Research Interests:
Scientists are called upon by policymakers to provide recommendations on how to address climate change. It has been argued that as policy advisors, scientists can legitimately make instrumental value judgements (recommendations based on... more
Scientists are called upon by policymakers to provide recommendations on how to address climate change. It has been argued that as policy advisors, scientists can legitimately make instrumental value judgements (recommendations based on defined policy goals), but not categorical value judgements (challenge and/or redefine established policy goals), and that to do otherwise is to overstep in ways that may threaten their perceived trustworthiness. However, whether these types of value judgements affect public trust in scientists remains largely untested. We conducted two studies (N1 = 367, N2 = 819) to investigate public perceptions of trustworthiness of a climate scientist expressing either an instrumental or a categorical value judgement. We found no difference in perceived trustworthiness between the two conditions. However, trustworthiness perceptions in both studies depended on individuals’ support for the policy recommended by the scientist. Our findings suggest that climate scientists should not fear for their overall perceived trustworthiness when making categorical value judgments if their opinions are supported by the majority of the public.
Research Interests:
Data-driven modelling with machine learning (ML) is already being used for predictions in environmental science. However, it is less clear to what extent data-driven models that successfully predict a phenomenon are representationally... more
Data-driven modelling with machine learning (ML) is already being used for predictions in environmental science. However, it is less clear to what extent data-driven models that successfully predict a phenomenon are
representationally accurate and thus increase our understanding of the phenomenon. Besides empirical accuracy, we propose three criteria to indirectly assess the relationships learned by the ML algorithms and how they relate to a phenomenon under investigation: first, consistency of the outcomes with background knowledge; second, the adequacy of the measurements, datasets and methods used to construct a data-driven model; third, the robustness of interpretable machine learning analyses across different ML algorithms. We apply the three criteria with a case study modelling of the effect of different urban green infrastructure types on temperature and show that our approach improves the assessment of representational accuracy and reduces representational uncertainty, which can improve the understanding of modelled phenomena.
representationally accurate and thus increase our understanding of the phenomenon. Besides empirical accuracy, we propose three criteria to indirectly assess the relationships learned by the ML algorithms and how they relate to a phenomenon under investigation: first, consistency of the outcomes with background knowledge; second, the adequacy of the measurements, datasets and methods used to construct a data-driven model; third, the robustness of interpretable machine learning analyses across different ML algorithms. We apply the three criteria with a case study modelling of the effect of different urban green infrastructure types on temperature and show that our approach improves the assessment of representational accuracy and reduces representational uncertainty, which can improve the understanding of modelled phenomena.
Research Interests:
Research Interests:
Increasing volumes of data allow environmental scientists to use machine learning to construct data-driven models of phenomena. These models can provide decision-relevant predictions, but confident decision-making requires that the... more
Increasing volumes of data allow environmental scientists to use machine learning to construct data-driven models of phenomena. These models can provide decision-relevant predictions, but confident decision-making requires that the involved uncertainties are understood. We argue that existing frameworks for characterizing uncertainties are not appropriate for data-driven models because of their focus on distinct locations of uncertainty. We propose a framework for uncertainty assessment that uses argument analysis to assess the justification of the assumption that the model is fit for the predictive purpose at hand. Its flexibility makes the framework applicable to data-driven models. The framework is illustrated using a case study from environmental science. We show that data-driven models can be subject to substantial second-order uncertainty, i.e., uncertainty in the assessment of the predictive uncertainty, because they are often applied to ill-understood problems. We close by discussing the implications of the predictive uncertainties of data-driven models for decision-making.
Research Interests:
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:... more
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.
Research Interests:
Research Interests:
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... more
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. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change K E Y W O R D S climate datasets, dataset ensembles, framework, model evaluation, uncertainty
Research Interests:
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... more
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 (i.e. an agreement of commitments, theory and background theories). 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.
Research Interests:
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... more
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.
Research Interests:
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... more
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.
Research Interests:
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... more
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.
Research Interests:
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... more
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.
Research Interests:
Der traditionellen Auffassung nach basiert die Schönheit und allgemeiner der ästhetische Charakter eines Werks auf seinen Formeigenschaften. Ein Musikstück ist heiter aufgrund seiner Klangstruktur, ein Gemälde harmonisch aufgrund seiner... more
Der traditionellen Auffassung nach basiert die Schönheit und allgemeiner der ästhetische Charakter eines Werks auf seinen Formeigenschaften. Ein Musikstück ist heiter aufgrund seiner Klangstruktur, ein Gemälde harmonisch aufgrund seiner Farbverteilung. Aber gehören bei Bauwerken nicht auch konstruktive Merkmale zu den Eigenschaften, die den ästhetischen Charakter bestimmen? Gibt es so etwas wie konstruktive Schönheit?
Research Interests:
Research Interests:
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... more
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.
Research Interests:
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... more
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.
Research Interests:
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?... more
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.
Research Interests:
Research Interests:
In science and philosophy, a relatively demanding notion of understanding is of central interest: an epistemic subject understands a subject matter by means of a theory. This notion can be explicated in a way which resembles JTB analyses... more
In science and philosophy, a relatively demanding notion of understanding is of central interest: an epistemic subject understands a subject matter by means of a theory. This notion can be explicated in a way which resembles JTB analyses of knowledge. The explication requires that the theory answers to the facts, that the subject grasps the theory, is committed to the theory and justified in the theory. In this paper, we focus on the justification condition and argue that it can be analysed with reference to the idea of a reflective equilibrium.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Ethik boomt: Sie soll uns in Zukunft vor Finanzkrisen bewahren, das mangelnde Wertebewusstsein unserer Gesellschaft ausbügeln und für mehr Gerechtigkeit sorgen. Der Boom wirft aber auch kritische Fragen auf: • Wie lässt sich über Ethik... more
Ethik boomt: Sie soll uns in Zukunft vor Finanzkrisen bewahren, das mangelnde Wertebewusstsein unserer Gesellschaft ausbügeln und für mehr Gerechtigkeit sorgen. Der Boom wirft aber auch kritische Fragen auf:
• Wie lässt sich über Ethik sprechen, ohne Moral zu predigen?
• Wie entscheidet man ethische Konflikte?
• Gibt es Wissen und Wahrheit in der Ethik?
• Wie stehen Recht und Ethik zueinander?
Die Autoren zeigen mit ihrem Schema ethischer Entscheidungsfindung auf anschauliche Weise, wie moralische Fragen diskutiert und ethische Konflikt gelöst werden können. Mit der Diskussion von Fallbeispielen und praxisnahen Übungen richtet sich dieses Handbuch an alle, die mit ethischen Fragen konfrontiert sind und sich mit diesen auseinandersetzen wollen.
• Wie lässt sich über Ethik sprechen, ohne Moral zu predigen?
• Wie entscheidet man ethische Konflikte?
• Gibt es Wissen und Wahrheit in der Ethik?
• Wie stehen Recht und Ethik zueinander?
Die Autoren zeigen mit ihrem Schema ethischer Entscheidungsfindung auf anschauliche Weise, wie moralische Fragen diskutiert und ethische Konflikt gelöst werden können. Mit der Diskussion von Fallbeispielen und praxisnahen Übungen richtet sich dieses Handbuch an alle, die mit ethischen Fragen konfrontiert sind und sich mit diesen auseinandersetzen wollen.
Research Interests:
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... more
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 Spannungs-verhältnisse 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.
Research Interests:
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... more
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.
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.