Cognitive values like simplicity, broad scope, and easy handling are properties of a scientific representation that result from the idealization which is involved in the construction of a representation. These properties may facilitate the application of epistemic values to credibility assessments, which provides a rationale for assigning an auxiliary function to cognitive values. In this paper, I defend a further rationale for cognitive values which consists in the assessment of the usefulness of a representation. Usefulness includes the relevance of a (...) representation regarding the investigation of a given problem and its practicability for the users. This rationale builds on the claim that any evaluation of scientific representations should pursue two aims: providing information about their credibility and providing information about their usefulness. Cognitive values relating to the usefulness of a representation and epistemic values relating to its credibility both perform a first-order function. Cognitive values are abstract, and several values with first-order functions may conflict in their application. Thus, in order for cognitive values to account for the sort of problem that is to be investigated by means of a representation, they need to be appropriately specified and weighed. Comprehensiveness, complexity, high resolution, and easy handling, for instance, may be required in a first-order function for model-based prediction of regional climate impacts but not for explaining how the global climate system works. Specifying and weighing cognitive and epistemic values relative to a given problem is a legitimate second-order function of social values. (shrink)

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. (shrink)

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. (shrink)

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. (shrink)

Weber's Ideal Type as a Method of Forming the Content of Theoretical Concepts in Social Sciences}. Max Weber introduced the ideal type as the specific method of concept formation in social sciences. But the ideal type is not established in social research. Instead, authors in philosophy of science until today try to reconstruct and interpret what Weber said about ideal types as well as what might be their importance in Weber's social theory. The thesis of the following paper is that (...) the difficulties in understanding Weber's ideal types are linked with Weber's intensional logic of concept formation. The thesis is defended in three steps. The first step deals with Weber's understanding of what is a scientific question in cultural sciences. Secondly Weber's critical arguments against positivism, hegelianism and historism are worked out. Thirdly, Weber's method of concept formation is reconstructed. (shrink)

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. (shrink)