In the latest book that I am writing, on how to find the business opportunities that have repeated multiple times over the last 40,000 years, I draw on insights from complexity science. Think of complexity science as an alternative to chemistry to explain the relationship between physics and biology. Two different languages and ways of thinking to explain the same events.
A fundamental part of complexity science is the concept of a network. Examples of networks are the Internet, Facebook and every "community". In a chapter on organizations in the book I make the point that great organizations are a stack of networks. For example, if we look at a great university like Harvard or MIT, we can understand them by their networks: students, faculty, alumni, institutes of learning, research partners, etc. These networks process information separately but compliment the whole of information. Fail to create a strong network in any one of these categories, and the university probably fails to achieve greatness.
This morning I was reading an article by Ray Kurweil, futurist and authoity on AI who trained at MIT with the late Marvin Minsky. Kurweil tells the story that multi-layer neural nets were proposed in the 1960s but were rejected by Minsky and his associates. Such networks are commonly used in AI today. The point of the story--multi-layer neural nets look an awful lot like stacked networks in great organizations.
Patterns that repeat in nature and in computer science are the basis for powerful thinking frameworks. Keep your eyes open for stacked networks.