A New Framework for Understanding Emergence in Complex Systems

Explore a new framework for predicting emergent phenomena in complex systems. Learn how concepts like causal, informational, and computational closure offer new insights into emergence, from ant colonies to financial markets. Explore practical applications for your work and life.

A New Framework for Understanding Emergence in Complex Systems
An abstract image of a murmuring of starlings

Stay tuned at the end of this article for a practical guide on how to apply the ideas from this paper to your life and work.

Scientists and philosophers have grappled with the concept of emergence in complex systems for a long time. Emergent phenomena are those fascinating instances where the whole becomes greater than the sum of its parts, which gives rise to unexpected behaviors and properties.

We’re familiar with examples of emergence such as the collective intelligence of ant colonies or the consciousness emerging from billions of neurons, for example. Even though we've been able to observe and describe emergent behaviors, predicting them has remained largely out of reach. This means that when it comes to dealing with complexity we are left being more reactive than proactive.

Recent research has given us some new tools. By bridging concepts from information theory, computational mechanics, and complex systems science, researchers have developed a theoretical framework that, for the first time, provides a formal foundation for understanding and potentially predicting emergent phenomena.

The researchers introduce several key concepts and demonstrate their interrelationships—causal, informational, and computational closure. These concepts help us understand when a system's behavior at one level can be fully explained or predicted without needing to know all the details of what's happening at lower levels. Here are the key concepts in more detail:

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