Multi-Scale Asset Distribution Model for Dynamic Environments

TL;DR

This paper introduces a generalized multi-scale asset distribution model inspired by plants, analyzing how control topology affects a system's ability to adapt resource distribution in dynamic environments.

Contribution

It extends a plant-inspired model to a more general framework applicable to various natural and artificial systems, emphasizing the impact of topology on adaptation.

Findings

Different topologies influence adaptation speed and stability.

System profitability varies with control structure and competition levels.

Topology selection is crucial for optimal resource management in changing environments.

Abstract

In many self-organising systems the ability to extract necessary resources from the external environment is essential to the system's growth and survival. Examples include the extraction of sunlight and nutrients in organic plants, of monetary income in business organisations and of mobile robots in swarm intelligence actions. When operating within competitive, ever-changing environments, such systems must distribute their internal assets wisely so as to improve and adapt their ability to extract available resources. As the system size increases, the asset-distribution process often gets organised around a multi-scale control topology. This topology may be static (fixed) or dynamic (enabling growth and structural adaptation) depending on the system's internal constraints and adaptive mechanisms. In this paper, we expand on a plant-inspired asset-distribution model and introduce a more general multi-scale model applicable across a wider range of natural and artificial system domains. We study the impact that the topology of the multi-scale control process has upon the system's ability to self-adapt asset distribution when resource availability changes within the environment. Results show how different topological characteristics and different competition levels between system branches impact overall system profitability, adaptation delays and disturbances when environmental changes occur. These findings provide a basis for system designers to select the most suitable topology and configuration for their particular application and execution environment.