Nino Antulov-Fantulin: Catalogue data in Spring Semester 2019 |
| Name | Dr. Nino Antulov-Fantulin |
| Field | Computational Social Science |
| Address | Computational Social Science ETH Zürich, STD F 4 Stampfenbachstrasse 48 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 61 57 |
| nino.antulov@gess.ethz.ch | |
| Department | Humanities, Social and Political Sciences |
| Relationship | Privatdozent |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 860-0022-00L | Complexity and Global Systems Science   Number of participants limited to 64. Prerequisites: solid mathematical skills. Particularly suitable for students of D-ITET, D-MAVT and ISTP | 3 credits | 2V | D. Helbing, N. Antulov-Fantulin | |
| Abstract | This 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 currently bothering the world. | ||||
| Learning objective | Participants 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. | ||||
| Content | This 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 / Notice | Mathematical skills can be helpful | ||||