Saving Human Lives: What Complexity Science and Information Systems can Contribute
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Abstract (arXiv)
We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Feb 2014.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 2.
4 papers.
- 2022Neural parameter calibration for large-scale multi-agent modelscited by 14
- 2021Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learningcited by 5
- 2025mmFlux: Crowd Flow Analytics with Commodity mmWave MIMO Radarcited by 0
- 2025Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systemscited by 0
- 2025Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems6 Aug 2025
- 2025mmFlux: Crowd Flow Analytics with Commodity mmWave MIMO Radar9 Jul 2025
- 2022Neural parameter calibration for large-scale multi-agent models27 Sep 2022
- 2021Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning15 Mar 2021