The next revolution in Economics: Complexity Economics

I have been enthusiastic about Complexity economics for a while now. And the state of the economy post-2009 only makes me a bigger fan of the whole setup.

Like all people you have mostly heard economic talk monopolized by Paul Krugman and his neo-Keynesian acolytes, or free market fundamentalists. This year’s Nobel Prize shows the divide in the academic community.

If you are someone who studied economics, then you, like me, have been confronted for years with something called « equilibrium » models, and rationality hypotheses. It was like a constant attempt to over-simplify complex mathematical models in order to get incomplete answers and convince ourselves that we are doing a good job.

The recent financial crisis, and the tragic failure of nearly all academic economists to seriously explain what happened and conclude with recommendations, have pushed me to read more about the field of complexity economics, and i got answers.

What is complexity economics?

Complexity economics is a branch of economics that originated in the Santa Fe institute in the late 1980’s. W.Brian Arthurlays the basic framework of complexity economic thought (link available below, did not find the book on amazon though publish date is supposed to be 2013). He defines complexity theory as the following

Complexity economics holds that the economy is not necessarily in equilibrium, that computation as well as mathematics is useful in economics, that increasing as well as diminishing returns may be present in an economic situation, and that the economy is not something given and existing but forms from a constantly developing set of institutions, arrangements, and technological innovations.

While traditional economics constantly searches for the path to optimal behavior under equilibrium conditions, allowing periodical disruptions , complexity economics differs. Instead of assuming rationality or theorizing an optimal agent behavior, complexity economic deals with situations of non-equilibrium.

Instead of studying fixed mechanisms, complexity studies ever evolving exchanges between economic agents in situations that arise consequently to the same agents’ prior actions. The study of economics seizes to be the exploration of a model and its predictive power, but the exploration of the interactions occurring between different levels of the system, and their predictive power. In this sense, complexity economics is closer to political economy than to the classical science of economics, as Brian Arthur puts it

It gives a different view, one where actions and strategies constantly evolve, where time becomes important, where structures constantly form and re-form, where phenomena appear that are not visible to standard equilibrium analysis, and where a meso-layer between the micro and the macro becomes important. This view, in other words, gives us a world closer to that of political economy than to neoclassical theory, a world that is organic, evolutionary, and historically-contingent.

Equilibrium and the world today

Any assertion that today’s world is in equilibrium is delusional. Any attempt to forecast or predict in today’s world can be painted as plain arrogance. Economic theory has not grasped this fact yet, completely.

Complexity economics explains that non-equilibrium is proper to the system itself. Brian Arthur proceeds in the following way: by asking the question « how an agent reacts to a certain problem », traditional mainstream economics assumes non-equilibrium. If an agent were in equilibrium, then why will he alter his behavior anyways?

And as is noted by Brian Arthur too, brilliant economists have an answer to that question. Non-equilibrium states of the economy exist, by they are immediately corrected by the market or another mechanism to return to a static equilibrium point.

The world today however fits a more complexity theory scenario. The two factors the complexity theory takes into consideration to explain the state of non-equilibrium are in full action in our world today: fundamental uncertainty and technological change.

Fundamental uncertainty in today’s post crisis world is ever-present. Let me take one example, the Fed tapering talk. Financial markets all over the world are suspended on a decision by the U.S Federal Reserve to cut on its exceptional low rates and toxic asset purchases.  With the market suspended on the uncertainty of the Fed decision, here is what happened

“As market participants gain additional insight from the words of Federal Reserve officials or by policy actions in coming quarters, further asset price volatility seems likely,” Lacker, who doesn’t vote on policy this year, said in a speech today in White Sulphur Springs, West Virginia. “This type of volatility is a normal part of the process of incorporating new information into financial asset prices.”

This small episode of uncertainty on the financial markets is the constant for today’s economy. We are sitting on a system so big that a hurricane in the US can trigger a default of a small British bank. The level of uncertainty inherent to the system, in some sectors, like the financial sector for example, makes the state of non-equilibrium a fact that analysis under equilibrium cannot neglect.

Complexity economics

Technological change has been dismissed by traditional economists as happening too rarely to have a  short-term impact on the system. On the long-term, the economic system adapts to tech change and attains a new equilibrium. While this was true in the 20th century, today’s world does not account for that reality.

Small incremental tech changes in the way people communicate, react, share ideas, share knowledge, and even consume have been happening since the early 2000’s, and shaping the way people do things. In a system where interactions between individual agents shape the way the system behaves in the future, changing the nature of interactions between individuals changes the system. In today’s increasingly tech changing world, non-equilibrium is a natural state of the economy.

The economy as a living system

Being under a state of non-equilibrium, the agents forming an economy react through feedback, and the economy gains the property of always being under formation, changing, adapting.

Through experimentation and past experiences, agents adapt to technological disruptions and uncertainties, by producing an ecology, or an outcome, the same experimentation and experiences helped create. Computation is used in the complexity theory to describe the way agents arrive to these outcomes. Brian Arthur lays a practical example of how a complexity theory framework works, applied on the steam engine and railway discovery.

The steps involved yield the following algorithm for the formation of the economy. 1. A novel technology appears. It is created from particular existing ones, and enters the active collection as a novel element.

2. The novel element becomes available to replace existing technologies and components in existing technologies.

3. The novel element sets up further “needs” or opportunity niches for supporting technologies and organizational arrangements.

4. If old displaced technologies fade from the collective, their ancillary needs are dropped. The opportunity niches they provide disappear with them, and the elements that in turn fill these may become inactive.

5. The novel element becomes available as a potential component in further technologies—further elements.

6. The economy—the pattern of goods and services produced and consumed—readjusts to these steps. Costs and prices (and therefore incentives for novel technologies) change accordingly.

Thus the railway locomotive was constructed from the already-existing steam engine, boiler, cranks, and iron wheels. It entered the collective around 1829 (step 1); replaced existing horse-drawn trains (step 2); set up needs for the fabrication of iron rails and the organization of railways (step 3); caused the canal and horse-drayage industries to wither (step 4); became a key component in the transportation of goods (step 5); and in time caused prices and incentives across the economy to change (step 6). Such events may operate in parallel: new opportunities for example appear almost as soon as a new technology appears.

In this example of how a complexity theory algorithm plays out, there is an infinite number of situations that allow the algorithm to continue, as further tech changes will further disrupt agent behavior reviving the algorithm.

Complexity and the financial system

The financial industry, hugely responsible of a lot of what happened pre, during, and after the 2009 crisis, is poorly described by traditional economics. Combine it with increasing technological change shaping communications, the system is in a state of fragile equilibrium. Any small spontaneous event might cause a severe disruption or a crash, and failing to predict that, traditional economics just dismisses it.

In this context, complexity economics, combined with psychology and behavioral economics, is a complete framework to grasp the fundamental forces that shape financial markets.

Instead of assuming that « We Know », and describing « Optimal Behavior », maybe we should simply assume that we do not control what individuals do, however we know the reasons that push them to take a decision. By making this assumption, we stop looking for rational behavior under equilibrium, and start asking questions about what happens in situations of chaos and over-leveraging.

To resume, the complexity theory of economics describes the real world situation in a far more accurate way than equilibrium models, mainstream, and textbook economics. The question is, do we have the humility to simply acknowledge complexity, and not drown in simplistic academic arrogance?

Links

Complexity Economics:A Different Framework for Economic Thought

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The next revolution in Economics: Complexity Economics was last modified: mai 27th, 2014 by Tony

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