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.

Highlights

  • Collective motion is an adaptive strategy found across multiple scales of biological organization, from cellular migrations to crowds of pedestrians [1,2,3,4]

  • Collective actions often rely on social cues, which are inherently ambiguous and ephemeral, and the benefits associated with group membership are contextdependent and can quickly become costs [10]

  • Armed with a short primer on how information theoretic (IT) metrics like mutual information, transfer entropy and causation entropy can be useful in the analysis of movement data, we illustrate how our previous 6 examples relate to real-world challenges in the study of collective motion

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Summary

Introduction

Collective motion is an adaptive strategy found across multiple scales of biological organization, from cellular migrations to crowds of pedestrians [1,2,3,4] Research on this subject is generally interdisciplinary and provides. There has been a renewed interest in the application and development of information theoretic (IT) tools to study interaction patterns in both real and synthetic data. IT tools are well suited for characterizing statistical patterns in time varying, dynamical systems and they have played a prominent role in doing so across a range of disciplines [30,31,32]. The goal of this paper is to provide a brief, practical synthesis on the benefits and pitfalls of applying IT tools like mutual information, transfer entropy and causation entropy to quantify interaction patterns in groups on the move. We conclude by summarizing common challenges in the application of these tools and discuss their future potential for the study of collective behaviour

Decoding collective communications
Information and Shannon entropy
Mutual information
Transfer entropy
Causation entropy
Identifying communication patterns
Inferring local interactions from global patterns
Tracking information flow
Caveats and concerns
Data length
Sampling interval
Discretization
Equivalency
Stationarity
Reliability
Conclusion
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