Field of Research and Teaching
The philosophers at our chair engage with a plurality of questions and topics in philosophy of science. In research and teaching, we are concerned with topics in epistemology, methodology, semantics and metaphysics of science. More specifically, we are concerned with the following questions:
- The concept of probability and its applications: How does statistical and probabilistic reasoning work in scientific contexts? What exactly are the probabilities to which scientists refer in such reasoning processes? Are probabilities objective facts in the world (e.g. frequencies) or a subjective expression of our uncertainty? What types of probabilities are there (e.g. precise and imprecise probabilities)? Which aspects of learning through experience can we understand in a better way by using the concept of probability? Is it possible to reconstruct deductively valid arguments and the semantics of conditionals in terms of probabilities? What can we learn from psychological experiments for probabilistic reconstruction of arguments and conditionals? Which principles guide inductive reasoning?
- Confirmation and Evidence: How should we interpret the statement that a scientific theory is well (or badly) confirmed? Are there both empirical and non-empirical types of confirmation? Is empirical confirmation via so-called analogue simulations in a lab possible? How exactly should we understand the concept of reliability? What defines good evidence? How are good evidence and objectivity related? How should we evaluate the quality of an argument if the premises are not true, but merely probable?
- Theories, Models, Analogies: Scientists typically postulate theories and models – but what exactly are scientific theories and models? How can we best describe their structure, semantics and ontology? What roles do symmetries play in theories? What does it mean to say that two theories are equivalent? Is it convincing to understand the relationships between theories in terms of reduction? What kind of relationship holds between different theories and models? Should we take realists or anti-realists with respect to theories and models? What can we learn from so called toy models (i.e. strongly idealized and simplified models)? Which problems arise from mathematical analogies if, for instance, aspects of statistical mechanics are used to model financial markets?
- Computer Simulations in Science and Philosophy: What type of knowledge do scientists gain through simulations (e.g. through simulations of the climate)? Is this knowledge comparable to the results of experiments? Do computer simulations and so-called analogue simulations generate different kinds of knowledge? Are computer simulations a suitable tool for answering philosophical questions (e.g. in social epistemology)?
- Causation and Explanation: How should we interpret the concepts of causation and explanation in scientific contexts? On which methods can we rely for discovering causes? What is the relationship between causes and probabilities? How can one represent non-causal relationships (e.g. constitutive and supervenience relations) in causal models? How are explanations and causality connected? How can we precisely explicate the varying strength of explanations? Do non-causal explanations exist – and if so, which philosophical implications do they have? What type of ontological conclusions can one draw from successful explanations (indispensability arguments)?
- Decision Theory and the Future of Artificial Intelligence: Which formal conditions must good decisions fulfill? Do groups of agents make better decisions than individual agents? Which kinds of distortions (biases) affect group decisions and how can they be corrected? How can decisions be automated with the help of artificial agents? How important is it that automated decisions (e.g. in self-driving cars) are transparent?
We pursue these (and further) philosophical questions with different and complementary methods and goals such as the following: Several of our projects aim to understand similarities and differences between several scientific disciplines (Is it, for instance, reasonable to speak of theories in both physics and economics? Are the concepts of causation and explanation used in all scientific disciplines? Is there a common core which characterizes the scientific method?). This sort of aim characterizes the field of the general philosophy of science.
The philosophical focus can, however, also be on a specific discipline, a particular theory, a specific model or a method (e.g. the concepts of space and time in the theory of quantum gravity, reduction in statistical mechanics, the specific philosophical problems in the physics of black holes, the problems of confirmation of string theory, the unique questions concerning the notion of probability in the theory of evolution, the interpretation of idealizations in the context of the Schelling model, etc.). This sort of methodological orientation is typical for the special philosophy of science. Our focus in research and teaching in this area is primarily on philosophy of physics (more specifically the so-called foundations of physics), philosophy of the social sciences and economics, cognitive psychology (especially psychology of reasoning and argumentation), neuroscience, statistics and computer science.
It is our methodological conviction that a fruitful interaction between general and special philosophy of science is necessary for obtaining a philosophy of science that is both closely tied to the sciences and, at the same time, philosophically rich in content.
We deliberately pursue a variety of complementary methodological approaches to philosophical problems and questions. These approaches include among others:
- Descriptively oriented research projects whose main goal consists in the exact description and the conceptual analysis of case studies of scientific theorizing and modeling as well as scientific practice.
- Formally oriented approaches that for instance use probability theory to answer philosophical questions and the goal of which is frequently descriptive as well as normative.
- Projects building on the import of empirical methods from the sciences, such as psychological tests and computer simulations for the resolution of philosophical problems.
- The sometimes critical engagement with the knowledge produced in the sciences and their implications for philosophical questions.