Complexity: Global Banking – “The Sources of Unpredictability”

The Bank of England in Threadneedle Street, Lo...

Image via Wikipedia

Image via Wikipedia

A recent interesting speech by Mervyn King, Governor of the Bank of England, on the “Uncertainty in Macroeconomic Policy Making: Art or Science?” discusses the sources of unpredictability. He goes on to claim that the sources of unpredictability are at least three: “First, it is very difficult to assign probabilities […]

This is not the first time that Bank of England have dealt with the matter of learning lessons from other “disciplines”. Andy, Haldane, Executive Director, Financial Stability went in to some detail during a presentation in April 2009.

“This paper considers the financial system as a complex adaptive system. It applies some of the lessons from other network disciplines – such as ecology, epidemiology, biology and engineering – to the financial sphere. Peering through the network lens, it provides a rather different account of the structural vulnerabilities that built-up in the financial system over the past decade and suggests ways of improving its robustness in the period ahead.”

The text of the speech is available here.

Of course this all feeds in rather well to previous blog items in relation to my firmly held belief that a fundamental part of the solution is the application of Quantitative Complexity analysis tools and management.

The Woman Who Just Might Save the Planet and Our Pocketbooks

Fund Strategy Magazine: Complexity lessons from nature for a better economic future

The following blog item from Willem Buiter reiterates the folly of financial modelling and, effectively, reinforces the argument

The unfortunate uselessness of most ’state of the art’ academic monetary economics

Those of us who have marvelled at the non-linear feedback loops between asset prices in illiquid markets and the funding illiquidity of financial institutions exposed to these asset prices through mark-to-market accounting, margin requirements, calls for additional collateral etc.  will appreciate what is lost by this castration of the macroeconomic models.  Threshold effects, critical mass, tipping points, non-linear accelerators – they are all out of the window.  Those of us who worry about endogenous uncertainty arising from the interactions of boundedly rational market participants cannot but scratch our heads at the insistence of the mainline models that all uncertainty is exogenous and additive.

Technically, the non-linear stochastic dynamic models were linearised (often log-linearised) at a deterministic (non-stochastic) steady state.  The analysis was further restricted by only considering forms of randomness that would become trivially small in the neighbourhood of the deterministic steady state.  Linear models with additive random shocks we can handle – almost !

Even this was not quite enough to get going, however.  As pointed out earlier, models with forward-looking (rational) expectations of asset prices will be driven not just by conventional, engineering-type dynamic processes where the past drives the present and the future, but also in part by past and present anticipations of the future.  When you linearize a model, and shock it with additive random disturbances, an unfortunate by-product is that the resulting linearised model behaves either in a very strongly stabilising fashion or in a relentlessly explosive manner.  There is no ‘bounded instability’ in such models.  The dynamic stochastic general equilibrium (DSGE) crowd saw that the economy had not exploded without bound in the past, and concluded from this that it made sense to rule out, in the linearized model, the explosive solution trajectories.  What they were left with was something that, following an exogenous  random disturbance, would return to the deterministic steady state pretty smartly.  No L-shaped recessions.  No processes of cumulative causation and bounded but persistent decline or expansion.  Just nice V-shaped recessions.

There actually are approaches to economics that treat non-linearities seriously.  Much of this work is numerical – analytical results of a policy-relevant nature are few and far between – but at least it attempts to address the problems as they are, rather than as we would like them lest we be asked to venture outside the range of issued we can address with the existing toolkit.

The practice of removing all non-linearities and most of the interesting aspects of uncertainty from the models that were then let loose on actual numerical policy analysis, was a major step backwards.  I trust it has been relegated to the dustbin of history by now in those central banks that matter.