Biology of Applied Digital Ecosystems

TL;DR

This paper explores how biological ecosystem properties can inform the design of digital ecosystems, emphasizing self-organization, robustness, and scalability through biological principles and ecological measures.

Contribution

It identifies key biological features not fully utilized in digital ecosystems and proposes a framework for mimicking these features to enhance system robustness and scalability.

Findings

Digital Ecosystem exhibits biological-like population dynamics

Simulations confirm ecological measures correlate with system development

Responsive adaptation demonstrates ecological succession in digital systems

Abstract

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, with measures originating from theoretical ecology, to confirm its likeness to a biological ecosystem. Including the responsiveness to requests for applications from the user base, as a measure of the 'ecological succession' (development).