We’ve seen that conventional thinking about “the economy” struggles to accommodate technologies such as machine learning, robotics, and artificial intelligence–which means it’s ripe for a big dose of reframing. Reframing is a problem-solving strategy that flips our usual ways of thinking so that blind spots are revealed, conundrums resolved, polarities synthesized, and barriers transformed into logistics.
The Santa Fe Institute is on the reframing case: Rolling Stone called the Institute “a sort of Justice League of renegade geeks, where teams of scientists from disparate fields study the Big Questions.” W. Brian Arthur is one of those geeks. He’s also onboard with PARC — a Xerox company in “the business of breakthroughs” — and has written two seminal books on complexity economics: Complexity and the Economy (2014) and The Nature of Technology: What it Is and How it Evolves (2009). Here’s his pitch for reframing “the economy”:
The standard way to define the economy — whether in dictionaries or economics textbooks — is as a “system of production and distribution and consumption” of goods and services. And we picture this system, “the economy,” as something that exists in itself, as a backdrop to the events and adjustments that occur within it. Seen this way, the economy becomes something like a gigantic container…, a huge machine with many modules or parts.
I want to look at the economy in a different way. The shift in thinking I am putting forward here is ,,, like seeing the mind not as a container for its concepts and habitual thought processes but as something that emerges from these. Or seeing an ecology not as containing a collection of biological species, but as forming from its collection of species. So it is with the economy.
The economy is a set of activities and behaviors and flows of goods and services mediated by — draped over — its technologies: the of arrangements and activities by which a society satisfies its needs. They include hospitals and surgical procedures. And markets and pricing systems. And trading arrangements, distribution systems, organizations, and businesses. And financial systems, banks, regulatory systems, and legal systems. All these are arrangements by which we fulfill our needs, all are means to fulfill human purposes.
George Zarkadakis is another Big Questions geek. He’s an artificial intelligence Ph.D. and engineer, and the author of In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence (2016). He describes his complexity economics reframe in a recent article “The Economy Is More A Messy, Fractal Living Thing Than A Machine”:
Mainstream economics is built on the premise that the economy is a machine-like system operating at equilibrium. According to this idea, individual actors – such as companies, government departments and consumers – behave in a rational way. The system might experience shocks, but the result of all these minute decisions is that the economy eventually works its way back to a stable state.
Unfortunately, this naive approach prevents us from coming to terms with the profound consequences of machine learning, robotics and artificial intelligence.
Both political camps accept a version of the elegant premise of economic equilibrium, which inclines them to a deterministic, linear way of thinking. But why not look at the economy in terms of the messy complexity of natural systems, such as the fractal growth of living organisms or the frantic jive of atoms?
These frameworks are bigger than the sum of their parts, in that you can’t predict the behaviour of the whole by studying the step-by-step movement of each individual bit. The underlying rules might be simple, but what emerges is inherently dynamic, chaotic and somehow self-organising.
Complexity economics takes its cue from these systems, and creates computational models of artificial worlds in which the actors display a more symbiotic and changeable relationship to their environments. Seen in this light, the economy becomes a pattern of continuous motion, emerging from numerous interactions. The shape of the pattern influences the behaviour of the agents within it, which in turn influences the shape of the pattern, and so on.
There’s a stark contrast between the classical notion of equilibrium and the complex-systems perspective. The former assumes rational agents with near-perfect knowledge, while the latter recognises that agents are limited in various ways, and that their behaviour is contingent on the outcomes of their previous actions. Most significantly, complexity economics recognises that the system itself constantly changes and evolves – including when new technologies upend the rules of the game.
That’s all pretty heady stuff, but what we’d really like to know is what complexity economics can tell us that conventional economics can’t. We’ll look at that next time.