A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation

Abstract

Social insects allocate their workforce in a decentralised fashion, addressing multiple tasks and responding effectively to environmental changes. This process is fundamental to their ecological success, but the mechanisms behind it are not well understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To address this gap, we propose a game theoretical model of task allocation. Our main findings are twofold: Firstly, the specialisation emerging from self-organised task allocation can be largely determined by the ecology. Weakly specialised colonies in which all individuals perform more than one task emerge when foraging is cheap; in contrast, harsher environments with high foraging costs lead to strong specialisation in which each individual fully engages in a single task. Secondly, social interactions lead to important differences in dynamic environments. Colonies whose individuals rely on their own experience are predicted to be more flexible when dealing with change than colonies relying on social information. We also find that, counter to intuition, strongly specialised colonies may perform suboptimally, whereas the group performance of weakly specialised colonies approaches optimality. Our simulation results fully agree with the predictions of the mathematical model for the regions where the latter is analytically tractable. Our results are useful in framing relevant and important empirical questions, where ecology and interactions are key elements of hypotheses and predictions.

Honeybee communication during collective defence is shaped by predation

“…Such an evolutionary perspective on self-organisation has been provided using neural networks [ 14 , 15 ] but while being one of the most powerful tools of machine learning, neural networks are typically hard to interpret because of their high dimensionality. Evolutionary game theory has also recently been applied to study task allocation in social insects [ 16 ], modelling behavioural changes on the scale of colony lifetime under certain imposed pay-off relations for the individual behaviour. Game-theoretic approaches provide interesting novel perspectives on the dynamics of task distribution in a population, but usually give no account of the agent-based mechanisms that underlie this dynamics [ 17 ], in stark contrast to the approach we will follow in this paper.…”

Achieving task allocation in swarm intelligence with bi-objective embodied evolution

“…However, the authors considered only the fitness with respect to genomes of the subspecies instead of those of the individual agents, thus ignoring the distribution of subspecies themselves. Therefore, we adopt a different computational model proposed by Chen et al (2020). They have noted that the task allocation behaviors in social insects are determined by the reward functions of their individual actions and those of their cooperative actions.…”

“…In case that f (x n ) is multimodal, the agents can operate at optimal or near-optimal efficiency at multiple distributions of effort between the two tasks, therefore the characteristics of agents favors specialization. This model simplifies the computational model used by Chen et al (2020).…”

“…Recently, there has been a growing interest in such models (Gordon, 2016). Notably, Chen et al (2020) have investigated individual and social learning in creating a division of labor among agents in a self-organized way. This is similar to the approaches to task allocation in embodied evolution.…”

“…Therefore, in this paper, we utilize a novel framework for a task allocation problem proposed by Chen et al (2020). This framework encourages specialized behaviors via the interactions between a global and a local objective function.…”