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Videomitschnitte der Lunch Lecture Reihe "Models and Modelling across Sciences" 2019/20

Modelling is an essential part of many academic disciplines. Models “aim to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate by referencing it to existing and usually commonly accepted knowledge.” (Wikipedia on “Scientific modelling”). We are surrounded by models, partly have received most our school book wisdom in individual fields of science from these models. Consider, for example, the billiard ball model of a gas, the Bohr model of the atom, the double helix model of DNA, mouse models in biomedical research, or climate models in the environmental sciences. In this lecture series, we want to inquire into models as one of the principal instruments of modern science from the perspective of different academic disciplines.


What is a scientific model?

PD Dr. Tobias Henschen, Epistemology and Theory of Science, University of Freiburg

It is relatively easy for us to provide a (potentially long) list of scientific models. But can we say (or define) what a scientific model is? In my talk, I’m going to argue that a descriptive account of scientific models (e.g. one of distinguishing different types of scientific models) is not going to help us with that question. I will then discuss and ultimately dismiss two philosophical (or normative) accounts of scientific models: one that answers the above question by considering the relation between models and theories, and one that defines scientific models in terms of structures. I will finally put up for discussion whether we can make progress when paying attention to the functions that scientific models serve.


The role of models and theories for understanding nature

Prof. Dr. Frank Stienkemeier, Physics, University of Freiburg

In order to understand properties, functions and the behavior of matter, objects, devices or even living organisms, models play an important role in physics disciplines. Modelling can be done on very different levels and sometimes even coalesce with physics theories. Since so far there is no “Theory of Everything” which describes nature at all conditions and length scales, several theories (e.g. classical mechanics, quantum mechanics, relativity) have been established for selected conditions. An overview and examples will be presented how theories and models in physics provide a framework for understanding and describing our world.


Narratology and Modelling

Prof. Dr. Monika Fludernik, English Literature and Narratology, University of Freiburg


How many species are there on Earth? Different models to approximate global species diversity (and is this even possible?)

Dr. Michael Staab, Ecology and Biodiversity, University of Freiburg

About 2 million species are currently known to science, but the ‘true‘ species number on Earth is without doubt considerably higher, with the largest number of unknown species being insect. Various models have tried to estimate global biodiversity, however, modelling approaches and predictions differ widely. I will introduce several fundamental ways to estimate global species numbers and discuss conceptual strengths and weaknesses. The lecture will close with an outlook and an appraisal of whether it will be realistic to know how many species there are on Earth.


The desire for simplicity in the complexity of living systems

Dr. Milena Bertolotti, Immunobiology, University of Freiburg

Complexity is one of the key features of biological living systems and it is a matter of fact that some questions cannot be answered with the traditional most popular genetic model systems. Moreover, high throughput genomics and proteomics approaches are producing an incredibly huge amount of biomedical data. The development and study of new atypical model organisms and the generation of mathematical and computational models are now a necessity and are starting to be used to accelerate the process of knowledge discovery of biological systems.


Which Forecast to trust? Credibility and craftmanship in environmental modelling

Prof. Dr. Carsten Dormann, Biometry and Environmental System Analysis, University of Freiburg

Forecasts, here understood as quantitative predictions of the future state of a system, come in all shapes and sizes. The German "economic wise guys" forecast next year's economic growth, the evening news present the three-day weather forecast, climate scientists predict drought conditions in 2100, and ecologists forecast loss of diversity following land-use and climate change. The forecasts communicated to the public typically come in maps or simple line graphs or single numbers. Assumptions, data quality and forecast uncertainty are never communicated. Within each community, however, these are fundamental for the credibility of a modelling approach; the forecasts are often merely the spin on the actual science for a more widely disseminated publication. In some areas of environmental science, however, forecasts are all there is: a predicted time-trend, a map, emerging from a short analysis without thorough scientific evaluation of uncertainties, explicit statement of assumptions, discussion of potential errors or alternative scenarios. And, indeed, without any scientific merit. This kind of science is largely politically motivated, its scientific credibility is rather low. After giving some examples of the current range of environmental forecasts, I want to present a few lines along which to identify, as a person outside the actual field, the scientific quality and hence credibility of an environmental forecast.


Backing a model – how to establish predictions from limited observations in water sciences

Jun.-Prof. Dr. Andreas Hartmann, Hydrology, University of Freiburg


Models and Modelling in Linguistics: The role of functional neuroimaging

Dr. Alice Blumenthal-Dramé, English Linguistics, University of Freiburg

Many popular models of language purport to represent the workings of the mind in at least some indirect sense. Interestingly, many of the models which strive for such “cognitive realism” have put forward claims which are mutually incompatible. This talk will explore the extent to which functional imaging research can help us adjudicate between competing models of language in the mind. Against this background, it will discuss what the notion of “cognitive realism” actually means and will also warn against exaggerated expectations and blind confidence in (what is often considered to be) “hard facts”.