Self-Organization and Artificial Life

TL;DR

The paper clarifies the concept of self-organization within Artificial Life (ALife) by surveying historical roots, multiple definitional lenses, and cross-domain applications. It introduces three ALife domains—soft, hard, and wet—as central contexts and presents a two-axis classification (internal/external and direct/indirect interactions) to organize diverse self-organizing systems. By outlining characteristic mechanisms, patterns, and design implications, the work proposes a principled, mechanism-based perspective to synthesize novel ALife artifacts and living technologies. It also identifies open questions about programming, predictability, and cross-disciplinary impact, highlighting both the promise and the limits of self-organization as a unifying framework for ALife research.

Abstract

Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

Figures (1)

• Figure 1: Turing pattern formation Turing1952 as an illustrative example of self-organization in computational models. A: Simulation in CA using Young's discrete model Young1984. B: Simulation in PDE using Turing's original formulation. Figures from Sayama2015.