Decentralized decision making by an ant colony: drift-diffusion model of individual choice, quorum and collective decision

TL;DR

This paper presents a stochastic drift-diffusion model of ant colony decision-making, showing how decentralized individual assessments lead to accurate collective choices, with analysis and comparison to experimental data.

Contribution

It introduces a novel multi-stage model combining drift-diffusion and exclusion processes to analyze ant colony decision dynamics.

Findings

Collective decisions are less error-prone than individual assessments.

Model predicts the speed of decision emergence based on parameters.

Theoretical results align with experimental observations.

Abstract

Ants are social insects. When the existing nest of an ant colony becomes uninhabitable, the hunt for a new suitable location for migration of the colony begins. Normally, multiple sites may be available as the potential new nest site. Distinct sites may be chosen by different scout ants based on their own assessments. Since the individual assessment is error prone, many ants may choose inferior site(s). But, the collective decision that emerges from the sequential and decentralized decision making process is often far better. We develop a model for this multi-stage decision making process. A stochastic drift-diffusion model (DDM) captures the sequential information accumulation by individual scout ants for arriving at their respective individual choices. The subsequent tandem runs of the scouts, whereby they recruit their active nestmates, is modelled in terms of suitable adaptations of the totally asymmetric simple exclusion processes (TASEP). By a systematic analysis of the model we explore the conditions that determine the speed of the emergence of the collective decision and the quality of that decision. More specifically, we demonstrate that collective decision of the colony is much less error-prone that the individual decisions of the scout ants. We also compare our theoretical predictions with experimental data.