opened up new opportunities for scientific research, including for research on climate change. These changes are associated with a number of interesting philosophical questions. This chapter provides an introduction to these questions. It starts by first clarifying terminological issues concerning “big data” and related terms and by giving an overview of big data elements that can be found in climate research. Second, it discusses data in climate research with a focus on new developments regarding the increase in the volume and complexity of climate data and on how the uncertainty of climate datasets may be assessed. Finally, the chapter addresses the topic of machine learning in climate research and specifically the use of machine learning for the data-driven modeling of climate phenomena. The focus of this discussion is on the representational accuracy of data-driven models and how it might be assessed and what this implies for their use for predictions and for understanding.

The notion of exemplification is essential for Goodman’s theory of symbols. But Goodman’s account of exemplification has been criticized as unclear and inadequate. He points out two conditions for an object x exemplifying a label y: (C1) y denotes x and (C2) x refers to y. While (C1) is uncontroversial, (C2) raises the question of how “refers to” should be interpreted. This problem is intertwined with three further questions that consequently should be discussed together with it. Are the two necessary conditions (C1) and (C2) conjointly sufficient? Do they amount to a definition of “exemplification”? Which notions of Goodman’s theory are basic, and hence undefined? In this paper, we address these questions and defend a reconstruction of the notion of exemplification that interprets “refers to” in (C2) as exemplificational reference and hence treats “exemplification” as a basic notion of Goodman’s theory. Firstly, we argue that even though the notion of exemplification is not defined, it is still sufficiently clear. This ensures its contribution to Goodman’s theory of symbols. Secondly, we show that our account is plausible as an interpretation of Goodman’s and Elgin’s writings, although it implies that some of Goodman’s theorems about selfreference have to be weakened. Thirdly, we argue that it is the only materially adequate reconstruction of Goodman’s notion of exemplification, whereas the alternative definitional accounts fail.

In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding , specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent. When citing this paper, please use the full journal title Studies in History and Philosophy of Science.

Model evaluation for long-term climate predictions must be done on quantities other than the actual prediction, and a comprehensive uncertainty quantificationUncertainty quantification is impossible. An ad hoc alternative is provided by coordinated model intercomparisonsModel intercomparisons which typically use a “one model one vote” approach. The problem with such an approach is that it treats all models as independent and equally plausible. Reweighting all models of the ensemble for performance and dependence seems like an obvious way to improve on model democracy, yet there are open questions on what constitutes a “good” model, how to define dependency, how to interpret robustnessRobustness, and how to incorporate background knowledgeBackground knowledge. UnderstandingUnderstanding those issues have the potential to increase confidence in model predictions in modeling efforts outside of climate scienceClimate science where similar challenges exist.

Based on a framework that distinguishes several types, roles and functions of values in science, we discuss legitimate applications of values in the validation of computer simulations. We argue that, first, epistemic valuesEpistemic values, such as empirical accuracyAccuracy and coherence with background knowledgeBackground knowledge, have the role to assess the credibilityCredibility of simulation results, whereas, second, cognitive valuesCognitive values, such as comprehensiveness of a conceptual modelConceptual model or easy handling of a numerical model, have the role to assess the usefulness of a model for investigating a hypothesisHypothesis. In both roles, values perform what we call first-order functions. In addition, cognitive values may also serve an auxiliary function by facilitating the assessment of credibility. As for a third type of values, i.e. social valuesSocial values, their legitimate role consists in specifying and weighing epistemic and cognitive values with respect to practical uses of a simulation, which is considered a second-order function. Rational intersubjective agreementAgreement on how to specify and weigh the different values is supposed to ensure objectivity in simulation validation.

In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles — parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets.

The paper provides a systematic overview of recent debates in epistemology and philosophy of science on the nature of understanding. We explain why philosophers have turned their attention to understanding and discuss conditions for “explanatory” understanding of why something is the case and for “objectual” understanding of a whole subject matter. The most debated conditions for these types of understanding roughly resemble the three traditional conditions for knowledge: truth, justification and belief. We discuss prominent views about how to construe these conditions for understanding, whether understanding indeed requires conditions of all three types and whether additional conditions are needed.

Elgin has presented an extensive defence of reflective equilibrium embedded in an epistemology which focuses on objectual understanding rather than ordinary propositional knowledge. This paper has two goals: to suggest an account of reflective equilibrium which is sympathetic to Elgin’s but includes a range of further developments, and to analyse its role in an account of understanding. We first address the structure of reflective equilibrium as a target state and argue that reflective equilibrium requires more than an equilibrium in the sense of a coherent position. On the one hand, the position also needs to be stable between a ‘conservative’ pull of input commitments and a ‘progressive’ pull of epistemic goals; on the other hand, reflective equilibrium requires that enough of the resulting commitments have some credibility independent of the coherence of the position. We then turn to the dynamics of reflective equilibrium, the process of mutual adjustment of commitments and theories. Here, the most pressing internal challenges for defenders of reflective equilibrium arise: to characterize this process more exactly and to explain its status in relation to reflective equilibrium as a target state. Finally, we investigate the role of reflective equilibrium in Elgin’s account of understanding and argue that objectual understanding cannot be explained in terms of reflective equilibrium alone. An epistemic agent who understands a subject matter by means of a theory also needs to be able to use this theory and the theory needs to meet some external rightness condition.

The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: an epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to the degree that the agent grasps the theory (i.e., is able to make use of it), the theory answers to the facts and the agent’s commitment to the theory is justified. The threshold for outright attributions of understanding is determined contextually. The explication has descriptive as well as normative facets and allows for the possibility of understanding by means of non-explanatory (e.g., purely classificatory) theories.

Science has not only produced a vast amount of knowledge about a wide range of phenomena, it has also enhanced our understanding of these phenomena. Indeed, understanding can be regarded as one of the central aims of science. But what exactly is it to understand phenomena scientifically, and how can scientific understanding be achieved? What is the difference between scientific knowledge and scientific understanding? These questions are hotly debated in contemporary epistemology and philosophy of science. While philosophers have long regarded understanding as a merely subjective and psychological notion that is irrelevant from an epistemological perspective, nowadays many of them acknowledge that a philosophical account of science and its aims should include an analysis of the nature of understanding. This chapter reviews the current debate on scientific understanding. It presents the main philosophical accounts of scientific understanding and discusses topical issues such as the relation between understanding, truth and knowledge, the phenomenology of understanding, and the role of understanding in scientific progress.

Die Erkenntnistheorie wird meist als Theorie des Wissens charakterisiert. In jüngerer Zeit ist der alleinige Fokus auf Wissen kritisiert und sind weitere epistemische Güter diskutiert worden. Verstehen und Weisheit sind von besonderer Bedeutung. Erstens ist Verstehen ein hohes und Weisheit vielleicht das höchste epistemische Gut; beide scheinen epistemisch wertvoller zu sein als Wissen (Riggs 2003). Zudem ist unklar, ob der epistemische Wert von Wissen den Wert seiner Bestandteile (z.B. wahre, gerechtfertigte Meinung) übersteigt. Es ist behauptet worden, dass sich für Verstehen kein solches Wertproblem stellt und die Erkenntnistheorie deshalb Verstehen ins Zentrum rücken sollte (Kvanvig 2003; Pritchard 2010). Zweitens ist Verstehen ein Ziel von Wissenschaft (Elgin 2017) und von wissenschaftlicher Erklärung (Friedman 1974). Theorien der Erklärung und der kognitiven Leistungen der Wissenschaft verlangen deshalb nach einer Theorie des Verstehens (de Regt u.a. 2009). Drittens mag ein geeigneter Begriff des Verstehens es erlauben, tiefsitzende Intuitionen zur epistemischen Rechtfertigung anzuerkennen, die für Wissen zurückgewiesen wurden, z.B. internalistische und kohärentistische Intuitionen sowie solche zur Rolle von intellektuellen Charaktertugenden (Riggs 2003; Zagzebski 1996). Diese Idee ist kürzlich kritisiert (z.B. Greco 2014) und dafür vorgeschlagen worden, dass solche Intuitionen Merkmale von Weisheit betreffen (Grimm u.a. 2017, Kap. 10). Dieser Artikel behandelt die Natur von Verstehen und von Weisheit, indem er Verstehen mit Wissen und beide mit Weisheit vergleicht (für die Wertfrage s. Kap. „Der Wert des Wissens“).

Based on a framework that distinguishes several types, roles and functions of values in science, we discuss legitimate applications of values in the validation of computer simulations. We argue that, first, epistemic values, such as empirical accuracy and coherence with background knowledge, have the role to assess the credibility of simulation results, whereas, second, cognitive values, such as comprehensiveness of a conceptual model or easy handling of a numerical model, have the role to assess the usefulness of a model for investigating a hypothesis. In both roles, values perform what we call first-order functions. In addition, cogni¬tive values may also serve an auxiliary function by facilitating the assessment of credibility. As for a third type of values, i.e. social values, their legitimate role consists in specifying and weighing epistemic and cognitive values with respect to practical uses of a simulation, which is considered a second-order function. Rational intersubjective agreement on how to specify and weigh the different values is supposed to ensure objectivity in simulation validation.

Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research.

The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: An epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to the degree that the agent grasps the theory (i.e., is able to make use of it), the theory answers to the facts and the agent’s commitment to the theory is justified. The threshold for outright attributions of understanding is determined contextually. The explication has descriptive as well as normative facets and allows for the possibility of understanding by means of non-explanatory (e.g., purely classificatory) theories.

Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of ‘model fit’; that is, how well model results fit observation‐based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long‐term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long‐term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non‐deductive argument for the adequacy of a climate model or a family of models for long‐term climate projections.

Die Theorie der Notation wurde von Nelson Goodman ([Goodman 1968]: Kap. 4) im Zusam­menhang mit der Frage nach den Iden­titäts­krite­rien für Kunstwer­ke ent­wickelt. Eine Nota­tion ist ein Zeichen­system, das ein syntak­tisches oder seman­tisches Krite­rium dafür ermög­licht, welche Gegen­stände oder Ereig­nisse Einzel­fälle eines bestimm­ten Werks sind. Ein solches Krite­rium ist dann notwen­dig, wenn Werke mehre­re Einzel­fälle zulas­sen, deren Iden­tität nicht durch ihre Entste­hungsge­schichte bestimmt ist. Da dies in para­digma­tischer Weise in der Musik der Fall ist, führe ich den Begriff der Nota­tion für die Musik ein, ehe ich mich kurz der Lite­ratur und ausführ­licher den bilden­den Künsten zuwen­de. Die resul­tieren­den Iden­titäts­krite­rien sollen auch für Musik, Texte und Bilder ohne Kunstwerk­status gelten, auch wenn wir hier in der Regel nicht von Werken sprechen.

In der Architekturkritik finden sich oft ethische Bewertungen. Haben diese einen Einfluss auf den architektonischen Wert von Bauwerken? Und in welcher Beziehung steht der ethische Wert architektonischer Werke zu ihrem ästhetischen Wert? Ich verteidige die folgenden Antworten, die einen moderaten Moralismus mit Bezug auf Architektur definieren: Ein architektonisches Werk ist in manchen Fällen (1) architektonisch kritisierbar (oder lobenswert), insofern es ethische Mängel (oder Vorzüge) hat, (2) ästhetisch kritisierbar (oder lobenswert), insofern es ethische Mängel (oder Vorzüge) hat, und (3) ethisch kritisierbar (oder lobenswert), insofern es ästhetische Mängel (oder Vorzüge) hat.

Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modelling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of “model fit”; i.e., how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model’s adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model’s adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model’s adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections.

Der Tagungsband versammelt Beiträge des 2. Forums Architekturwissenschaft zum Thema Architektur im Gebrauch, das vom 25. bis 27. November 2015 im Schader-Forum in Darmstadt stattfand. Die Beiträge nähern sich dem Thema grundlegend in zwei Perspektiven. Zum einen interessiert die lebensweltliche Verankerung von Architektur: die Gebrauchserfahrungen und die vielfältigen Weisen, in denen das Gebaute im Alltag jedes Menschen in Erscheinung tritt. Zum anderen werden die Vorstellungen vom Gebrauch in Prozessen des Planens und Bauens untersucht. Dabei treten unweigerlich auch Spannungsverhältnisse auf - zwischen Planerinnen und Nutzern, aber auch zwischen unterschiedlichen Gebrauchsweisen. Sowohl in theoretischen Auseinandersetzungen zu einem Begriff von Gebrauch in der Architektur als auch in empirischen Studien zu einzelnen Bauten und Bautypen, zeitgeschichtlichen Gebrauchsphänomenen und Situationen des Alltags wird dem auf den Grund gegangen.

Summary. Does architecture allow for quotations in a precise and non-metaphorical sense? To answer this question, I formulate with Nelson Goodman for the clearest case, i.e. linguistic quotations, three indi-vidually necessary and collectively sufficient conditions and ask whether they can be satisfied by build-ings or their parts. On the one hand, I argue that in most cases which architectural theorists and critics describe as architectural quotations, what in fact is present is some kind of allusion. On the other hand, I suggest to weaken the conditions for architectural quotations in order to be able to describe cases as quo-tations which share more similarities with linguistic quotations than with linguistic allusions.

In non-philosophical discourse, “identity” is often used when the specific character of artefacts is described or evaluated. We argue that this usage of “identity” can be explicated as referring to the symbol properties of artefacts as they are conceptualized in the symbol theory of Goodman and Elgin. This explication is backed by an analysis of various uses of “identity”. The explicandum clearly differs from the concepts of numerical identity, qualitative identity and essence, but it has a range of similarities with the notion of self-concept used in psychology and practical philosophy. The proposed explication is used to analyse claims about identity-pluralism and identity-conflicts. Firstly, the explication allows us to distinguish various ways how the same artefact can have a plurality of identities. Secondly, more or less sharp conflicts within an identity or between identities of an artefact are distinguished. Thirdly, many phenomena called “identity-conflicts” are only apparently identity-conflicts and can be analysed as involving some other form of tension

Buildings are frequently described as ambiguous and, indeed, they often involve the ambivalence associated with ambiguous symbols. In this paper, I develop a theory of architectural ambiguity within the framework of a Goodmanian symbol theory. Based upon Israel Scheffler’s study of verbal and pictorial ambiguity, I present a theory of denotational ambiguity of buildings which distinguishes four types of ambiguity: elementary ambiguity, interpretation-ambiguity, multiple meaning and metaphor, which proves to be a special case of multiple meaning. Denotationally ambiguous buildings are exceptions, because buildings usually exemplify rather than denote. I therefore add a theory of exemplificational ambiguity. The crucial distinction between mere multiple exemplification and genuine exemplificational ambiguity leads to two versions of each of the first three types of ambiguity. The resulting extension of Goodman’s symbol theory is of interest beyond architecture.

The profession of the master builder has become differentiated in the course of the technological developments stemming from industrialization, separating into the professional disciplines of the architect (in the modern sense) and the structural engineer. Ever since, the question has been about the relationship between architect and structural engineer and the nature of their collaboration. Differing responses have been propagated in architectural theory and exemplified by building practice. In this essay, I distinguish between the models of the monologue by an architect or engineer, the soliloquy of the engineer-architect, and the dialogue between architect and engineer as equal partners. For the third model, upon which we will focus our attention, I characterize more precisely the method of collaboration and discuss two construction methods—shear wall-slab systems (also known as deep-beam/slab structures) and truss structures—that call for the method presented. Beginning with a clarification of the concept of tectonics, I conclude by examining whether, with regard to the discussed structures, one can speak of a “new tectonic culture.”.

Gebaute Zeichen zeigt am Beispiel der Architektur, wie auf der Grundlage der Arbeiten von Nelson Goodman eine leistungsfähige Symboltheorie entwickelt werden kann. Neben der bisher umfassendsten Rekonstruktion enthält das Buch wichtige Modifikationen und wesentliche Erweiterungen von Goodmans Theorie. Die resultierende Symboltheorie der Architektur bietet ein weit reicheres Instrumentarium zur Interpretation von Bauwerken als bestehende Architektursemiotiken. Zudem entgeht sie einer Reihe von Einwänden, welche gegen diese vorgebracht wurden. Vorbereitend wird gezeigt, inwiefern Bauwerke und ihre Teile als Symbole funktionieren können. Der Hauptteil behandelt die Symbolisierungsweisen von Bauwerken. Abschließend werden die Identitäts- und Persistenzkriterien für Bauwerke diskutiert.

Aesthetic cognitivism is best thought of as a conjunction of an epistemic and an aesthetic claim. The epistemic thesis is that artworks have cognitive functions, the aesthetic thesis that cognitive functions of artworks partly determine their artistic value. In this article, my first aim is to defend the epistemic thesis of aesthetic cognitivism. Since it seems undeniable that artworks have cognitive functions, yet less clear whether they have them as artworks, I will focus on cognitive functions that plausibly belong to the proper functions of artworks. My second aim is to sketch a suitable epistemological framework for aesthetic cognitivism. I will pursue my first aim by realizing my second aim and hence argue for the thesis that artworks have cognitive functions by sketching a suitable epistemological framework. In Section 1, I present reasons for conceiving of epistemology as a theory of understanding rather than a theory of knowledge. Art enters the stage only in Section 2, where I show that an epistemology of understanding can and should accommodate the cognitive functions aesthetic cognitivists claimed for artworks, hence providing a suitable epistemological framework. To argue in this way in favour of the claim that artworks have cognitive functions does not beg the question since my argument for an epistemology of understanding does not depend on my aim to defend the epistemic thesis of aesthetic cognitivism. In Section 3, I will answer some of the most pressing non-cognitivist objections within the proposed framework.