2. and 3. November 2017
Tagungszentrum Evangelische Akademie. Bad Boll
Simulations are the result of a scientific activity and an artistic practice (in the sense of a technē). The tension between scientific models and technical ingenuity, between the claims about exactitude, validity, and certainty on the one hand, and the necessities of intelligently dealing with technical conditions, epistemic uncertainty, and forms of visual representation on the other, typically characterize the processes of simulation. These considerations outline the main themes of the workshop series The Science and Art of Simulation (SAS). Contributions will be published in a series of books by Springer.
SAS’16 addresses “Normativity in Computer Simulations”. There are several reasons for this:
1. The role of values, norms, and standards in science have become a topic within the philosophy of science (norms has long been a key issue for the philosophy of technology). This corrects substantially our predominant picture of science.
2. Beyond its topic, the issue is of most relevance to the organization of research. Computer simulations are a highly popular subject for the philosophy of science while other disciplines (sociology, political science, legal studies) are tending to be marginalized. The question of normativity might open the discussion to other disciplines.
3. By their very nature –the selectivity of models, the handling of their uncertainty, the shaping of world by computer simulations (beyond the representation of reality) as well as the striking competition for computation– they imply values and norms.
Track 1: Values and/in Models
Traditional accounts take models as pristine representations of a target system, that is, devoid of any kind of values. With the arrival of the practice turn, however, this view was heavily contended and largely criticized as values play a specific role. What is the significance of values for modeling? It is a commonplace that models are selective. Less commonly is to ask what guides the selection process. One answer might be: Values in the form of epistemic virtues, scientific standards, social expectations, or political interests orientate the model-building process. This track addresses questions regarding their nature, role, and force, asking how facts and norms, genesis and recognition are interwoven.
Track 2: Evaluating Uncertainty
Uncertainties and ignorance have become surprisingly familiar terms in our theoretical language. For example, not only in climate studies but also in medical simulations we are confronted with that topic. As has been stated repeatedly, knowledge comes along with ignorance, and the refinement of scientific methods raises the level of uncertainty. Thereby uncertainty is not just a topic of our theoretical reference, but also and above all a practical challenge. For even if we could resolve the question what uncertainty is, it would still be unsettled how one could handle uncertainty reasonably. The track discusses strategies to deal with uncertainty (ignorance etc.). The question is how to assess uncertainty and to evaluate to what extent we are willing to accept this uncertainty.
Track 3: Representation and Formation: CS and Political Consulting
Computer simulations have largely been aids for political decisions. The climate models used by the IPCC are a good example of this. For the most part, they are discussed in terms of epistemology. With the increasing use of computer simulations in social and political fields, a transformation becomes evident: computer simulations are seen as an opportunity to explore the social world and to enable behavior in rehearsal. In this way, political measures should be evaluated virtually, and possible futures are not only represented but shaped and evaluated in rehearsal. The track discusses the role of politics in computer simulation in the context of governance and consultation.
Track 4: The Competition for computation
Performance is a fundamental value in computer simulations: from nations and institutions who compete for the fastest supercomputer, to practitioners whose everyday life is oriented that simulations run faster, this value penetrates all levels of HPC. The benchmark determines the field and as every benchmark, it combines description and evaluation. This track examines the role, meaning and possible historical transformation of the measurement of “performance”.
Tuesday, 4. October 2016
10:45 Hannes Haberl (U Bonn/Medicine)
11:15 Response: A. Kaminski (HLRS/Philosophy), A. Wackerbarth (HLRS/Sociology)
11:30 Discussion12:15 LunchValues in Modeling II
13:45 Getrude Hirsch Hadorn (ETH Zürich/Department of Environmental Systems Science)
14:15 Response: Claus Pias (MECS Lüneberg/Media Science)
14:30 Discussion15:15 Coffee BreakAssessment of Uncertainty I
15:30 Rainer Hegselmann (U Bayreuth/Philosophy)
16:00 Response: Hermann Held (U Hamburg/ Geosciences and Economics)
16:15 Discussion17:00 Coffee Break
Assessment of Uncertainty II
17:15 Gabriele Gramelsberger (MECS Lüneburg/Philosophy)
17:45 Response: Christoph Hubig (TU Darmstadt/Philosophy)
Wednesday, 5. October 2016
Representation or Formation of Reality? I
09:00 S. Ammon (TU Berlin/Philosophy), H. Meyer (TU Berlin/Engineering)
09:30 Response: Bruno Gransche (ISI Karlsruhe/Philosophy)
10:30 Coffee Break
Representation or Formation of Reality? II
10:45 Luis Kornblueh (DKRZ Hamburg, Computer Science)
11:15 Response: Ricky Wichum (MECS Lüneburg/Sociology)
The competition of calculation I
13:45 Michael Resch (HLRS/Mathematics)
14:15 Response: Petra Gehring (TU Darmstadt/Philosophy)
15:15 Coffee Break
The competition of calculation II
15:30 H. Hasse (U Kaiserslautern/Thermodynamics), J. Lenhard (U Bielefeld/Philosophy)
16:00 Response: Martin Warnke (MECS Lüneburg/Computer Science)