Insight & Analysis

The Algorithm Economist: bringing ‘the dismal science’ up to date

Published: Jan 2019

If money makes the world go round, then algorithms are its driving force. Black box style coding has propelled investment decisions, most notably in the Big Bucks world of high frequency trading, for decades. In the 1980s, ‘programme trading’ was common between the S&P 500 equity and futures markets.

And no wonder, compared to humans, algorithms are faster, more accurate and can process infinitely more data.

However, one area of apparent resistance to the lure of algorithmic speed and accuracy in the financial community can be found in the central banks. More precisely, amongst the economists who help direct the policies that affect everyone.

Whilst the Fed economists, for example, use arcane mechanisms such as the Anderson-Moore algorithm (for solving linear saddle point models), their wider adoption as a tool to aid policy-making is still very much a work in progress.

When respected economists such as Nouriel Roubini dismiss that other great techy hope, blockchain, as “the most overhyped – and least useful – technology in human history” and “in practice, nothing more than a glorified spreadsheet,” what hope is there?

Yet with several major, and seemingly unforeseen, economic disasters to their discredit, the economist community (purveyors of the so-called ‘dismal science’, after the gloomy predictions of political economist, Thomas Malthus) is surely due an update?

Recessions, noted John Williams, President of The Federal Reserve Bank of San Francisco, and a voting member on the US’s policy-making Federal Open Market Committee, “generally happen because of unanticipated shocks”.

The global financial crisis of 2008 was caused by the subprime mortgage crisis, the recession of the early 2000s was caused by the dot-com bubble, and the recession of the early 1990s was triggered by the sharp rise in oil prices.

There will always be anxiety about how the Fed in particular reacts to such events, admits Williams. But the inability of economists to spot them has a global impact; the US sneezes and the world catches a cold, as the saying goes.

Machine time

So why not let the machines have a go at dictating policy? Today’s available computing power allied with improvements in algorithmic complexity and accuracy – even weather predictions are more reliable these days – could mean that machines really are better suited to making economic decisions than humans.

We could be about to enter the era of the ‘Algorithm Economist’, where coding decides economic policy. Arguably, as the new breed of millennial economists emerge into positions of authority, fully steeped in economic theory yet also immersed in the benefits of technology, that time is not far off.

According to commentator, Yuval Noah Harari, currently only about 1% of the population understands the economy. In 20 years, he argues, if most of the economy is run by algorithms, no-one will understand it.

Decision-makers will then be taking policy directions from machines that will be using a rationale far too complex for humans to understand. Machines will be making most tactical decisions, being much faster and infinitely more consistent than their human counterparts. Perhaps that is a good thing.

It’s on its way

Of course, an algorithm requires the widest set of variables, inputs and scenarios to function effectively. But even then, their ability to adapt to extreme states is questionable, at least by today’s standards.

Optimally tweaking interest rates to increase or decrease inflation may be easy in normal times for a smart bit of coding with the right inputs. But when things aren’t normal, could an algorithm come up with the right policy to defend an economy? Possibly not. But then, if it was that smart, perhaps it would not allow economic disaster to draw its first breath anyway.

With the availability of more and better data, and huge computing power, a future where algorithms will find causes rather than just correlations, and solutions instead of just problems, is not beyond reach.

Warren Buffet has been out-invested by an algorithm. A chess world champion has been beaten by one. Just as treasurers may one day find that artificial intelligence has taken their job, the day of the ‘Algorithm Economist’ is surely coming. If corporate systems connect to those of the central banks, then a revolution is nigh. The solution? Reinvent yourself before it’s too late.

Any takers for the concept of Algorithm Economist? Is it just fanciful nonsense? Let us know what you think at

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