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.

在此研究中，我们扩展了一种植物灵感的资产分配模型，引入了一个更普遍的多尺度模型，适用于更广泛的自然和人工系统领域，研究了多尺度控制流程拓扑对系统自适应资产分配能力的影响，结果表明不同的拓扑特征和系统分支之间的不同竞争水平对整个系统的盈利能力、适应延迟和环境变化时的干扰产生影响。这些发现为系统设计人员选择最适合其特定应用和执行环境的拓扑和配置提供了基础。