Herbert Spencer (1857) argued that cultures “evolve” before Darwin revealed the mechanism for how species evolve; and for 150 years, “social evolution” has been part of our lexicon. Certainly societies change over time, but there has always been a nagging concern that social evolution is not based on quite the same principles as biological evolution. The problem, of course, is that biological evolution is defined and understood in terms of genes and populations of reproducing organisms. These concepts are not easy to import into discussions of social change. Dawkins (in The Selfish Gene) introduced the term “meme” to represent the cultural equivalent of a gene in societal contexts. But, Dawkins never intended for the concept to have a rigorous meaning, and indeed it has never been possible to build the social science equivalent of population genetics based on memes.
In The Engine of Complexity, Evolution as Computation, I argue the fundamental mechanism that makes biological evolution possible is an information processing strategy, i.e. a computation (which I call the engine of complexity). This computational strategy is iterated (cyclic) and is conveniently presented visually. In the book, I further suggest that when a question arises as to whether some process is evolutionary in the Darwinian sense the question can be settled by determining whether or not that process incorporates the engine of complexity computation. For example, various authors have loosely referred to the developmental processes that create the brain to be “evolutionary.” Careful examination of brain development shows that brain development does not incorporate the engine of complexity computation; so though the outcome is impressive, the strategy used is different from that which underlies biological evolution (discussed in Chapter 7 of The Engine of Complexity).
In contrast, when social evolution is examined, it is possible to identify the engine of complexity computation working at the heart of many aspects of social change. An appropriately labeled diagram taken from the book (Figure 11-1) is shown below.
In this diagram, concepts are informational, while outcomes result from that information. Thus, a concept might be a business plan whose outcome is a business, a scientific hypothesis whose outcome is explanation of a series of experiments, or religious strictures whose outcomes are human behaviors. The diagram has exactly the same form as a diagram of the engine of complexity computation (see A General Theory of Evolution blog).
Because they can be described in exactly the same way, I conclude that many aspects of cultural change happen by means of mechanisms that utilizes the same computational strategy as biological evolution. This is despite the obvious fact that information in these systems is encoded in completely different ways. Placing biological evolution and cultural evolution on the same theoretical foundation creates unity in our understanding and establishes from a computational perspective how the impressive accomplishments of these two rather different systems are possible.