Chapter 1 of Murray Gell-Mann’s Quark and Jaguar, “Early Light”, opened the vista of Complex Adaptive Systems to the world (pp 16-21). The book is full of references and examples of such systems; this review pays special attention to Chapter 18, “Adaptive and Maladaptive Schemata”, especially its section on Cultural DNA (pp 292-294).
This review, paper #14 in our scenario series, was inspired by the Music Trust’s work on opera issues (Dick Letts’s Opera in Australia as a Complex Adaptive System). Paper #14 attempts to explain these systems from the Gell-Mann treatise as a basis of outlining the Australian music sector as an extensively interactive set of such systems which can lead to very different futures. It uses opera as an example in a broad-brush way only. A list of scenario papers forming the basis for our projections of plausible 20-year music sector futures is shown at the end of this paper.
While Gell-Mann’s text is the primary source, this review includes two diagrams of our own, trying to visualise in a simple way the complex interactions that exist in the real world, including the Australian music sector. They were based on Quark and Jaguar and the Letts opera paper, respectively.
Complexity is contrasted with the simple underlying principles of nature (Gell-Mann, p 16). As the universe evolved, the law of gravity (simple in principle, but as Einstein’s general theory of relativity and current cosmology science amply demonstrates, conceptually anything but simple) caused the clumping of matter into galaxies, stars and planets – complex adaptive systems with their own degree of complexity, diversity and individuality.
On Earth, the origin of terrestrial life and the process of biological evolution led to diversity in the behaviour of organisms in biological systems, how immune systems operate, how animals including humans learn and think, how human societies evolve, how investors in financial markets behave, and how computer software and hardware is designed to evolve strategies or make predictions based on past observations. (p 17)
The common feature is that each complex adaptive system (CAS) acquires information about its environment and its own interaction with that environment, identifying regularities in that information, condensing those regularities into a kind of schema or model, and acting in the real world on that schema. In each case, there are various competing schemata, and the results of action in the real world become feedbacks influencing the competition among those schemata.
In short, there is a learning process involved, influenced by the changing environment. In a simple example on pp 17-18 “you” are trying to hail a taxi in a strange city during the evening rush hour. Taxies rush by, without stopping for you. Eventually you find out, through several steps of learning, that taxies marked “Out of service” are unavailable, and that you cannot hail a taxi unless it has only the inner part of the roof light illuminated. You also find that taxies stopping ahead are snapped up by other pedestrians, so you are impelled to cast your net wider to search for a successful schema. In those “pre-Uber” days, you finally observe that on the other side of the avenue, going in the opposite direction, many taxies have just their inner roof lights on. You cross the avenue, hail one, and bingo.
“You” are a successful CAS who adapted your schema!
Gell-Mann finds that adaptation takes place at a minimum of three different levels:
The three levels of adaptation take place, generally speaking, at different time scales. An existing dominant schema can be put into action within days or months. A revolution in the hierarchy of schemata is generally associated with a longer time scale, although the culminating event may come swiftly. Extinction of societies take even longer time. (p 294)
The previous sections should have made it clear that a CAS can exist at every possible scale, from the universe through nations and persons to microscopic life, and comprising organisms, societies and institutional arrangements alike. The Australian economy is a CAS; so is the music sector; so are individual parts of the music sector, an orchestra or band, a venue, a singer, an instrumentalist, the way opera companies are managed, governments ranging from national to local, and from parliament to individual portfolios and departments, and so on.
Chart 1 represents an attempt to show the entire background and processes involved in the formation of Complex Adaptive Systems. It is the most wide-ranging graph ever likely to appear on the Knowledge Base.
In the beginning was particles, each category such as a given type of quark identical and interchangeable. Quarks are the basic building blocks of composite particles such as protons and neutrons which form the atomic nuclei.
Gravity made the universe infinitely more complex, from galaxies to the Earth’s living organisms. Gravity caused particles to become part of larger complex adaptive systems at any level we might care to contemplate. All functioning systems, whether living or associated with societies and communities, natural ecosystems and the rest, have their own CAS, each with its own schemata for interacting in the real world. All complex adaptive systems in principle influence all other systems (obviously most if they are closely related), and are influenced in turn by them. The “system of complex adaptive systems” is in a state of continuous flux, though the degree of turbulence is likely to vary with the relative power of the schemata of each CAS, and with the level of adaptation that Gell-Mann described in his section on cultural DNA.
In this sense, the CAS model explains “everything” – to call it a powerful concept may be one of the understatements of the century. And we now find that it is valuable for understanding the dynamic development of artforms such as music.
The Letts paper on Opera in Australia as a Complex Adaptive System was written independently of this #14 paper and contains some attractive classifying concepts which the Knowledge Base wishes to retain because they resonate in the arts sector and makes it special. Each has its equivalent in the Gell-Mann model but the terminology is different. Furthermore, it fits hand-in-glove into the scenario model we have developed.
Dick Letts deals with three levels of interrelated aspects of the system (Chart 2):
Where does opera fit into the three CAS levels of Gell-Mann's "cultural DNA"?
Opera does appear to be at risk of suffering the fate of the Maya empire – near-death of the unfittest. Mayan people remain a cultural influence from the Yucatan to Guatemala, but the empire is long past. The issue for opera in Australia is: Can it still turn itself around to survive despite not being the “fittest” in natural and cultural selection terms, and how can it be helped in this?
That question takes us to the “scenario” group at the bottom of Chart 2. It proceeds from what Dick Letts calls the current equilibrium level in his opera paper (large orange box in Chart 2). He calls the opportunities “basins of attraction” which takes us towards the future we are attempting to describe in terms of four scenarios from the best-case “culture prevails” to the worst-case “sliding inexorably”. No one can predict in advance what scenario is most likely to occur; we are dealing with an unpredictable nonlinear system.
But we can prepare for it by asking a sufficient number of adequate “what-if” questions, with associated plans. It is difficult, and it involves every Australian with a stake in our national cultural future. We have to coexist with a multitude of different interests and preferences, and every sector whether cultural or not has to be actively involved in promoting their respective futures.
Ultimately it all comes back to basics: Are we a first-world nation and want to remain one? How does culture help us there? How do we allocate the budget and give a proper share to cultural activities including opera (which connects with other cultural activities in CAS terms)? And, of course, what is the proper share – a question that stretches far and wide into all complex adaptive systems that are in any way associated with Australian culture, and becomes intimately associated with the basic issues of how cultural activities benefit the economic future.
These questions cannot be asked without providing the framework in which they are asked. The CAS structure is built to provide such a framework, reinforced because it is oriented towards the future by definition.
In summary, music is just an example but Opera in Australia as a Complex Adaptive System represents the first time any attempt appears to have been made to describe an artform as a CAS. Dick Letts’s paper contains some creative original concepts which could serve as a model not only for other music artforms but far beyond. The paper is firmly based on the Gell-Mann treatise and has the special merit of being capable of incorporating scenario-planning which has become a major topic in the Music in Australia Knowledge Base.
It is evident from the history of the scenario project for the music sector outlined by the list of papers below that the process of developing the project has been highly explorative, treading new paths not always visible in scenario-planning projects, occasionally running into seemingly blind alleys but always finding light at the end of the tunnel. It may seem surprising that the relevance of complex adaptive systems for music sector scenarios dawned upon us so late in the project, given our prior knowledge of Gell-Mann’s book and Dick Letts’s The Arts on the Edge of Chaos which became our first e-book on the Knowledge Base in December 2015.
Our “excuse”, if any is needed, is that the entire project from its start in 2014 has been a pioneering effort, apparently the first-ever published scenario plan for a major artform. We trust that with this added dimension of the scenario model we can travel towards the goal: to provide plausible stories with numerical projections for each of four defined scenarios from 2015 to 2035 as a potentially powerful policy-making tool.
Hans Hoegh-Guldberg, 9 January 2016