Javier Argota Sánchez-Vaquerizo: Catalogue data in Autumn Semester 2021 |
| Name | Mr Javier Argota Sánchez-Vaquerizo |
| Address | Computational Social Science ETH Zürich, STD F 7 Stampfenbachstrasse 48 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 39 86 |
| javier.argota@gess.ethz.ch | |
| Department | Humanities, Social and Political Sciences |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0585-41L | Computational Social Science  Number of participants limited to 50. | 3 credits | 2S | D. Helbing, J. Argota Sánchez-Vaquerizo, M. Korecki | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The seminar aims at three-fold integration: (1) bringing modeling and computer simulation of techno-socio-economic processes and phenomena together with related empirical, experimental, and data-driven work, (2) combining perspectives of different scientific disciplines (e.g. sociology, computer science, physics, complexity science, engineering), (3) bridging between fundamental and applied work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants of the seminar should understand how tightly connected systems lead to networked risks, and why this can imply systems we do not understand and cannot control well, thereby causing systemic risks and extreme events. They should also be able to explain how systemic instabilities can be understood by changing the perspective from a component-oriented to an interaction- and network-oriented view, and what fundamental implications this has for the proper design and management of complex dynamical systems. Computational Social Science and Global Systems Science serve to better understand the emerging digital society with its close co-evolution of information and communication technology (ICT) and society. They make current theories of crises and disasters applicable to the solution of global-scale problems, taking a data-based approach that builds on a serious collaboration between the natural, engineering, and social sciences, i.e. an interdisciplinary integration of knowledge. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Computational Social Science https://science.sciencemag.org/content/sci/323/5915/721.full.pdf Manifesto of Computational Social Science https://link.springer.com/article/10.1140/epjst/e2012-01697-8 Social Self-Organisation https://www.springer.com/gp/book/9783642240034 How simple rules determine pedestrian behaviour and crowd disasters https://www.pnas.org/content/108/17/6884.short Peer review and competition in the Art Exhibition Game https://www.pnas.org/content/113/30/8414.short Generalized network dismantling https://www.pnas.org/content/116/14/6554.short Computational Social Science: Obstacles and Opportunities https://science.sciencemag.org/content/369/6507/1060?rss%253D1= Bit by Bit: Social Research in the Digital Age https://www.amazon.co.uk/Bit-Social-Research-Digital-Age-ebook/dp/B072MPFXX2/ Further literature will be recommended in the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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