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Information and phase transitions in socio-economic systems

Terry Bossomaier1*, Lionel Barnett2 and Michael Harré3

Author Affiliations

1 Centre for Research in Complex Systems, Charles Sturt University, Panorama Ave, Bathurst NSW 2795, Australia

2 Department of Informatics, University of Sussex, Falmer, Brighton BN1 9QH, UK

3 Department of Civil Engineering, University of Sydney, Sydney, NSW, Australia

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Complex Adaptive Systems Modeling 2013, 1:9  doi:10.1186/2194-3206-1-9

Published: 8 April 2013


We examine the role of information-based measures in detecting and analysing phase transitions. We contend that phase transitions have a general character, visible in transitions in systems as diverse as classical flocking models, human expertise, and social networks. Information-based measures such as mutual information and transfer entropy are particularly suited to detecting the change in scale and range of coupling in systems that herald a phase transition in progress, but their use is not necessarily straightforward, possessing difficulties in accurate estimation due to limited sample sizes and the complexities of analysing non-stationary time series. These difficulties are surmountable with careful experimental choices. Their effectiveness in revealing unexpected connections between diverse systems makes them a promising tool for future research.

Phase transitions; Mutual information; Transfer entropy; Social networks; Stock markets; Expertise