Saving Human Lives: What Complexity Science and Information Systems can Contribute

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Abstract

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.

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Most cited references 141

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Power-law distributions in empirical data

Cosma Rohilla Shalizi, Aaron Clauset, M. Newman (2007)

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.

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THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES

R Fisher (1937)

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The tragedy of the commons.

(1968)

The population problem has no technical solution; it requires a fundamental extension in morality.

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Author and article information

Contributors

Dirk Helbing: dirk.helbing@gess.ethz.ch

Journal

Journal ID (nlm-ta): J Stat Phys

Journal ID (iso-abbrev): J Stat Phys

Title: Journal of Statistical Physics

Publisher: Springer US (Boston )

ISSN (Print): 0022-4715

ISSN (Electronic): 1572-9613

Publication date (Electronic): 5 June 2014

Publication date PMC-release: 5 June 2014

Publication date (Print): 2015

Volume: 158

Issue: 3

Pages: 735-781

Affiliations

[ ]ETH Zurich, Swiss Federal Institute of Technology, 8092 Zurich, Switzerland

[ ]Risk Center, ETH Zurich, Swiss Federal Institute of Technology, 8092 Zurich, Switzerland

[ ]Robert Koch-Institute, 13353 Berlin, Germany

[ ]Institute for Theoretical Biology, Humboldt-University, 10115 Berlin, Germany

[ ]Wearable Computing Laboratory, ETH Zurich, Swiss Federal Institute of Technology, 8092 Zurich, Switzerland

[ ]Center for Adaptive Rationality (ARC), Max Planck Institute for Human Development, 14195 Berlin, Germany

[ ]Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ UK

[ ]Systems Centre, Department of Civil Engineering, University of Bristol, Bristol, BS8 1UB UK

[ ]Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany

[ ]Center for Comparative and International Studies, ETH Zurich, Swiss Federal Institute of Technology, 8092 Zurich, Switzerland

[ ]Faculty of Natural Sciences and Mathematics, University of Maribor, 2000 Maribor, Slovenia

Article

Publisher ID: 1024

DOI: 10.1007/s10955-014-1024-9

PMC ID: 4457089

PubMed ID: 26074625

ArXiV ID: 1402.7011

SO-VID: 56ead4c7-d63f-4c7e-aa9b-50667b169388

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

History

Date received : 31 January 2014

Date accepted : 20 May 2014

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Keywords: crowd disasters,crime,terrorism,war,disease spreading, complexity science

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Keywords: crowd disasters, crime, terrorism, war, disease spreading, complexity science

Saving Human Lives: What Complexity Science and Information Systems can Contribute

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Abstract

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UCL: UN SDG 03 Good Health and Well-Being

Most cited references 141

Power-law distributions in empirical data

THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES

The tragedy of the commons.

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