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.

What does it mean to understand something? What types of understanding can be distinguished? Is understanding always provided by explanations? And how is it related to knowledge? Such questions have attracted considerable interest in epistemology recently. These discussions, however, have not yet engaged insights about explanations and theories developed in philosophy of science. Conversely, philosophers of science have debated the nature of explanations and theories, while dismissing understanding as a psychological by-product. In this book, epistemologists and philosophers of science together address basic questions about the nature of understanding, providing a new overview of the field. False theories, cognitive bias, transparency, coherency, and other important issues are discussed. Its 15 original chapters are essential reading for researchers and graduate students interested in the current debates about understanding.

Im Zusammenhang mit der Museumsarchitektur wie auch sonst in der Architekturtheorie und der Philosophie der Architektur werden die Ausdrücke »Funktion« und »funktional« sehr häufig verwendet, aber kaum je expliziert. In diesem Aufsatz schlage ich eine Explikation des architektonischen Funktionsbegriffs vor, um am Beispiel der Museumsarchitektur ein Framework für eine funktionale Analyse von Bauwerken zu entwickeln. Im Anschluss an meinen Explikationsvorschlag, der es erlaubt, verschiedene Funktionstypen zu unterscheiden, behandle ich die praktischen und die ästhetischen Funktionen von Museumsbauten. Abschließend weise ich auf weitere Funktionen hin und verorte das vorgeschlagene Framework in einer allgemeinen Architekturtheorie.

In den letzten Jahren ist verschiedentlich vorgeschlagen worden, die philosophischen Ansätze zur Architektur in einer eigenständigen Disziplin der Architekturphilosophie zu-sammenzufassen. Im vorliegenden Beitrag gebe ich eine allgemeine Charakterisierung der Architekturphilosophie, indem ich sie einerseits von der Architekturtheorie abgrenze und andererseits in der Philosophie verorte. Im ersten Teil diskutiere ich verschiedene Ideen, die Abgrenzung von der Architekturtheorie über autorbezogene oder über inhaltliche Kriterien zu leisten, und mache einen Vorschlag, der mit stärker formalen Kriterien operiert. Im zwei¬ten Teil wende ich mich der Architekturästhetik und der Architekturethik als den beiden bisher am besten etablierten Zweigen der Architekturphilosophie zu. Ich charakterisiere diese, indem ich eine Liste von Fragen vorschlage, mit denen sie sich jeweils beschäftigen, und diskutiere, ob die Architektur eine eigene Bereichsästhetik und Bereichsethik erfordert. Abschließend zeige ich, weshalb es sinnvoll ist, für die beiden Zweige eine eigenständige phi¬losophische Disziplin der Architekturphilosophie zu postulieren.

Abstract: The practice of architectural criticism is supercharged with ethical evaluations. But do they have any bearing on the architectural value of a building? And how are the ethical value of an architectural work and its aesthetic value related? I defend the following answers, which define a version of moderate moralism with respect to architecture: An architectural work will in some cases be (1) architecturally flawed (or meritorious) due to the fact that it has ethical flaws (or merits), (2) aesthetically flawed (or meritorious) due to the fact that it has ethical flaws (or merits), and (3) ethically flawed (or meritorious) due to the fact that it has aesthetic flaws (or merits).

Architekturkritiker und -historiker verwenden eine Vielzahl von Ausdrücken, um anzugeben, was Bauwerke bedeuten. Es ist beispielsweise die Rede davon, dass sie etwas ausdrücken, repräsentieren, zitieren, manifestieren, darstellen oder aussagen; man kann von Gebäuden lesen, die mehrdeutig sind, als Metaphern fungieren oder auf etwas anspielen. In diesem Aufsatz frage ich, wie Bauwerke bedeuten können, um die Grundzüge einer Theorie der Bedeutungsweisen von Bauwerken und ihren Teilen vorzustellen, die als Rahmen für Einzelanalysen und historische Untersuchungen verwendet werden kann. Anstatt die meist unklaren und oft einander widersprechenden Verwendungsweisen der erwähnten Ausdrücke in der Architekturtheorie vergleichend zu analysieren, präsentiere ich zumindest für einige von ihnen Vorschläge, sie für theoretische Zwecke durch technische Termini zu ersetzen, die in den relevanten Anwendungsfällen anstelle der fraglichen Ausdrücke verwendet werden können. Die Vorschläge beruhen auf der allgemeinen Symboltheorie, die Nelson Goodman in Sprachen der Kunst entwickelt hat, und interpretieren die Bedeutungsweisen von Bauwerken damit als Symbolisierungsweisen.

Welche Rolle spielt ein Verständnis der Konstruktion eines Bauwerks für seine ästhetische Erfahrung und Wertschätzung? In diesem Beitrage argumentiere ich für die These, dass die ästhetische Erfahrung und Wertschätzung von Bauwerken wesentlich von einem Verständnis ihrer Konstruktion abhängt, und zwar selbst dann, wenn Bauwerke ihre Konstruktion weder direkt zeigen noch indirekt darauf anspielen. In einem ersten Schritt bette ich meine These in ein umfassenderes Bild ein, indem ich die ästhetische Erfahrung allgemein charakterisiere und die ästhetische Erfahrung von Architektur von anderen Instanzen ästhetischer Erfahrung unterscheide. Danach untersuche ich am Beispiel zeitgenössischer Holzarchitektur, wie konstruktive Eigenschaften die ästhetischen Eigenschaften mitbestimmen und damit ein Verständnis der Konstruktion die ästhetische Erfahrung von Bauwerken beeinflusst. Ich kritisiere den Vorschlag, dass konstruktive Eigenschaften die ästhetischen mitbestimmen, indem sie sich in der Form eines Bauwerks ausdrücken, und schlage mit Bezug auf Kendall Waltons Kategorienansatz vor, dass konstruktive Eigenschaften die ästhetischen mitbestimmen, indem sie festlegen, welche Aspekte der Form eines Bauwerks üblich, welche variabel und welche unüblich sind für seine Konstruktionsweise.

In diesem Aufsatz entwickle ich eine Definition von explanatorischem Verstehen, indem ich dieses mit Wissen vergleiche. Erstens zeige ich, inwiefern explanatorisches Verstehen das Erfassen einer anspruchsvolleren Erklärung und eine anspruchsvollere Rechtfertigung verlangt als explanatorisches Wissen. Zweitens argumentiere ich dafür, dass die Erklärung den Tatsachen gerecht werden muss, explanatorisches Verstehen aber im Gegensatz zu Wissen nicht immer faktiv ist. Drittens verteidige ich die Auffassung, dass explanatorisches Verstehen, anders als Wissen, mit epistemischem Glück kompatibel ist. Als Ergebnis schlage ich vor, dass S (in einem gewissen Ausmaß) versteht, warum p, gdw. S jede der folgenden Bedingungen (in einem gewissen Ausmaß) erfüllt: (a) S legt sich auf eine Erklärung E fest, die aus dem Explanandum p und einem Explanans besteht, das zeigt, wie p von q abhängt, (b) S erfasst E, (c) S ist fähig, E zu rechtfertigen, und (d) E wird den Tatsachen gerecht. Die Bedingungen (b) und (c) verlangen bestimmte Fähigkeiten: das Erfassen einer Erklärung die Fähigkeit, von ihr Gebrauch zu machen, und das Rechtfertigen einer Erklärung die Fähigkeit, zeigen zu können, dass sie die beste verfügbare und eine hinreichend gute Erklärung ist. Ich zeige, wie die Definition für objektuales Verstehen adaptiert werden kann, und argumentiere gegen eine Reduktion des explanatorischen Verstehens auf propositionales Verstehen.