Emergence: from physics to biology, sociology, and computer science

TL;DR

This paper explores the concept of emergence across various scientific disciplines, emphasizing its defining features, theoretical approaches, and significance in understanding complex systems and phenomena.

Contribution

It provides a comprehensive overview of emergence, its characteristics, theoretical models, and its role in multiple fields, highlighting the importance of intermediate scales and effective theories.

Findings

Emergence involves properties not reducible to individual parts.

Effective theories and toy models are crucial for understanding emergence.

Emergence is central to many scientific and societal challenges.

Abstract

Many systems involve numerous interacting parts and the whole system can have properties that the individual parts do not. I take this novelty as the defining characteristic of an emergent property. Other characteristics associated with emergence discussed include universality, order, complexity, unpredictability, irreducibility, diversity, self-organisation, discontinuities, and singularities. Emergent phenomena are widespread across physics, biology, social sciences, and computing, and are central to major scientific and societal challenges. Understanding emergence involves considering the stratification of reality across different scales (energy, time, length, complexity), each with its distinct ontology and epistemology, leading to semi-autonomous scientific disciplines. A central challenge is bridging the gap between macroscopic emergent properties and microscopic component interactions. Identifying an intermediate mesoscopic scale where new, weakly interacting entities or modular structures emerge is key. Theoretical approaches, such as effective theories (describing phenomena at a specific scale) and toy models (simplified systems for analysis), are vital. The Ising model exemplifies how toy models can elucidate emergence characteristics. Emergence is central to condensed matter physics, chaotic systems, fluid dynamics, nuclear physics, quantum gravity, neural networks, protein folding, and social segregation. An emergent perspective should influence scientific strategy by shaping research questions, methodologies, priorities, and resource allocation. An elusive goal is the design and control of emergent properties.