Context-dependent interaction leads to emergent search behavior in social aggregates

TL;DR

This paper presents a decentralized, group-based search strategy enabling autonomous agents to locate chemical sources in complex, heterogenous flow environments without complex gradient assessment, highlighting emergent collective intelligence.

Contribution

It introduces a novel context-dependent interaction model that allows groups to solve source localization without advanced individual cognition, demonstrating emergent environmental awareness.

Findings

Groups can locate chemical sources in complex flows using simple social rules.

Decentralized interactions lead to emergent environmental awareness.

The method does not require gradient measurement or complex calculations.

Abstract

Locating the source of an advected chemical signal is a common challenge facing many living organisms. When the advecting medium is characterized by either high Reynolds number or high Peclet number the task becomes highly non-trivial due to the generation of heterogenous, dynamically changing filamental concentrations which do not decrease monotonically with distance to the source. Defining search strategies which are effective in these environments has important implications for the understanding of animal behavior and for the design of biologically inspired technology. Here we present a strategy which is able to solve this task without the higher intelligence required to assess spatial gradient direction, measure the diffusive properties of the flow field or perform complex calculations. Instead our method is based on the collective behavior of autonomous individuals following simple social interaction rules which are modified according to the local conditions they are experiencing. Through these context-dependent interactions the group is able to locate the source of a chemical signal and in doing so displays an awareness of the environment not present at the individual level. Our model demonstrates the ability of decentralized information processing systems to solve real world problems and also illustrates an alternative pathway to the evolution of higher cognitive capacity via the emergent, group level intelligence which can result from local interactions.