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Decoding Collective Communications Using Information Theory Tools Royalsocietypublishing.Org/Journal/Rsif K Decoding collective communications using information theory tools royalsocietypublishing.org/journal/rsif K. R. Pilkiewicz1, B. H. Lemasson2, M. A. Rowland1, A. Hein3,4, J. Sun5, A. Berdahl6,M.L.Mayo1, J. Moehlis7, M. Porfiri8, E. Fernández-Juricic9, Review S. Garnier10, E. M. Bollt5, J. M. Carlson11, M. R. Tarampi12, K. L. Macuga13, L. Rossi14 and C.-C. Shen15 1 Cite this article: Pilkiewicz KR et al. 2020 Environmental Laboratory, U.S. Army Engineer Research and Development Center (EL-ERDC), Vicksburg, MS, USA 2EL-ERDC, Newport, OR, USA Decoding collective communications using 3National Oceanic and Atmospheric Administration, Santa Cruz, CA, USA information theory tools. J. R. Soc. Interface 4University of California, Santa Cruz, CA, USA 17: 20190563. 5Department of Mathematics, Clarkson University, Potsdam, NY, USA 6 http://dx.doi.org/10.1098/rsif.2019.0563 School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA 7Department of Mechanical Engineering, University of California, Santa Barbara, CA, USA 8Department of Mechanical and Aerospace Engineering and Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA 9Department of Biological Sciences, Purdue University, West Lafayette, IN, USA Received: 9 August 2019 10Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ, USA Accepted: 28 February 2020 11Department of Physics, University of California, Santa Barbara, CA, USA 12Department of Psychology, University of Hartford, West Hartford, CT, USA 13School of Psychological Science, Oregon State University, Corvallis, OR, USA 14Department of Mathematical Sciences, University of Delaware, Newark, DE, USA 15Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA Subject Category: KRP, 0000-0002-3278-2268; BHL, 0000-0001-8022-110X; MP, 0000-0002-1480-3539; Life Sciences–Earth Science interface SG, 0000-0002-3886-3974 Subject Areas: Organisms have evolved sensory mechanisms to extract pertinent information environmental science, biomathematics, from their environment, enabling them to assess their situation and act accord- systems biology ingly. 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 coordi- Keywords: nation, however, have largely allowed our understanding of why collective collective behaviour, mutual information, behaviours have evolved to outpace our mechanistic knowledge of how transfer entropy, causation entropy 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 infor- mation, transfer entropy and causation entropy, which can help identify causal Authors for correspondence: interactions in the type of complex, dynamical patterns often on display K. R. Pilkiewicz when organisms act collectively. Yet, there is a communications gap between e-mail: [email protected] studies focused on the ecological constraints and solutions of collective B. H. Lemasson action with those demonstrating the promise of IT tools in this arena. We e-mail: [email protected] 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. 1. Introduction Collective motion is an adaptive strategy found across multiple scales of bio- Electronic supplementary material is available logical organization, from cellular migrations to crowds of pedestrians [1–4]. online at https://doi.org/10.6084/m9.figshare. Consequently, research on this subject is generally interdisciplinary and provides c.4881012. © 2020 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. insights into a broad range of socially mediated actions, such as information in animal communications, however, remains a 2 resource acquisition, navigation, risk mitigation and even basic contentious topic in biology and how ‘information’ is defined royalsocietypublishing.org/journal/rsif democratic principles [5]. Yet, while few assumptions are often clashes with Shannon’s original definition [10,35,39]. required to model collective motion, the search for general Shannon himself had warned his peers against what he saw ‘rules’ that govern these dynamical systems continues [6–9]. as the growing misuse of the theory across disciplines (the Ecological and evolutionary factors have played an impor- bandwagon effect) [40]. Nonetheless, IT approaches are tant role in generating the algorithms governing collective rooted in statistical mechanics [41] and therefore have the behaviour, but these forces can also be a source of consternation potential to provide a model-free means of quantifying statis- for investigators. Collective actions often rely on social tical associations between data streams, such as the positions cues, which are inherently ambiguous and ephemeral, and or orientations recorded between individuals over time. the benefits associated with group membership are context- Before proceeding, it is worth clarifying why one should dependent and can quickly become costs (e.g. improved consider using IT tools if there is a chance of getting mislead- resource acquisition and vigilance versus density-dependent ing information. After all, there is already a rich variety of competition or energetic losses from false alarms) [10]. While approaches used to quantify inter-individual interactions J. R. Soc. Interface the macroscopic patterns of such collective actions are well (e.g. pair-wise correlations [18,19], force-matching [20], documented across a wide array of taxa (bacteria [11], insects mixed-models [42], neural networks [43] and extreme-event [12], fish [13], birds [14] and humans [15]), the nature of the synchronization [44]). This methodological diversity holds inter-individual interactions driving them remains an open the promise of providing robust insights when different question [16,17]. Specifically, while many studies are able to approaches converge on similar conclusions. – 17 infer interactions among group members (e.g. [6,18 23]) it There is also merit in adopting common quantities that can : 20190563 remains challenging to synthesize these lessons across improve current efforts to make comparisons across studies or bodies of work as approaches and conclusions can vary on a systems in the absence of a consensus. Consider the order par- case-by-case basis. ameter. It is an intuitive quantity that characterizes a group’s Recently, there has been a renewed interest in the appli- coordination (or order) and has been widely adopted, yet it cation and development of information theoretic (IT) tools tells us nothing about the underlying interactions that have to study interaction patterns in both real and synthetic data. generated the observed behaviours. IT tools have the potential IT tools are being used to identify statistical structures, infor- to fill this gap in a number of ways. Mutual information pro- mation flow and causal relationships on topics ranging from vides an equitable means of drawing statistical comparisons societal trends [24] to leader–follower dynamics [23,25–28] between variables that correlations can misconstrue [45–48]. and predator–prey interactions [29]. IT tools are well suited Transfer entropy enables one to isolate influential interactions for characterizing statistical patterns in time varying, dynami- [49] and causation entropy can distinguish between direct cal systems and they have played a prominent role in doing and indirect influence [50,51]. Below we demonstrate each of so across a range of disciplines [30–32]. However, while these properties and we begin by reviewing the source there are seminal sources on information theory and the of these metrics, Shannon’s entropy. metrics derived from Claude Shannon’s work (e.g. [33,34]), these are often tailored to specific disciplines and can be chal- lenging to interpret and apply due to a failure to recognize 2.1. Information and Shannon entropy either the mathematical or biological conditions involved in The foundation of modern information theory is Shannon’s a given process [35–38]. Consequently, we find the recent notion of entropy [33], which measures a stochastically fluctu- application of IT tools in collective behaviour to be skewed ating observable and was originally used to quantify the towards the physical and mathematical disciplines. The uncertainty found in decoding a message. The heading of a extent to which these tools can help ecologists, psychologists bird in a flock, for example, is a measurable quantity that will and evolutionary biologists studying collective behaviour fluctuate due to the complex moment-to-moment decisions a remains unknown. bird must make in response to the movements of its neighbours; The goal of this paper is to provide a brief, practical syn- the variability
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