Information socialtaxis and efficient collective behavior emerging in groups of information-seeking agents

Abstract

Individual behavior, in biology, economics, and computer science, is often described in terms of balancing exploration and exploitation. Foraging has been a canonical setting for studying reward seeking and information gathering, from bacteria to humans, mostly focusing on individual behavior. Inspired by the gradientclimbing nature of chemotaxis, the infotaxis algorithm showed that locally maximizing the expected information gain leads to efficient and ethological individual foraging. In nature, as well as in theoretical settings, conspecifics can be a valuable source of information about the environment. Whereas the nature and role of interactions between animals have been studied extensively, the design principles of information processing in such groups are mostly unknown. We present an algorithm for group foraging, which we term "socialtaxis," that unifies infotaxis and social interactions, where each individual in the group simultaneously maximizes its own sensory information and a social information term. Surprisingly, we show that when individuals aim to increase their information diversity, efficient collective behavior emerges in groups of opportunistic agents, which is comparable to the optimal group behavior. Importantly, we show the high efficiency of biologically plausible socialtaxis settings, where agents share little or no information and rely on simple computations to infer information from the behavior of their conspecifics. Moreover, socialtaxis does not require parameter tuning and is highly robust to sensory and behavioral noise. We use socialtaxis to predict distinct optimal couplings in groups of selfish vs. altruistic agents, reflecting how it can be naturally extended to study social dynamics and collective computation in general settings.foraging | information maximization | chemotaxis | exploration | exploitation F oraging is fundamental for survival and reproduction in numerous species and is often based on sparse and noisy sensory cues in complex environments. Understanding how organisms represent space and navigate and the utility functions and computations that underlie movement patterns and social behavior (1-6) critically depends on characterizing the nature of foraging. Theoretical models of foraging have been pivotal in economics, physics (7,8), and machine learning (9) as a general framework for decision making, exploration and exploitation, optimization, and learning (10).Climbing the gradient of a sensory signal is an efficient foraging strategy for finding a strong source in nonturbulent environments or on a microscopic scale, as demonstrated, for example, by theoretical and experimental studies of bacterial chemotaxis (1, 11). However, for weak sources, or on a macroscopic scale that is characterized by turbulent flows, the signal coming from a source is typically broken into random, sparse, and disconnected patches (12). Without a continuous gradient, a different strategy is needed to read and use information from these patches about the location of the source (13). Verg...

Social Information Links Individual Behavior to Population and Community Dynamics

“…Computational models have shown that social information can improve the ability of groups of foragers to track resources that shift at small spatial scales and over relatively short, behavioral timescales [5,9,79] or between breeding seasons [32], but that large-scale or longterm environmental changes can eliminate these benefits or cause social information to become highly detrimental to populations [32,80,81]. For example, habitat fragmentation can reduce or cease migration in populations that rely on information from a small number of leaders, the leadership by which can fail to evolve in a fragmented landscape [ 2 9 6 _ T D $ D I F F ] [80].…”

Social interactions drive efficient foraging and income equality in groups of fish

“…We focused here on attraction-based strategies for the fish, since in our experiments fish were attracted to areas where neighbors detected food. However, previous studies have suggested other search strategies that were based on repulsion between individuals ( Tani et al, 2014 ) or on maximizing information about the location of food ( Karpas et al, 2017 ). Although our modeling framework gave an excellent fit to the data, it is possible that foraging fish combine or alternate between strategies in different environmental conditions, based on group composition or their internal state ( Farine et al, 2014 ; Michelena et al, 2009 ; Harpaz et al, 2017 ; Kurvers et al, 2011 ).…”

“…Living in a group has clear benefits, including expansion of sensory sensitivity (1)(2)(3)(4), sharing of responsibilities and resources (5)(6)(7), collective computation (1)(2)(3)(4)(8)(9)(10), and the potential for symbiotic relations between members that would allow for specialization by individual members (7,11). Understanding the interactions among individuals that give rise to macroscopic behavior of groups is therefore central to the study and analysis of collective behavior in animal groups and other biological systems.…”

“…Living in a group has clear benefits, including expansion of sensory sensitivity ( Miller and Bassler, 2001 ; Pratt et al, 2002 ; Berdahl et al, 2013 ; Ward et al, 2011 ), sharing of responsibilities and resources ( Clutton-Brock et al, 1999 ; McGowan and Woolfenden, 1989 ; Chittka and Muller, 2009 ), collective computation ( Miller and Bassler, 2001 ; Pratt et al, 2002 ; Berdahl et al, 2013 ; Ward et al, 2011 ; Karpas et al, 2017 ; Shklarsh et al, 2011 ; Ward et al, 2012 ), and the potential for symbiotic relations between members that would allow for specialization by individual members ( Chittka and Muller, 2009 ; Gordon, 1996 ). Understanding the interactions among individuals that give rise to macroscopic behavior of groups is therefore central to the study and analysis of collective behavior in animal groups and other biological systems.…”

“…Moreover, most models studying how schooling or flocking may improve foraging efficiency have explored the case of single sources, or clumped food patches ( Karpas et al, 2017 ; Shklarsh et al, 2011 ; Ward et al, 2012 ; Hein et al, 2016 ; Miller et al, 2013 ; Aplin et al, 2014 ; Jolles et al, 2017 ). Yet, in many real-world situations, animals are likely to encounter distributed food sources, where maintaining a tight group may not be beneficial for all group members.…”