Collective learning and optimal consensus decisions in social animal groups.

Albert B Kao,Colin Torney,Noam Miller,Iain D Couzin,Andrew Hartnett,Stefano Allesina

doi:10.1371/journal.pcbi.1003762

Albert B Kao, Colin Torney + Show 4 more

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https://doi.org/10.1371/journal.pcbi.1003762

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Abstract

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

Highlights

Associative learning tunes an organism’s behavior to exploit statistical patterns in the environment and can improve decisionmaking accuracy across a wide range of scenarios [1,2]
We describe an empirically derived model of collective learning and compare the learned performance of animals within groups to the optimal behavior for a wide range of environmental conditions and group sizes
We find that the learning rules derived from experiments with individual animals readily generalize to a social context, and these relatively simple rules result in behavior that is close to optimal, even when individuals know neither the size of their group nor the properties of environmental cues

Summary

Introduction

Associative learning tunes an organism’s behavior to exploit statistical patterns in the environment and can improve decisionmaking accuracy across a wide range of scenarios [1,2]. The consequences of the behavior (such as a reward or punishment) may alter the animal’s valuation of the environmental cues, resulting in a feedback loop that gradually tunes its behavior to its environment [3,4,5,6,7] In contrast to this relatively simple scenario, many animals – including the majority of species commonly employed in learning experiments, such as rats, pigeons, and humans – live and forage naturally in social groups.

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