Program and Speakers

The conference brings together an array of researchers from across Europe, representing a diverse range of academic disciplines. 

The speakers' varied backgrounds span the natural sciences, social sciences, and humanities, offering a truly interdisciplinary perspective on modeling and change. 

Section 1: 

Prof. Dr. Andreas Diekmann
ETH Zurich em./University of Leipzig - Seniorprofessor at the Insitute for Sociology
Preliminary lecture title: "Model building in sociology"

Prof. Dr. Paul Hoyningen-Huene
Leibniz University Hannover - Professor of Philosophy of Science
Preliminary lecture title: "How do robust abstract economic models explain?"

Prof. Dr. Katharina Al-Shamery
Carl von Ossietzky University Oldenbourg - Professor of Physical Chemistry 
Preliminary lecture title: "Models to understand catalysis"

Prof. Dr. Wolfgang Kautek
University of Wien - Professor of Physical Chemistry 
Preliminary lecture title: "Perspectives on scientific modelling and simulation"

Prof. Dr. Armin Gölzhäuser (Faculty of Physics) and Prof. Dr. Martin (Faculty of History, Philosophy and Theology) are responsible for the thematic focus and for recruiting the speakers of this section. 

Section 2: 

Dr. Inês Fragata
University of Lissabon - Centre for ecology, evolution and environmental changes
Preliminary lecture title: "How can species interactions shape fitness landscapes"

Prof. Dr. Jonathan Jeschke
FU Berlin - Head of the Department of Evolutionary and Integrative Ecology at IGB Berlin
Preliminary lecture title: "Knowledge network models for biological invasions and beyond"

Prof. Dr. Wolfgang Knöbl
Hamburger Institut für Sozialforschung - Head of the Hamburg Institute for Social Research
Preliminary lecture title: „Attempts of Theorizing Social Change: Problems and Pitfalls“

Prof. Dr. André Krischer
University of Freiburg - Professor of Early Modern History
​Preliminary lecture title: "Explaining Change in the Early Modern Period with Modernization Theories"

Prof. Dr Franz-Josef Arlinghaus (Faculty of History, Philosophy and Theology), Prof. Dr Caroline Müller (Faculty of Biology/ Chemical ecology) and Prof. Dr Meike Wittmann (Faculty of Biology/ Theoretical biology), are responsible for the thematic focus and for recruiting the speakers of this section.

Section 3: 

Prof. Dr. Michael Piotrowski
University of Lausanne - Professor of Digital Humanities in the Department of Language and Information Sciences
Preliminary lecture title: "Computational historical models and historiographical uncertainty"

Prof. Dr. Mark Freeston 
Newcastle University - Doctorate in Clinical Psychology
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Prof. Dr. Jean-Marc Tallon
University of Paris - PSE Chaired Professor 
Preliminary lecture title: "Modeling decision-making under uncertainty"

Prof. Dr. Tina Comes
TU Delft - Director of the TPM Resilience Lab 
Preliminary lecture title: "Tempus fugit - On the Dynamics of Resilience Modeling & Policy"

Prof. Dr. Herbert Dawid (Faculty of Business Administration and Economics) and Prof. Dr. Silke Schwandt (Faculty of History, Philosophy and Theology), are responsible for the thematic focus and for recruiting the speakers of this section.

Section 1

Prof. Dr. Andreas Diekmann

The development and application of mathematical models in sociology goes back well over half a century. The Journal of Mathematical Sociology has existed since 1971; there are also numerous textbooks on mathematical sociology. In addition, there are specialized textbooks in fields such as game theory, network analysis, models of social diffusion, computer simulations, stochastic models of social mobility (Markov chains), etc. Classic works date back to the 1950s, such as the work of Coleman, Katz, and Menzel on the diffusion of innovations. In contrast to economics, however, it must be added that in sociology only a minority is concerned with the explicit mathematical formalization of sociological theories. If statistical models are also included, the degree of formalization in sociological work is correspondingly higher, as multivariable methods are part of the tools of standardized empirical social research. More recently, with the development of artificial intelligence, machine learning and agent-based computer simulation, applications of formal methods have flourished under the label “computational social science”. In addition, there are already specializations in the teaching of Master's courses in sociology.

In my lecture, I will briefly discuss the history of mathematical modeling in sociology, the current status and new trends. The focus will be on examples that demonstrate the advantages of using formal models in sociology. In addition to increased precision of theoretical statements through mathematical models, the potential of deriving often new and surprising hypotheses that can be tested on empirical data should be emphasized. Another particular strength of formalization is that models can often be generalized to other areas of application. Examples of this will also be presented in the lecture.

Prof. Dr. Paul Hyningen-Huene

I shall try to answer the question how robust abstract economic models explain; my main illustrative example is the Sakoda-Schelling model of (racial) segregation. I shall presuppose that abstract economic models deliver for the real world how-possibly explanations at best. The crucial question is how model results can be transferred to real-world phenomena. I shall propose reframing this transfer problem in the following way. Robust model results inductively support a conjectured, non-obvious logical truth that can be immediately applied both to the model world and to the real world, thereby delivering a how-possibly explanation. I shall develop this thesis in eight steps gradually dismantling its counter-intuitive character. The result will be that one function of robust abstract models is to tease out non-obvious explanatory consequences of theories (evolutionary theory, e.g.) or mechanisms (Sakoda-Schelling dynamics, e.g.) that cannot be directly inferred from them.

Section 2

Prof. Dr. Jonathan Jeschke 

In the present era, an unprecedented amount of data and information is available to us in principle, yet these data and information are typically disconnected and trapped in silos. This is true among and even within scientific disciplines. In ecological research fields, for example, there is often a disconnect between researchers working empirically in the laboratory or the field versus those working on theory and models. And different ecological research fields work on different drivers of biodiversity and global change in a way that makes integration across fields challenging. To improve integration and knowledge transfer, we have developed and applied approaches that allow to map the major hypotheses of research fields and to zoom into each hypothesis, thereby connecting scientific theory with empirical data. I will outline these approaches with a focus on biological invasions as a case study but will highlight their applicability across research fields. Developing such knowledge network models allows to integrate knowledge and strengthens inter- and transdisciplinary bridges.

Prof. Dr. Wolfgang Knöbl 

For a variety of reasons the social sciences have been used to theorize large-scale social change by using robust processual terms such as secularization, rationalization, modernization and others. The promises of this particular kind of processual thinking, however, have been disappointed for a variety of reasons. Firstly, some of these terms have been very heavily critized insofar as they seem to foster a rather linear and even teleological understanding of social change. Secondly, most of the nouns out of which these processual terms have been built (e.g. the secular – secularization; the individual person – individualization etc.) were very much shaped by their (western) political and socio-cultural origins which has never been sufficiently taken into consideration by social scientists. Without a thorough contextualization and historcization of these terms the social sciences are in danger of giving rather odd accounts of what was and is going on in our world.

At that same time, the crisis of these processual terms seems to allow a new and fruitful cooperation between the social sciences and the humanities insofar as historians and literary scholars on the one side are the very experts in the business of contextualization and historicization but are on the other side themselves in need of structuring their data or in giving model-like accounts. The paper will try to give some hints how one mighgt be abler to overcome this dilematic situation. 

Section 3

Prof. Dr. Michael Piotrowski

 The biggest impact of computers on scientific research has probably not been for calculations, but as universal modeling machines: computational models and simulations have considerably changed the natural and engineering sciences.
 
  What about the humanities, and history in particular?  As Gordon Leff remarked, “Historians as a profession are not given to constructing or employing models in any formal or explicit sense.”  In fact, historians do construct models of their research objects, but these are rarely made explicit, let alone formalized.  There are good reasons for this: given the little reliable information we have about the past, there is a high degree of uncertainty, and historians need to fill a lot of gaps to be able to produce coherent and defensible narratives.  Consequently, the use of computational models and simulations in historical research (in the widest) sense is relatively limited and generally exclude the most important aspect, the causal relations inherent in a historical narrative.
 
  Here, we are confronted with another level of uncertainty, historiographical uncertainty, which pertains to the interpretations and choices made by historians, i.e., uncertainty that relates to the narratives rather than the facts of the past. Is there any chance that we may deal with this type of uncertainty in a way that would make computational models as useful for historical research as for research in the natural sciences?

Prof. Dr. Mark Freeston 

As a clinical-academic I started working on responses to uncertainty in 1992 with the aim of understanding why some people worry more than others.  Originally the focus was quite narrow on anxiety disorders, but has broadened over the years. Many individuals find uncertainty aversive and this leads people to seek to minimize uncertainty  in often rigid and inflexible ways, resulting in decisions that limit the pursuit of valued goals.  We were already considering real-life situations by 2019.  And then the pandemic happened and many people suddenly “realized” the world was uncertain.  To help develop interventions, we accelerated our work both theoretically and empirically, and trained a lot of clinicians. We could also observe how what we knew from individuals was playing out at organizational, regional, national and international levels. Information alone cannot resolve uncertainty, it just shifts the point at which we are uncertain.  Learning to “make friends with uncertainty” allows individuals to understand uncertainty for what it is, accept and even embrace it, rather than assuming a threat is just around the corner. Could organizations learn the same? 

Prof. Dr. Jean-Marc Tallon 

The presentation begins by discussing classical decision theory, focusing on expected utility and Bayesian updating as tools for rational choice under uncertainty. The talk then delves into ambiguity aversion and its implications, with illustrations from economics.

Prof. Dr. Tina Comes

In the face of climate change and geopolitical conflict, resilience has made it to the forefront of research and policy agendas. Resilience, as the way to respond to crises, adapt and transform, is inherently a dynamic concept. Yet, very often, models and tools seek to establish snapshots of the situation or aim to establish a steady-state or equilibrium, neglecting the different rhythms and timescapes of resilience.

In this talk, I argue for the need to recognize the different rhythms of resilience, including behavioural patterns, cycles of planning and policy-making, and rhythms of growth and decline. I will especially stress the need to consolidate short-term responsive actions and models with longer-term planning for sustainable futures. I will discuss the challenge for modelers to bridge different temporal (and geographical) scales, using examples from agent-based and land-use change modeling, and I will illustrate my arguments with findings from Resilient Urban Planning, Epidemic Response, and Humanitarian Disasters.