Multi-Scale Asset Distribution Model for Dynamic Environments
- URL: http://arxiv.org/abs/2207.12063v1
- Date: Mon, 25 Jul 2022 11:14:49 GMT
- Title: Multi-Scale Asset Distribution Model for Dynamic Environments
- Authors: Payam Zahadat and Ada Diaconescu
- Abstract summary: We study the impact that the topology of the multi-scale control process has upon the system's ability to self-adapt asset distribution.
Results show how different topological characteristics and different competition levels between system branches impact overall system profitability.
- Score: 1.370633147306388
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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