Complexity Economics

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Let me declare that I am no expert but  those responsible for policy, legislation and regulation have no excuses.

“Classical” economic theory is based upon concepts such as rational decisions and stable equilibrium whereas, unless I am making a complete idiot of myself, REAL economic systems do not achieve “static equilibrium” and are inherently unstable. As for rational…I’ll leave you to make up your own mind!

So, whilst there is a huge industry dedicated to financial prediction of one sort or another I can’t help wondering WHY!?

I am not comfortable with the thought of nations or Corporations wasting money in trying to predict the future but the chances of my voice (even though not entirely alone) making any difference are about as long as the chances of some highly paid economists or quants accurately predicting the economic future. So the best that I can do is attempt to educate and assist those businesses whose leaders no longer trust financial or political institutions, question the practice or recognise the sheer folly of such exercises!

It’s a dirty job but someone’s gotta do it and I feel compelled to give it my best shot!!!

Getting my head round complexity has only reinforced my belief that investing in prevention and getting “Fit for randomness” is the most sensible strategy in the face of risk and uncertainty (whether natural or man-made). Quantitative Complexity Management, from Ontonix, enables a business to measure and manage the internal interdependencies and external inter-connectedness of its company ecosystem. We extend the “risk horizon” into areas of (epistemic) uncertainty and, in doing so:

extend the range of conventional risk management techniques and tools

provide  “crisis anticipation”

measure the structural robustness of the business

map and rate the effectiveness of internal interdependencies and external inter-connectedness

When a social, economic or political system is explicitly conceptualized as a dynamic process involving the interrelationships of many actors who possess limited information about the intentions and objectives of others, it is clear that the behaviour of such a system cannot be understood exclusively by asking whether it has any steady states.

These extracts are from a 1997 paper: What Should Policymakers know about Economic Complexity?

Complex economic systems have several messages useful to policymakers. First, interdependence among various actors can create multiple types of internally consistent aggregate behaviour. As a result, economic environments can become stuck in undesirable steady states. Such undesirable steady states may include high levels of social pathologies or inferior technology choices. Second, the consequences of policies will depend critically on the nature of the interdependences. In particular, the effects of different policies may be highly nonlinear, rendering history a poor guide to evaluating policy effectiveness. Hence, even though many complex economic environments seem to be particularly likely situations in which there exist social welfare enhancing policies, it may prove especially difficult to identify such policies. At a minimum, detailed empirical studies which underlie conventional policy analysis should prove to be even more valuable in complex environments.

To illustrate the differences between conventional and complexity economics…without asking anyone to take a huge chunk out of their day…the following table, from Wikipedia, illustrates the differences between the complexity perspective and classical economics. Eric Beinhocker proposes five concepts that distinguish complexity economics from traditional economics. The first five categories are Beinhocker’s synthesis, the last four are from W. Brian Arthur as reprinted in David Colander’s The Complexity Vision.[3]

Complexity Economics

Traditional Economics


Open, dynamic, non-linear systems, far from equilibrium

Closed, static, linear systems in equilibrium


Modelled individually; use inductive rules of thumb to make decisions; have incomplete information; are subject to errors and biases; learn to adapt over time; heterogeneous agents

Modelled collectively; use complex deductive calculations to make decisions; have complete information; make no errors and have no biases; have no need for learning or adaptation (are already perfect), mostly homogeneous agents


Explicitly model bi-lateral interactions between individual agents; networks of relationships change over time

Assume agents only interact indirectly through market mechanisms (e.g. auctions)


No distinction between micro/macro economics; macro patterns are emergent result of micro level behaviours and interactions.

Micro-and macroeconomics remain separate disciplines


The evolutionary process of differentiation, selection and amplification provides the system with novelty and is responsible for its growth in order and complexity

No mechanism for endogenously creating novelty, or growth in order and complexity


Technology fluid, endogenous to the system

Technology as given or selected on economic basis


Formulation of preferences becomes central; individuals not necessarily selfish

Preferences given; Individuals selfish

Origins from Physical Sciences

Based on Biology (structure, pattern, self-organized, life cycle)

Based on 19th-century physics (equilibrium, stability, deterministic dynamics)


Patterns and Possibilities

Price and Quantity

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3 Responses to Complexity Economics

  1. An excellent paper well worth reading in the context of the above:

    Rethinking Economics Using Complexity Theory

    In this paper we argue that if we want to find a more satisfactory approach to tackling the major socio-economic problems we are facing, we need to thoroughly rethink the basic assumptions of
    macroeconomics and financial theory. Making minor modifications to the standard models to remove “imperfections” is not enough, the whole framework needs to be revisited.

    Let us here enumerate some of the standard assumptions and postulates of economic theory.

  2. Andrew Smithers remarks that “..if, like other scientific endeavours, economics is an attempt to understand, predict and control the unknown through quantitative analysis, the kind of uncertainty affecting economic interactions is critical in determining its successes and failures.”

  3. Economists Need to Admit When They’re Wrong

    …significant change would threaten the standing of the economics profession. Much of economists’ authority stems from their claims to insight on which policies will make people better off. Those claims arise from core theorems of mathematical economics — known as welfare theorems — which in turn depend on some wildly implausible assumptions, such as the idea that people are perfectly rational and make decisions with full awareness of all possible futures.

    If economists used more realistic assumptions, the theorems wouldn’t work and claims to any insight about public welfare would immediately fall apart. Take a few tiny steps from mathematical fantasy into reality, and you quickly have no theory at all, no reason to think the market is superior to alternatives. The authority of the profession goes up in a puff of smoke.

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