From Traffic Modeling to Smart Cities and Digital Democracies

851-0467-00L From Traffic Modeling to Smart Cities and Digital Democracies
Autumn Semester 2024
Dozierende: D. Helbing, R. K. Dubey

Place: LEE D 101
Time: Mondays 12.15 - 14.00

This seminar will present speakers who discuss the challenges and opportunities arising for our cities and societies with the digital revolution. Besides discussing questions of automation using Big Data, AI and other digital technologies, we will also reflect on the question of how democracy could be digitally upgraded, and how citizen participation could contribute to innovation, sustainability, resilience, and quality of life. This includes questions around collective intelligence and digital platforms that support creativity, engagement, coordination and cooperation.

Learning Goals:
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 diverse scientific disciplines (e.g. sociology, computer science, physics, complexity science, engineering),
(3) bridging between fundamental and applied work.

Students should learn to reflect, present and discuss questions related to how traffic systems, smart cities, and democracy can be (digitally) upgraded, how citizen participation can contribute to innovation, progress, sustainability, resilience, and quality of life.

Presentations and Grading:
To collect credit points, students will have to actively contribute and give an individual presentation for around 30 minutes in the seminar on a subject agreed with the lecturer, after which the presentation will be discussed. The presentation will be graded.

Selection Criteria for Papers:
For the student presentations, please choose a publication in the area of Computational Social Science from:
• an internationally established, peer-reviewed journal with a reasonably high impact factor (say, >2)
• a publication with a reasonably high number of citations (say, >50)
• a publication by an established scientists with a good track record (say, an h-index >30)

Non-scientific papers or preprints (except those of well-established scientists) are to be avoided.

Please consult us about your choice for approval of the paper and subject.