Decoding collective communications using information theory tools

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.

Naïve individuals promote collective exploration in homing pigeons

“…We quantified the amount of information transferred between birds using information-theoretic measures (Cover and Thomas 2005;Pilkiewicz et al 2020) estimated from the series of rotations of the experienced, > = (_ P , U ≥ 1), and of the naïve, ? = (J P , U ≥ 1), birds (Figure 1, point 3).…”

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

“…This definition is based on the work of Pikovsky et al [23]. Our candidate measure for synchronicity is based on the information-theoretic transfer entropy measure, which has been widely studied to understand the flow of information between agents within complex adaptive systems, such as swarms [20], [22], [24]. Transfer entropy is a non-parametric approach that provides a measure of the asymmetric, directed transfer of information between two stochastic processes [25].…”

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

“…This definition is based on the work of Pikovsky et al [23]. Our candidate measure for synchronicity is based on the information-theoretic measure of transfer entropy, which has been widely studied to understand the flow of information between agents within complex adaptive systems, such as swarms [20], [22], [24]. Transfer entropy is a non-parametric approach that provides a measure of the asymmetric, directed transfer of information between two stochastic processes [25].…”

“…We select transfer entropy over other approaches due to the intuitiveness of its interpretation, and the well-established foundation of research use [20], [22], [24], [27], [28]. Our specific transfer entropy calculation methodology is based on local transfer entropy [29] and implemented per [24], demonstrating the reconstruction of local information flows over time.…”