Nino Antulov-Fantulin: Catalogue data in Autumn Semester 2016

Name Dr. Nino Antulov-Fantulin
FieldComputational Social Science
Address
Computational Social Science
ETH Zürich, STD F 4
Stampfenbachstrasse 48
8092 Zürich
SWITZERLAND
Telephone+41 44 632 61 57
E-mailnino.antulov@gess.ethz.ch
DepartmentHumanities, Social and Political Sciences
RelationshipPrivatdozent

NumberTitleECTSHoursLecturers
851-0585-15LComplexity and Global Systems Science Information
Prerequisites: solid mathematical skills.
Particularly suitable for students of D-ITET, D-MAVT
3 credits2VD. Helbing, N. Antulov-Fantulin
AbstractThis course discusses complex techno-socio-economic systems, their counter-intuitive behaviors, and how their theoretical understanding empowers us to solve some long-standing problems that are curently bothering the world.
Learning objectiveParticipants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop models for open problems, to analyze them, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to think scientifically about complex dynamical systems.
ContentThis course starts with a discussion of the typical and often counter-intuitive features of complex dynamical systems such as self-organization, emergence, (sudden) phase transitions at "tipping points", multi-stability, systemic instability, deterministic chaos, and turbulence. It then discusses phenomena in networked systems such as feedback, side and cascade effects, and the problem of radical uncertainty. The course progresses by demonstrating the relevance of these properties for understanding societal and, at times, global-scale problems such as traffic jams, crowd disasters, breakdowns of cooperation, crime, conflict, social unrests, political revolutions, bubbles and crashes in financial markets, epidemic spreading, and/or "tragedies of the commons" such as environmental exploitation, overfishing, or climate change. Based on this understanding, the course points to possible ways of mitigating techno-socio-economic-environmental problems, and what data science may contribute to their solution.
Prerequisites / NoticeMathematical skills can be helpful