Difference between revisions of "CompactCourse: Simulating Societies Evolution and Swarming - Summer20"

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| tutorials = -
| tutorials = -
| exam = written report (depending on the number of participants)
| exam = written report (depending on the number of participants)
| tumonline = t.b.a.
| tumonline = https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950464347

Revision as of 10:39, 20 February 2020

Summer 20
Dr. Florian Rupp, Universität der Bundeswehr; contact: Dr. rer. nat. Tobias Neckel
Time and Place
every second Friday (in average), 10:00-12:00, room t.b.a.

currently planned dates: 24.04., 08.05., 15.05., 19.06., 26.06., 03.07., 10.07.

all interested students, in particular students of BGCE, Games Engineering, TopMath, CSE, Mathematics, and Informatics
written report (depending on the number of participants)
Semesterwochenstunden / ECTS Credits
2 credits



The course "Simulating Societies: Evolution & Swarming" provides a fast-paced introduction and overview for modeling and simulating the dynamics of heterogeneous multi-agent systems with respect to spatial movement. Our focus is on mobile societies, the evolution of ideologies, beliefs and interaction strategies, as well as on swarm formation. Compared especially with common physical systems, the most interesting aspect of this class of problems is that the agents may have different views (preferences, ideologies, movement, and rescue strategies) and that these may be substantially influenced by social interaction.

  • Principles of agent-based modeling, such as preferences, utilities, modeling and control of crowds
  • Multiple scale models with micro-descriptions as dynamic transitions, Markovian transitions involving 1st principles, and multi-agent limits
  • Socioeconomic transactions and group interactions, like interaction decisions and the choice of behavior, the establishment of norms and standards, social networking, and non-locality including movements to far-flung locations
  • Evolution and learning of socio-economic strategies (e.g. by means of genetic algorithms or machine learning approaches)
  • Computational aspects for large crowds of agents incl. combination of ecologic and economic dynamics, cluster consensus, and feedback controls for crowd movement in certain areas (e.g. corridors, open spaces).


via dropbox. For access, please contact Dr. Rupp directly.

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