Decoding collective communications using information theory tools. Issue 164 (25th March 2020)

• Record Type: Journal Article
• Authors: Pilkiewicz, K. R.; Lemasson, B. H.; Rowland, M. A.; Hein, A.; Sun, J.; Berdahl, A.; Mayo, M. L.; Moehlis, J.; Porfiri, M.; Fernández-Juricic, E.; Garnier, S.; Bollt, E. M.; Carlson, J. M.; Tarampi, M. R.; Macuga, K. L.; Rossi, L.; Shen, C.-C.
• Is Part Of: Journal of the Royal Society interface. Volume 17:Issue 164(2020)
• Publication Date: 2020-03-25
• Subjects: collective behaviour -- mutual information -- transfer entropy -- causation entropy; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2019.0563
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : Organisms have evolved sensory mechanisms to extract pertinent information from their environment, enabling them to assess their situation and act accordingly. For social organisms travelling in groups, like the fish in a school or the birds in a flock, sharing information can further improve their situational awareness and reaction times. Data on the benefits and costs of social coordination, however, have largely allowed our understanding of why collective behaviours have evolved to outpace our mechanistic knowledge of how they arise. Recent studies have begun to correct this imbalance through fine-scale analyses of group movement data. One approach that has received renewed attention is the use of information theoretic (IT) tools like mutual information, transfer entropy and causation entropy, which can help identify causal interactions in the type of complex, dynamical patterns often on display when organisms act collectively. Yet, there is a communications gap between studies focused on the ecological constraints and solutions of collective action with those demonstrating the promise of IT tools in this arena. We attempt to bridge this divide through a series of ecologically motivated examples designed to illustrate the benefits and challenges of using IT tools to extract deeper insights into the interaction patterns governing group-level dynamics. We summarize some of the approaches taken thus far to circumvent existing challenges in this area and we conclude with an optimistic, yet cautionary perspective.