W Brian Arthur: the Complexity Economics vision
What is an ‘Economy’?
For much of modern economic thought, the economy has been presented as a system which not only tends towards, but habitually, announces itself at ‘equilibrium’, for which the transient, inter-equilibrium, state is of little interest. Here, the economy is an efficient information processing and allocation mechanism, aggregating vast quantities of information (billions of preferences, offers, production and transaction costs) and deftly allocating scarce resources and time to the highest marginal gain. Where the economy fails to be this model of efficiency, it is because of distortions to its underlying modus operandi — things are getting in the way of efficiency. Such distortions can be identified, policies developed, and (politics-willing) efficiency regained.
So far so good.
But what if the time spent by any economy in a state of ‘equilibrium’ is actually a blip in time — a rare moment of dynamic rest — before the system plunges again into a long-lived transient, a period characterised by all manner of complicated feedback mechanisms, dynamic preferences, learning and behavioural shifts, of temporal dynamics characterised by long ‘build’ periods and short-lived ‘release’ events?
In other words, what if non-equilibrium is the normal state of economic systems? What then?
Thankfully, Santa Fe Institute economist W Brian Arthur has devoted his life to thinking about just such non-equilibrium economics (Arthur, 2010). He coined the term complexity economics in a 1999 Science article to denote such a situation (Arthur, 1999).
Three themes could be said to sum Arthur’s counter-cultural complexity economics perspective: I. non-equilibrium dynamics; II. perpetual novelty; and III. inductive reasoning (Arthur, 2010). Arthur summarises elsewhere, “complexity economics sees the economy as in motion, perpetually ‘computing’ itself — perpetually constructing itself anew. Where equilibrium economics emphasizes order, determinacy, deduction, and stasis, complexity economics emphasizes contingency, indeterminacy, sense-making, and openness to change.” (Arthur, 2013: p.1)
Indeed, complexity economics draws on a broader movement in the sciences called ‘complexity science’ (Holland, 2014), that studies, “how the interacting elements in a system create overall patterns, and how these overall patterns in turn cause the interacting elements to change or adapt. It might study how individual cars together act to form patterns in traffic, and how these patterns in turn cause the cars to alter their position. Complexity is about formation — the formation of structures — and how this formation affects the objects causing it.” (Arthur, 1999:p.2)
Notice Arthur’s emphasis on formation. For Arthur, questions of allocation are reasonably investigated with notions of equilibrium, the formation of the economic system, and its onward sectorial and networked trajectory need to employ other tools (Arthur, 2013). A good example of these tools is found in Arthur’s fascinating combinatorial and computational study of technology, “the evolution of technology within a simple computer model” (Arthur & Polak, 2006).
Whereas much of neo-classical and even new-growth theory treats technology as a simple multiplier, Arthur is deeply fascinated by how the process of invention, modularisation, and recombination that stands behind all of our productive methods actually comes about. In the paper, Arthur and Polak use software to model the evolutionary process of discovery itself. Small pieces of code are built from primitive operators, which, after trial-and-error, combination and re-combination, are built up over successive iterations into complex functional operators capable of mimicking advanced computing tasks.
In doing so, Arthur and Polak shed new light on the very nature of technology itself, “technology”, they write, “the collection of devices and methods available to human society — evolves by constructing new devices and methods from ones that previously exist, and in turn offering these as possible components — building blocks — for the construction of further new devices and elements. The collective of technology in this way forms a network of elements where novel elements are created from existing ones and where more complicated elements evolve from simpler ones.” (Arthur & Polak, 2006:p.23, see also Arthur, 2011) Here is theme II: perpetual novelty — the ability for economic systems, driven by the creativity of mankind, to constantly re-form, re-fashion, re-skill, re-build themselves.
Arthur classically applied complexity thinking to the nature of economic decision making. In, ‘Inductive reasoning and bounded rationality’ (Arthur, 1994) Arthur explains that in ill-defined, ‘messy’ decision environments, humans typically employ a kind of behaviour which ‘may not be familiar in economics’ (p.407), called ‘inductive reasoning’ (theme III).
Arthur explains, “Modern psychology tells us that as humans we are only moderately good at deductive logic, and we make only moderate use of it. But we are superb at seeing or recognizing or matching patterns — behaviours that confer obvious evolutionary benefits. In problems of complication then, we look for patterns; and we simplify the problem by using these to construct temporary internal models or hypotheses or schemata to work with. We carry out localised deductions based on our current hypotheses and act on them. As feedback from the environment comes in, we may strengthen or weaken our beliefs in our current hypotheses, discarding some when they cease to perform, and replacing them as needed with new ones. In other words, when we cannot fully reason or lack full definition of the problem, we use simple models to fill the gaps in our understanding. Such behaviour is inductive.” (Arthur, 1994: p.406-407)
In the paper, Arthur describes the results of perhaps his most famous complexity economics experiment: the El Farol Bar problem. The El Farol is a real tapas bar in Santa Fe, New Mexico, USA. Arthur used the El Farol as the setting for a complex, many-agent, decision-problem: when to go to the bar? Each agent will go to the bar if their current best-performing predictor rule estimates that less than 60 people will be at the bar on a given night (the bar seats 100 but we assume they prefer it not too crowded). Arthur endowed 100 agents with a sub-set of prediction rules from a ‘soup’ of such rules such as ‘the same as last week’, or ‘the rounded average of the last four weeks’, ’the same as 2 weeks ago’,…and so on…The key is that some agents have access to a few such rules (say, 3), others to many (say, 24). Each week the actual attendance is learned by all agents and their personal rule-sets ranked, with each agent’s new rank 1 rule being used to decide their attendance in the following week.
Remarkably, this complex, yet ‘simple’ inductive reasoning, pattern-forming, algorithm, sees a very stable 60 or so people attending the bar each iteration (week) of the simulation (refer to fig. 1). However, the exact people attending are always different, the best-performing rules in constant flux. Here is an example of a complex adaptive system: agent behaviours and even agent decision-rules are in constant flux, micro-actions lead to aggregates which fold back and inform micro-behaviour. ‘Simple’ (boundedly rational), inductive rules give rise to seemingly ‘complex’ aggregate behaviour. No wonder the Santa Fe Institute, the ‘birthplace’ of complexity science has as its motto, ‘From simplicity to complexity’.
Taken together, W Brian Arthur has made significant contributions to the developing mental models of complexity economics. From his work, and the work of several others, mostly connected to the Santa Fe Institute, a body of work has emerged which takes the ‘messy’ complexity of the real economy at face-value, in much the same way as Veblen, Schumpeter, and Hayek did before.
Unsurprisingly, the non-equilibrium mindset of complexity economics has made few inroads into mainstream Economics journals or departments. However, progress seems inevitable. Few economists who encounter the ideas of W Brian Arthur leave with their prior conceptualisation of the Economy unchanged.
Arthur concludes, “Complexity economics is still in its early days and many economists are pushing its boundaries outward. It shows us an economy perpetually inventing itself, perpetually creating possibilities for exploitation, perpetually open to response. An economy that is not dead, static, timeless, and perfect, but one that is alive, ever-changing, organic, and full of messy vitality.” (Arthur, 2013:p.19)
Who knew the economy could be so interesting?
Arthur, W. B. (1994). ‘Inductive Reasoning and Bounded Rationality’. The American Economic Review. 84(2), Papers and proceedings of the hundred and sixth annual meeting of the AEA, May 1994, p.406-411.
Arthur, W.B (1999), ‘Complexity and the Economy’, Science, 284:107-109, 1999.
Arthur, W. B., & Polak, W., (2006), ‘The evolution of technology within a simple computer model’, Complexity, 11(5), 23–31.
Arthur, W. B. (2010). ‘Complexity, the Santa Fe Approach, and non-Equilibrium Economics’, History of Economic Ideas, 18(2), 149–166.
Arthur, W. B., (2011), ‘The Nature of Technology: what it is and how it evolves’, Free Press.
Arthur, W. B., (2013), ‘Complexity Economics: A Di_ erent Framework for Economic Thought’, Santa Fe Institute Working Papers, 13-04-012.
Holland, J. (2014), Complexity: A very short introduction, Oxford University Press, Oxford.