The Queen, Seventeen, Halloween…

Ten years after the 2008 crisis I came across an old Guardian article :

Queen finally finds out why no one saw the financial crisis coming

Her Majesty gets the answer to her question – four years after she asked it – on a tour of the Bank of England

One doesn’t normally keep the Queen waiting. But on Thursday Her Majesty finally received an answer to a question she first asked four years ago: why did nobody notice the “awful” financial crisis earlier?

As she toured the Bank of England’s gold vault, Sujit Kapadia, an economist and one of the Bank’s top financial policy experts, stopped the Queen to say he would like to answer the question she first posed to academics at the London School of Economics at the height of the financial crisis in 2008. “Oh,” she said, slightly taken aback, as Kapadia went on to explain that as the global economy boomed in the pre-crisis years, the City had got “complacent” and many thought regulation wasn’t necessary.

The Queen, who appeared quite animated during the discussion, said: “People got a bit lax … perhaps it is difficult to foresee [a financial crisis].”

Kapadia told Her Majesty that financial crises were a bit like earthquakes and flu pandemics in being rare and difficult to predict, and reassured her that the staff at the Bank were there to help prevent another one. “Is there another one coming?” the Duke of Edinburgh joked, before warning them: “Don’t do it again.”  …   As the Queen walked out of the building in the heart of the City towards a large crowd gathered outside, she said of her visit: “Very interesting, isn’t it?”

Having just discovered Steve Keen I decided to write to The Queen:

“Her Majesty,

After the calamitous financial crisis ten years ago You asked: “Why did no one see this coming?” In due course You received a reply from the LSE.

Unfortunately this reply was not as honest as it should have been. It failed to mention that there were a significant number of economists who did foresee the crisis.

But these economists do not belong to the dominant mainstream paradigm practiced at most universities. Their work was and largely continues to be ignored and belittled.

Ten years after, many experts agree that the response to the last crisis has been inadequate and that the next even bigger crisis is more or less inevitable.

His Royal Highness Prince Philip’s humorous but highly appropriate request “Don’t do it again!” was, I fear, in vain because he was addressing professionals who were and remain in denial of their own limitations. They are in no position to contribute to the prevention of the next crisis. To them the next crisis will be as inexplicable and surprising as the last.

I therefore humbly suggest that the reassuring incantations of mainstream economists are probably less reliable than the calculations of those they prefer to ignore.

Yours faithfully

P.S. Should You desire to meet one of those economists who did predict the last crisis, I would suggest Prof Steve Keen, Head of the School of Economics, History and Politics at Kingston University in London.”

Recently I discovered David Orrell’s Quantum Economics. Here is an excerpt from Chapter 8 : Entangled Clouds  (all bold emphases mine)

Let the confabulation begin

On a visit to the London School of Economics in November 2008 , Queen Elizabeth famously asked her hosts : ‘Why did no one see it coming ?’ ( Economists are tired of hearing this story brought up . However , the issue has never been properly settled by something like a public enquiry ; it was historic in nature and inspired the call for alternatives to mainstream economics ; and as seen below , the answers supplied over the years have proved quite revealing . )

The Queen finally received a response from the LSE in July the next year , which concluded that ‘ the failure to foresee the timing , extent and severity of the crisis and to head it off , while it had many causes , was principally a failure of the collective imagination of many bright people , both in this country and internationally , to understand the risks to the system as a whole ’ .

A related question , though , was why did no one – see it ? Consider for example the discussion at the US Federal Open Market Committee Meeting in December 2007 , when it was later determined that the recession had already begun . ‘ Overall ’ , reads the transcript , ‘ our forecast could admittedly be read as still painting a pretty benign picture : despite all the financial turmoil , the economy avoids recession and , even with steeply higher prices for food and energy and a lower exchange value of the dollar , we achieve some modest edging – off of inflation . So I tried not to take it personally when I received a notice the other day that the Board had approved more frequent drug – testing for certain members of the senior staff , myself included . [ Laughter ] ’

In fact the prediction wasn’t unconventional in the least , it was merely reflecting common opinion . The same month the OECD predicted that the slowdown in US housing ‘is unlikely to trigger a recession’ . And a study by IMF economists showed the consensus of forecasters in 2008 was that not one of 77 countries considered would be in recession the next year ( 49 of them were ) . That isn’t a ‘ Michael Fish moment ’it is a weather forecaster protesting that the storm that has already begun and is blowing down people’s houses isn’t actually a storm , as he is marched off for his drug test . Rather than helping economists see into the future , their models were preventing them from noticing what was going on outside their windows .

In 2008 testimony, Alan Greenspan told Congress that the forecasting miss happened because ‘ the data inputted into the risk management models generally covered only the past two decades , a period of euphoria ’ . That is like a weather forecaster saying they did not see the storm coming because it had been sunny for so long . ( Compare Steve Martin’s ‘ wacky weatherman ’ character Harris K . Telemacher in L.A . Story , whose forecasts of fine weather are always the same – ‘ So I pretape the weather and some sailors lost their boats , big deal ! ’ Nobel laureate economist Robert Lucas provided an innovative excuse for forecast error when he clarified in a 2009 article for The Economist that ‘ simulations were not presented as assurance that no crisis would occur , but as a forecast of what could be expected conditional on a crisis not occurring ’ . That is like a weather forecaster saying their forecast was explicitly based on no storms . ( A conditional forecast is one of the form ‘If X, then Y, ceteris paribus’ – see discussion of this clause in Chapter 4 . The usage here reflects the fact that standard models treat crises as being caused by random external shocks.)

In 2010 , during a US House of Representatives hearing on ‘ the promise and limits of modern macroeconomic theory in light of the current economic crisis ’ , a robust defence of traditional modelling approaches was provided in testimony – which as we’ll see , doesn’t seem to have dated much – by V.V . Chari from the Federal Reserve Bank of Minneapolis . According to Chari , ‘ All the interesting policy questions involve understanding how people make decisions over time and how they handle uncertainty . All must deal with the effects on the whole economy . So , any interesting model must be a dynamic stochastic general equilibrium model . From this perspective , there is no other game in town … The only alternatives are models in which the modeler does not clearly spell out how people make decisions . Why should we prefer obfuscation to clarity ? ’  ( Indeed , it frequently seems that just by calling their models dynamic , stochastic , and general , DSGE modellers have somehow ruled out all other approaches – like a weather forecaster saying that their General Circulation Model which missed the storm is still the only game in town , because any model must be general and involve circulation.

Chari offered three reasons why these models ‘ failed to predict the recent financial crisis ’ and more precisely ‘failed to emphasize the risks to which the economy was exposed in the period before the crisis’. The first was Greenspan’s excuse that the models were calibrated to a period in history when economic fluctuations in the US were relatively small . The second reason was that ‘we deemphasized the insights of the theoretical literature on the perverse effects of government bailouts because understanding these effects requires that we impute even more rationality and foresight to economic agents than we currently impute’. In other words, rational economic man was insufficiently rational.

The third reason was lack of funds . As a solution, Chari suggested more of the same: ‘ after all, when the AIDS crisis hit , we did not turn over medical research to acupuncturists . In the wake of the oil spill in the Gulf of Mexico, should we stop using mathematical models of oil pressure? Rather than pursuing elusive chimera dreamt up in remote corners of the profession, the best way of using the power in the modeling style of modern macroeconomics is to devote more resources to it.’ (Quantum approaches would presumably be right out.) He ended with a rousing plea for ‘ substantially more resources’ to go towards this ‘severely underfunded’ research programme – even if such equilibrium models must be one of the longest running , best funded and least successful international modelling efforts in the history of applied mathematics.

In 2011 , John Cochrane wrote that ‘ It is fun to say that we did not see the crisis coming , but the central empirical prediction of the efficient markets hypothesis is precisely that nobody can tell where markets are going’ (again since changes are due to random shocks ) . Future laureate Eugene Fama agreed that his theory ‘ did quite well in this episode ’ . ( Nobel – prize winning weather forecaster: ‘It is fun to say we didn’t predict the storm , but at least our prediction that we couldn’t predict it was spot – on . ’ ) In 2015 , at an event called ‘ The Genius of Economics’, laureate Paul Krugman said the crisis ‘ came as a shock to me as to almost everyone’ ; of the few people who did predict it , ‘ they also saw five crises that didn’t happen coming so it doesn’t quite count ’ , and ‘ there’s always going to be something out there that you miss’. (Weather genius accusing other , successful forecasters of always predicting a storm.)

In a 2015 Rethinking Economics debate , Dutch economist Pieter Gautier told students that ‘ we know that it is difficult to predict a crisis . We can say what we can do when a crisis occurs , such as having enough liquidity . Meanwhile , we know how we can try to prevent crises , such as making sure that banks have enough equity . Unfortunately , we don’t always listen to economists . ’ Unfortunately , we did : all the risk models that were used by financial institutions to price those complex derivatives , for example , were based on standard economic theories , including the efficient market hypothesis . That was the problem .

Why we missed the storm : more great forecasting excuses from the crisis

My work as an applied mathematician involves providing forecasts , and I know how hard it is to make accurate predictions ( I even wrote a book on the topic ) .
But when predictions do go badly wrong , the experience often offers useful information that can be used to update and improve your forecasting model . This can be a difficult process , especially since it may involve changing your mental model of reality as well ; but in economics it seems to be taking an unusually long time , as shown by the range of explanations and excuses supplied over the years .

Glenn Stevens , Governor of the Reserve Bank of Australia , in 2008 : ‘ I do not know anyone who predicted this course of events . This should give us cause to reflect on how hard a job it is to make genuinely useful forecasts . ’

Ben Bernanke musing in a 2009 commencement address : ‘ Like weather forecasters , economic forecasters must deal with a system that is extraordinarily complex , that is subject to random shocks [ from the weather gods ? ] , and about which our data and understanding will always be imperfect … Mathematicians have discussed the so – called butterfly effect , which holds that , in a sufficiently complex system , a small cause – the flapping of a butterfly’s wings in Brazil – might conceivably have a disproportionately large effect – a typhoon in the Pacific . ’

Future Nobel laureate Tom Sargent in a 2010 interview : ‘ It is just wrong to say that this financial crisis caught modern macroeconomists by surprise . ’ 54 ( The non – modern ones were presumably dead , so even less surprised . ) US Federal Reserve report from 2010 : DSGE models ‘ are very poor in forecasting , but so are all other approaches ’ . 55 ( Funny how this was mentioned less before the crisis . 56 )

The Bank of England’s Sujit Kapadia to Queen Elizabeth on her visit in 2012 : ‘ People thought markets were efficient , people thought regulation wasn’t necessary … people didn’t realise just how interconnected the system had become . ’

Nick Macpherson ( aka The Lord Macpherson of Earl’s Court ) , former head of the UK Treasury , in 2016 : ‘ I see myself as one of a number of people … who failed to see the crisis coming , who failed to spot the build – up of risk . This was a monumental collective intellectual error . ’

Christopher Auld from the University of Victoria , in a 2017 Times Higher Education article : ‘ Economists don’t claim to be able to make unconditional forecasts of future states of a system as complex as the macroeconomy ’ , and criticism which relegates the field to ‘ failed “ weather ” forecasting is not just misguided , it is anti – intellectual and dangerous ’ .

A 2017 Prospect article in which six eminent UK economists respond to ‘ dangerous ’ ( but more dangerous than flawed economic models ? ) and ‘ ill – informed expert bashing ’ of their profession from ‘ Writers , students and even some social scientists from other disciplines ’ ( mathematicians , physicists , quants , etc . are not mentioned ) : ‘ Like most economists , we do not try to forecast the date of the next financial crisis , or any other such event . We are not astrologers , nor priests to the market gods . We analyse data . Gigas and gigas of data . ’ ( Since the crisis , economists have distanced themselves from the whole business of macroeconomic forecasting and risk analysis , by emphasising that they apply their theories to many other things as well . * )

Andrew Haldane , chief economist at the Bank of England , in 2017 : ‘ Michael Fish getting up : “ Someone’s called me , there’s no hurricane coming but it will be windy in Spain . ” It is very similar to the sort of reports central banks issued pre – crisis , that there is no hurricane coming but it might be very windy in sub – prime . ’ ( Michael Fish answered that his forecast was ‘ better than the Bank of England’s ’ . 61 )

Paul Krugman , responding directly to ‘ The Queen’s question ’ – and summing up some of the lines in this chapter – as part of the ‘ Rebuilding Macroeconomic Theory Project ’ in 2018 : ‘ My answer may seem unsatisfying , but I believe it to be true : for the most part what happened was a demonstration of the old line that predictions are hard , especially about the future . It’s a complicated world out there , and one’s ability to track potential threats is limited … If you like , it’s as if meteorologists with limited resources concentrated those resources in places that had helped track previous storms , leading to the occasional surprise when a storm comes from an unusual direction … My bottom line is that the failure of nearly all macroeconomists , even of the saltwater [ Krugman’s ] camp , to predict the 2008 crisis was similar in type to the Met Office failure in 1987 , a failure of observation rather than a fundamental failure of concept . Neither the financial crisis nor the Great Recession that followed required a rethinking of basic ideas . ’ ( What then would it take ? )

The penny drops

While , as shown in the box above , many mainstream economists have continued to make excuses for their epic forecasting miss , not all have been so sanguine . George Akerlof and Robert Shiller wrote in their 2015 Phishing for Phools : ‘ It is truly remarkable that so few economists foresaw what would happen . There are about 2 ¼ million article and book listings regarding finance and economics on Google Scholar . That may not indicate enough economist – monkeys to randomly type Hamlet , but it should have been enough to generate quite a few papers that would tell how Countrywide , WaMu , IndyMac , Lehman , and many , many others would in short order flame out and crash . We should have known that their positions in mortgage – backed securities and credit default swaps were fragile . At the time we should have also foreseen the future vulnerabilities of the euro . ’ The authors ascribe this to the ‘ mental frame ’ of economists which sees markets as fundamentally efficient , blames pathologies on externalities , and ignores the fact that ‘ competitive markets by their very nature spawn deception and trickery , as a result of the same profit motives that give us our prosperity ’ . ( An exception was Shiller , who himself pointed out in 2005 that the US housing market was in a bubble . 64 ) Indeed , this points to a basic contradiction in neoclassical models , which assumes on the one hand that people are rational utility – optimisers , and on the other hand that they will always honour contracts – so no systematic fraud from bankers , or ‘ jingle mail ’ from homeowners returning the keys rather than pay their mortgage .

In July 2016 , Narayana Kocherlakota – who like Chari was formerly a president of the Federal Reserve Bank of Minneapolis – noted in a report that ‘ we simply do not have a settled successful theory of the macroeconomy ’ . A couple of months later Paul Romer, who is now chief economist at the World Bank , released a paper called ‘The Trouble With Macroeconomics’ in which he described the area as a ‘pseudoscience’ because the models are packed with implausible assumptions and parameters that are made up to give reasonable – looking answers , and all changes are attributed to external shocks ( Chari responded by saying that ‘ Burning down the edifice , and saying we’ll figure out what we’ll build on its foundations later , just does not seem like a constructive way to proceed ’ , though it would be a start ) .

Finally , some ten years after the crisis began , in a May 2017 speech , Portuguese economist Vítor Constâncio of the European Central Bank told his audience : ‘ In the prevalent macro models , the financial sector was absent , considered to have a remote effect on the real economic activity . In these model frameworks , macroeconomic fluctuations resulted mostly from technological or productivity shocks or from monetary policy unexpected measures . The economy was supposed to be mostly self – correcting and move quickly towards its steady state . No defaults of any agent were possible . Thus , excessive debt could not be a problem . As many wrote , for any debtor there was a creditor and so debt was a non – event at the macro level . This ignored the fact that banks create money by extending credit ex nihilo within the limits of their capital ratio . ’ ( Weather forecaster : with the benefit of hindsight , it may have been a mistake to leave out all the wet stuff . ) One reason perhaps for this omission is that , as discussed in Chapter 5 , the ability of private banks to create money out of nothing was only spelled out by the Bank of England in 2014 .

Constâncio’s comment points to the real issue , which is not that mainstream economists failed to predict ‘ the timing , extent and severity ’ ( as the London School of Economics put it ) of some freak storm – economists have never been held to any such standard of forecasting skill , and no one asked for an exact date . It is that they could not have predicted or warned of the crisis , even in principle . Furthermore , the models directly contributed to the crisis both by creating a false sense of security , and by enabling the financial sector to develop increasingly risky and dangerous products . The efficient market hypothesis did not perform well during the crisis , unless you count creating it . Because the models could not understand the causes of the crisis , they were not useful for suggesting the appropriate policy responses afterwards . And the main reason for this failure is even simpler than leaving out things like deception and trickery ( Akerlof and Shiller ) , or insufficient data / rationality / funding ( Chari ) – it is because the models left out money ( which of course is a main cause of deception and trickery ) . Only by doing so could economists maintain the illusion that independent rational consumers and producers with set supply and demand curves drive the economy to a stable equilibrium through what amounts to barter . Chari mocked the idea that we should ‘ stop using mathematical models of oil pressure ’ in the event of an oil spill , but in economics the problem was that money – what David Hume called the oil of trade , what Jean – Baptiste Say compared to ‘ oil in a machine ’ – wasn’t there . 69 It was therefore impossible to detect that the housing bubble was feeding the money supply , which was feeding the housing bubble , and so on , with complex derivative schemes helping to hide the risk .

By interpreting crises as random shocks , the models also eliminated any sense of responsibility for failing to prevent them . According to Dean Baker ( who presciently warned in a 2005 paper with David Rosnick that for economists to miss the housing bubble would be an ‘ act of extraordinary negligence ’ 70 ) , a rough but conservative estimate would be that it cost each person in the US around $ 27,000 in lost earnings – to say nothing of the human impact in terms of things like mental health and youth unemployment – so ‘ how about a little accountability for economists when they mess up ? ’ 71 Many people lost their jobs , but it seems no economists did . Remarkably , in the US their salaries even ‘ reached a historical high during the recession year of 2009 ’ , which as Kocherlakota noted in 2016 , ‘ might help explain the lack of a paradigm shift in macroeconomic research ’ .

Hush, money

In fact , while the story that no one , apart from a few perma – bears , saw the crisis coming is popular with mainstream economists , the reality is that a good number of people did warn of the crisis – including Baker and Rosnick , William White , Ann Pettifor , Steve Keen , Michael Hudson , Paul Wilmott , Nassim Nicholas Taleb , Nouriel Roubini , Robert Shiller , and traders like those featured in The Big Short , to name some of the better – known ones . They did this – not by constantly predicting disaster , as Krugman claimed , or by random chance – but by focusing on the role of debt , finance , and the banking system . Given , then , that these things were effectively absent from the models used by mainstream economists , the question is why experts would not mention this drawback during testimony ; why central bankers only felt fit to remark on it during conference speeches several years later ; and why , as Joseph Stiglitz noted in 2017 , DSGE models and their underlying assumptions are not just still in use , but ‘ have become a dogma , with little incentive to call them into question especially in a context of peer – reviewed publications ’ . To find the answer , we need to adopt a quantum approach , look at the context , seek out entanglements , and consider the effect of the observer – or in this case , the economist – on the system being studied .

One clue is provided by Robert Lucas , who wrote that ‘ The construction of theoretical models is our way to bring order to the way we think about the world , but the process necessarily involves ignoring some evidence or alternative theories – setting them aside . That can be hard to do – facts are facts – and sometimes my unconscious mind carries out the abstraction for me : I simply fail to see some of the data or some alternative theory . ’  Other people , he says , ‘ will see the blind spot … keep what is good , and correct what is not ’ . This seems a reasonable description of the modelling process ; but in this case the blind spot was very large , and no one corrected it . Or rather , no insider corrected it . Why ?

As mentioned in the previous discussion of quantum cognition , our decisions are shaped by context , including our own mental frames , conscious or unconscious ; and as Akerlof and Shiller note , the mental frame of economists was affected by their view of the economy as a rational , efficient system . One way to think of this is in terms of aesthetics , which , as I have argued elsewhere , plays a surprisingly strong role in science . Science in general has long been dominated by a type of machine aesthetic , where a beautiful model is one that exhibits the classical qualities of symmetry , unity , and stability – at least in its ideas , if not the actual execution . An early example was the Greek model of the cosmos , which saw the heavenly bodies as being encased in beautifully symmetric crystalline spheres ( even if epicycles had to be added to make the model work ) . In economics , we have neoclassical economics , with its perfectly rational and symmetric models ( again until epicycles are added ) . Money – with its complex , multi – faceted , nonlinear , and shapeshifting nature – doesn’t fit in this neat and tidy picture .

As Paul Krugman put it quite accurately in 2009 , ‘ The economics profession went astray because economists , as a group , mistook beauty , clad in impressive – looking mathematics , for truth ’ . ( For this lapse , John Cochrane responded in a journal article by comparing Krugman to ‘ an AIDS – HIV disbeliever , a creationist or a stalwart that maybe continents do not move after all ’ . The Pythagorean good / evil division is very much alive when it comes to the scientific aesthetic – see the box at the end of the next chapter.) But while aesthetics is certainly a powerful influence on the way we see the world, it still doesn’t quite explain why it took so long for the truth about these economic models to leak out through official channels . And this points to a more worldly form of entanglement : that between the economics profession and the financial sector, who were happy to keep money – and therefore themselves – out of the picture . After all, if you were a banker , which narrative would you prefer : that the crisis was caused by banks , or –official version– that it was the result of efficiency and rationality ?

As economist Barry Eichengreen noted in 2009 , in academic economics there is ‘ a subconscious tendency to embrace the arguments of one’s more “ successful ” colleagues in a discipline where money , in this case earned through speaking engagements and consultancies , is the common denominator of success ’ . This issue was highlighted in Charles Ferguson’s documentary The Inside Job , which found that the influence of private money on the profession played a key role in the crisis . As he wrote in 2010 , ‘ These days , if you see a famous economics professor testify in Congress , or write an article , there is a good chance he or she is being paid by someone with a big stake in what’s being debated . Most of the time , these professors do not disclose these conflicts of interest , and most of the time their universities look the other way . ’ A 2012 study in the Cambridge Journal of Economics concluded that ‘ economists almost never reveal their financial associations when they make public pronouncements on issues such as financial regulation ’ and noted that ‘ Perhaps these connections helped explain why few mainstream economists warned about the oncoming financial crisis ’ . In 2016 , philosopher Alan Jay Levinovitz found while researching this topic that ‘ Every economist I interviewed agreed that conflicts of interest were highly problematic for the scientific integrity of their field ’ . That is like a forecaster failing to predict a storm because it would be bad for their show’s sponsors . ( Economists often either seem to be genuinely unaware that there is an ethical component to their work , including things like forecasts , or take it for granted that they are acting correctly .

It is therefore ironic that V.V . Chari also brought up severe underfunding as being a main culprit for his profession’s failure to warn of the crisis . The truth is that DSGE models are a waste of money . That is why quants don’t use them to make predictions . And there is a remarkable symmetry in the fact that financial companies are funding economists whose mental frames are rotated in just such a direction that they have a blind spot for those same financial companies . The question is not necessarily one of explicit corruption – the influence may be as much unconscious as conscious – but more the creation of a certain context and set of beliefs . When Stanford’s Anat Admati sat down with academics and policy – makers to discuss the role of the financial sector in the crisis , she found that ‘ many would not engage . It was very disturbing . I realized that we hit a raw nerve in banking … It felt like I encountered a sort of religion , where people want to believe certain things to be true . ’

This selective blindness is further cultivated by economics textbooks , which shape the opinions not only of students but of future economists , policy – makers , economic journalists , and the public in general . As we have seen , a basic but also poorly understood feature of money is that most of it is created by private banks when they issue loans . Charging interest on these loans is a highly profitable business , which is the main reason banks dominate stockmarkets around the world and their buildings dominate skylines in major cities . Yet as Norbert Häring observed in 2014 , there is a ‘ complete absence in all major textbooks of any mention of the pecuniary benefit ’ , which as he suggests , ‘ points to a taboo imposed by the interest of a very powerful group ’ .

Money is not an externality , it is about as internal as you can get ; and omitting money , credit , and a quadrillion dollars ’ – worth of derivatives from the models is not the same as missing a forecast – it is missing reality .The real reason economists didn’t see the crisis coming was because they ignored its entangled , quantum nature . The message for quantum economics is clear : acknowledge the role of money , both in models and in the profession itself . For as always with quantum systems , context matters ; and far from being detached observers , economists are very much involved .

A new story

According to Chari’s testimony , ‘ A useful aphorism in macroeconomics is : “ If you have an interesting and coherent story to tell , you can tell it in a DSGE model . If you cannot , your story is incoherent ” ’ , which is like a weather forecaster saying that , yes they did miss the storm but they did so in an interesting and coherent way . However , Chari was correct in identifying the need to provide such a story in the first place , because stories are how we frame and give context to information . † As a 2017 note from the UK’s Economic and Social Research Council remarked , while ‘ policy makers have been experimenting in an improvised fashion with new kinds of measures ’ , it still seems that ‘ changes in dominant forms of knowledge have been slow to emerge ’ ( which is why they are bringing in non – economists to take a look ) . The reason many economists couldn’t either face up to their role in the crisis or move forward from it , was that it did not fit with the story that they had been telling themselves and others about how the economy works . So is it possible to come up with another approach – and a story – that incorporates an understanding of ‘ how people make decisions over time and how they handle uncertainty ’ and that deals with ‘ the effects on the whole economy ’ , as Chari put it ?

One of the main appeals of DSGE models to economists is that they were built up from microfoundations based on individual behaviour , which gave them an air of Ricardian rigour . As we have seen , though , these foundations incorporated a simplistic , mechanistic view of the world which bears little relation to the complex entanglements of real human behaviour . And by necessity , the models had to exclude money as a force in itself , because money is irrational , entangling , unstable , and so on . The models therefore tried to simulate the economy without ever addressing its most basic feature , which is transactions involving money .

Of course , one can try to modify DSGE models by adding a simplified financial sector ; and as Chari also mentioned in his testimony , some models do take a stab at researching the effects of what he and other economists call ‘ financial frictions ’ which slow the adjustment to equilibrium ( odd , since money is usually considered a lubricant ) . An early attempt was a model by Bernanke , Gertler and Gilchrist , which accounted for the fact that borrowing costs are inversely related to the borrower’s net worth . However , while this addressed changes in credit allocation , it did not address the issue of credit creation by banks , i.e . the new money produced by making loans , and its inclusion had only a ‘ modest quantitative effect on the way the model economy responds to shocks ’ , as one paper put it . As noted in a 2015 Bank of England article , models which do include money creation run into the problem that ‘ banks that create purchasing power can technically do so instantaneously and discontinuously , because the process does not involve physical goods , but rather the creation of money through the simultaneous expansion of both sides of banks ’ balance sheets ’ . In other words – as is also the case with events such as credit default or bankruptcy – it is an on / off process , not a smooth mechanical one , and as easy to model using conventional tools as a lightning strike .

DSGE models were designed from the outset to see changes as smooth and continuous rather than abrupt and quantum – nature makes no sudden leaps , as Alfred Marshall’s epitaph insisted – and the complexity of financial entanglements means those assumptions of continuity and stability no longer apply . The ‘ friction ’ analogy , for example , is a reference to the idea from physics that Newtonian laws of motion , as applied to something like the arc of a projectile , give somewhat different answers when friction is taken into account . Models can therefore be improved in an iterative fashion , first by solving for the frictionless case , and then adding friction if more accuracy is required . But finance acting ‘ instantaneously and discontinuously ’ is a completely different thing ( unless you count accelerating into a brick wall as friction ) .Physicists couldn’t address quantum effects by adding a few bells and whistles to Newtonian models , and the same is true in economics .

While we therefore need to incorporate money and debt into our model of the economy , it isn’t enough to simply bolt a neutered model of the financial sector onto existing models while at the same time assuming the system is stable ( economists have long been adept at defusing criticism by going through the motions in this way ) . 93 Instead the models have to start with the fact that money transactions are at the heart of everything , so assumptions about things like rationality , fixed preferences , utility – maximising behaviour , equilibrium and so on are no longer tenable , even as a first approximation . The most obvious feature of the crisis was that nothing was fixed and there was no equilibrium – and at times where there is a sense of equilibrium , it often says more about institutional effects , or social or political forces , than it does about markets themselves . It was the government that rescued the markets through a massive bailout ( in the US , the Emergency Economic Stabilization Act of 2008 ) , not the other way round .

In recent years modellers have also attempted to make models more fine – grained by doing things like increasing the number of representative agents . However , a problem with reductionist models of any sort , as I noted in my 2007 book Apollo’s Arrow on the science of prediction , is that as they are made more detailed , the number of unknown parameters whose values cannot be accurately inferred from the data tends to explode . 94 This is one reason why , paradoxically , simple models that are based on sound forecasting principles often outperform complicated models at making predictions . 95 Joseph Stiglitz similarly noted in 2017 the ‘ Ptolemaic attempt ’ of modellers to add more and more features until models become ‘ little more than an exercise in curve fitting ’ .  Adding a financial sector to DSGE models is particularly challenging because of the nonlinearities that it introduces .

Viewed this way , the predictive uncertainty that we confront in economics is not so different from the uncertainty that is taken for granted in other fields where living things are involved , such as medicine or politics . Perhaps the problem is that because money is based on number , we have become used to the idea that the economy is a kind of predictable , mechanical system , rather than something with a life of its own . At his Nobel speech in 1933 , the physicist Paul Dirac said that ‘ There is in my opinion a great similarity between the problems provided by the mysterious behaviour of the atom and those provided by the present economic paradoxes confronting the world . In both cases one is given a great many facts which are expressible with numbers , and one has to find the underlying principles . ’  But as we’ve seen , while there are certainly similarities between physics and economics, the numbers themselves only give one side of the story .

Fortunately , there are many mathematical tools available to help with this , and they have been around for a while . But for these tools to succeed , economists first need to stop wasting energy by confabulating excuses and further complicating their models ; and instead rotate their mental frames from seeing the economy as a mechanical system , to seeing it as a complex quantum system with a life and an agency of its own .

Negative entropy

In his 1944 book What is Life ? the quantum physicist Erwin Schrödinger proposed that organisms stay alive by feeding on ‘ negative entropy ’ .  Like Maxwell’s demon , we are information workers , extracting energy from order ( low entropy ) in our environment by manipulating it . Schrödinger didn’t know how this information was coded , or where it was kept , but he believed that any such process would take place on the border between the quantum and the classical world . His thoughts later inspired Francis Crick and James Watson when they worked out how genetic information was stored in DNA . Instead of allowing information to leak out into our environment , we package it tightly , and pass it on to our descendants . Equilibrium is death .

However , genes are not our only information storage devices – we also have language , social institutions , and of course the financial system . Viewed this way , money takes on the aspect of a kind of active biological molecule , whose quantum interactions are at the heart of the economy . Instead of modelling its flows using linear techniques designed for the analysis of Victorian steam engines , we should use ones that are better adapted for the analysis of living systems , such as network theory , complexity theory , and the nonlinear dynamics discussed in Chapter 6 . ( Though one of the first applications of the latter , by James Clerk Maxwell , was to Victorian steam engines , which had nonlinear instabilities of their own . )

One of the main findings from the Human Genome Project was that the function of genes and proteins can usually only be understood when they are considered as part of a connected network . The analysis of such networks has therefore become a useful tool for finding which genes and associated proteins are involved in pathologies such as cancer . Similar techniques have been used for analysing the power structure of the economy , as expressed by share ownership , or cross – border flows of the sort discussed above ; and for understanding the properties which make a system fragile or robust to perturbations .

The related area of complexity theory concerns systems that are characterised by emergent properties , which even in principle cannot be predicted from a knowledge of the system’s parts alone , and therefore resist a reductionist approach . In economics , as discussed in Chapter 4 , an active area of complexity research involves agent – based models , which model individuals or firms as separate agents which are then allowed to interact with one another through simulated transactions . These models work in discrete time steps so can easily handle things like instantaneous money creation or loan defaults , and as mentioned in Chapter 7 , they can in principle at least be extended to include quantum decision – making .

A first step , though , in predicting a system is to properly see it , and another use for complexity theory is to visualise and understand data . Part of the Harvard Atlas of Economic Complexity project was to come up with complexity – based metrics that could be used to quantify the availability of productive knowledge embedded in an economy . The main two metrics were ubiquity and diversity . The former is the number of countries that make a particular product , so is like a measure of entropy ( the more ubiquitous a product , the less special and ordered it is ) , while diversity is the number of products that a country makes . A third metric , proximity , describes the similarity between products . The researchers found that ubiquity and diversity gave insights into an economy’s overall complexity , while the proximity of a product to others helped to explain a country’s likelihood of developing it . In Ricardo’s imagined two – country , two – product example , the diversity would be low , ubiquity would be high , and proximity would be near – zero ( a cloth – maker is unlikely to adapt easily to wine – making ) .

A related approach , from a team led by physicist Matthieu Cristelli , uses a metric they call ‘ fitness ’ which sums the value of the exports , weighted for complexity , and serves as a measure of the ‘ information content ’ embedded in economic systems . 102 They then plot how fitness and GDP evolve over time for different countries . The study found that ‘ country dynamics presents strongly heterogeneous patterns of evolution ’ . Developing countries with a low level of fitness show chaotic dynamics , with little discernible pattern ; however , once a country escapes the turbulent low – fitness regime , it enters a smoother regime where its path can be predicted by comparing it with the progress of other countries . For example , if GDP is relatively low for a given fitness , then it can be expected to increase . This type of regime analysis was pioneered in the study of chaotic nonlinear systems . Economists cannot rely on the simplifying tools of traditional analysis , such as looking at aggregate levels of education or investment , but instead ‘ must face issues which are very close to the problems of predictability for dynamical systems ( i.e . atmosphere , climate , wind , ocean dynamics , and weather forecast , etc ) ’ .

The use of such techniques has been accompanied by increasingly sophisticated tools for data analysis . Andrew Haldane of the Bank of England has even called for a ‘ global financial surveillance system ’ that would involve real – time tracking of global funds ‘ in much the same way as happens with global weather systems and global internet traffic . Its centre piece would be a global map of financial flows , charting spill – overs and correlations . ’ 103 One complication is that money has a propensity to hide . About 10 per cent of the world’s wealth is estimated to be held in offshore tax havens , according to the National Bureau of Economic Research , mostly by the ultra – rich . 104 And while foreign direct investment ‘ is generally assumed to represent long – term investments within the “ real ” economy ’ , as economic geographers Daniel Haberly and Dariusz Wójcik note , ‘ approximately 30 – 50 % of global FDI is accounted for by networks of offshore shell companies created by corporations and wealthy individuals for tax and other purposes ’ .

The crossroads

As mentioned in Chapter 1 , the fact that a system is based at some level on quantum principles does not imply that it can or should be modelled using quantum methods – indeed , one of the main lessons of quantum physics is that most properties we observe at larger scales cannot be reduced to quantum principles , because they are emergent phenomena ( in which the whole might be more than the sum of its parts ) . Quantum physics at small scales has yet to be reconciled with relativity theory at large scales , but they each work in their own domain . The main contribution of quantum economics is to draw attention to the role of money – which by its entangled and context – sensitive nature leads inevitably to a quantum approach . By omitting it , and modelling the economy as a stable and self – correcting system , economists helped create an economy that was the exact opposite .

At the same time , the economy differs from something like the weather in that it is not just a complex system , it is a complex quantum system , where some quantum properties do manifest themselves at larger scales . As discussed further in the next chapter , the fact that agents are entangled means that they can show collective dynamics and modes of behaviour that are unique to quantum systems . And while standard complexity models can cope with properties that emerge from agents , in a quantum system the collective behaviour changes the agents in turn . In recent years a number of research centres have been set up to study the emergent dynamics of complex quantum systems in physics – quantum thermodynamics is one example of this approach – and some of those techniques may prove useful in economics .

‘ Ultimately ’ , according to former chief economist for the Bank for International Settlements Stephen Cecchetti , ‘ an economic model can only be defeated by an opposing model ’ . 106 But the mainstream economic model has been defeated by something else – quantum reality . If we take the quantum emergent properties ( i.e . those which emerge from quantum transactions , but cannot be reduced to them ) of the world economy seriously , there is no reason to think that we should be able to perfectly simulate it using any set of equations . People who predicted the crisis did so by paying attention to what was important , not by building a more elaborate model . So rather than develop a single unified model of ‘ the effects on the whole economy ’ as V.V . Chari demanded , it makes more sense to adopt an agile approach where models are seen as patches to fit particular problems . 107 And instead of group – think , with everyone – or more precisely everyone with power and influence – agreeing on the correct model , no matter its flaws , we would be better served by a diversity of techniques . The same is true in physics : as physicists Nigel Goldenfeld and Leo P . Kadanoff wrote in 1999 , ‘ Up to now , physicists looked for fundamental laws true for all times and all places . But each complex system is different ; apparently there are not general laws for complexity . Instead , one must reach for “ lessons ” that might , with insight and understanding , be learned in one system and applied to another . Maybe physics studies will become more like human experience . ’ 108 A quantum macroeconomics approach would incorporate a dashboard of techniques , including complexity – based data mining , machine learning , agent – based simulations ( quantum or otherwise ) , nonlinear dynamics models , and model – free analysis ; each a different way of exploring the shapeshifting quantum nature of the economy , and looking for signs of storms . ( An analogy would be from my own work in predicting the effects of anti – cancer drugs , where we use a hybrid agent – based / nonlinear – dynamics model of a growing tumour in concert with empirical data – driven approaches and literature research .

One drawback of such a pluralistic approach is that the different tools might not all say the same thing . ‘ The danger in encouraging plurality ’ , explained Oxford economist Simon Wren – Lewis in 2018 , ‘ is that you make it much easier for politicians to select the advice they like , because there is almost certain to be a school of thought that gives the “ right ” answers from the politicians ’ point of view . The point is obvious once you make the comparison to medicine . Don’t like the idea of vaccination ? Pick an expert from the anti – vaccination medical school . ’ 110 This is an interesting ( though not unusual – see box in next chapter ) choice of analogy , given that mainstream economists were the ones who saw no need to vaccinate the financial system against crisis . Surely a bigger issue than plurality is rebuilding credibility with the public ; a 2017 UK survey by YouGov asking which experts could be trusted when talking about their own areas of expertise showed that doctors were trusted by 82 per cent , weather forecasters by 51 per cent , and economists by 25 per cent . 111 But it also points to the fact that mainstream economics , or at least the part of it with influence over policy , remains too much of a monoculture with little real interest in reinventing itself , despite numerous well – publicised initiatives to do just that . It seems that economists ’ interest in the benefits of competition and new ideas breaks down rather quickly when it comes to their own field .

One 2017 survey of recent progress , written by leading DSGE modellers Lawrence J . Christiano , Martin S . Eichenbaum , and Mathias Trabandt , began by doubling down on Chari’s ‘ only game in town ’ argument : ‘ People who don’t like dynamic stochastic general equilibrium ( DSGE ) models are dilettantes . By this we mean they aren’t serious about policy analysis . ’ 112 Explaining that significant progress has been made in ‘ incorporating financial frictions and heterogeneity into DSGE models ’ , they conclude : ‘ We do know that DSGE models will remain central to how macroeconomists think about aggregate phenomena and policy . There is simply no credible alternative to policy analysis in a world of competing economic forces . ’

In a Bloomberg article called ‘ Fixing macroeconomics will be really hard ’ , the economist Noah Smith reported on a cutting – edge symposium on rethinking macroeconomics , held by the Peterson Institute for International Economics in October 2017 , and featuring luminaries such as Ben Bernanke and Larry Summers . ‘ In the past few years ’ , writes Smith , ‘ macroeconomists have been scrambling to shoehorn the financial sector into their standard models . Of course , there’s always the danger that the Great Recession prompts macroeconomists to focus too much on finance . ’ 113 However , the ‘ real sea change ’ is in the approach to recessions : ‘ Most modern econ theories posit that recessions arrive randomly , instead of as the result of pressures that build up over time . And they assume that recessions are short – lived affairs that go away of their own accord . If these assumptions are wrong , then most of the theories written down in macroeconomic journals over the past several decades – and most of those being written as we speak – are of questionable usefulness . ’ According to Smith , ‘ economists have known for decades that recessions might not be random , short – lived events , but the idea always remained on the fringes . One big reason was mathematical convenience – models where recessions are like rainstorms , arriving and departing on their own , are mathematically a lot easier to work with . ’ Another complication is that the economy is ‘ almost certainly a chaotic system . Researchers have known for decades that unstable economies are very hard to work with or predict . In the past , economists have simply ignored this unsettling possibility and chosen to focus on models with only one possible long – term outcome . ’ On the bright side , better microeconomic data will result in more accurate microfoundations , and therefore ‘ more realistic models ’ .

This sounds like weather forecasters announcing at a conference that they have agreed to stop predicting rainstorms that come ‘ on their own ’ by tossing a coin . While the emphasis on data , and the recognition of instability , is encouraging , it sounds like we can expect the next DSGE models to be ‘ complex and difficult ’ creations with a ‘ shoehorned ’ financial sector , ‘ harder math ’ ( or ‘ mathiness ’ as Paul Romer calls it ) , and extra new chaotic behaviour ( which will at least make a good excuse for forecast error ) . 114 In other words , a continuation of the same mechanistic weather forecasting approach , though less informed by its sister field’s long history of empiricism , honed by daily comparisons of predictions and reality ; and less so still by other areas such as biology .

A 2018 paper summarising results from the Oxford Review of Economic Policy’s ‘ Rebuilding Macroeconomic Theory ’ project admitted that ‘ The benchmark model … was not good enough to give any warning of the emergence of crisis in 2008 . And it has been of very little help in understanding what to do next . Notwithstanding these failings , there is not yet a new paradigm in sight . ’ Instead , the basic DSGE model should be updated by : ‘ ( i ) incorporating financial frictions rather than assuming that financial intermediation is costless ; ( ii ) relaxing the requirement of rational expectations ; ( iii ) introducing heterogeneous agents ; and ( iv ) underpinning the model – and each of these three new additions – with more appropriate microfoundations . ’ 115 On the last point , the authors add : ‘ It seems there is an analogy in the natural sciences , chemistry has been “ microfounded ” through the use of explanations from quantum physics ’ , which will come as news to chemists – so much for emergent properties . 116 ( This remark , taken literally , would seem to imply that the only logical way to proceed would be to microfound a model of the global economy based on the behaviour of quantum agents . Unfortunately such faith in the power of reductionism is a hallmark of neoclassical economics , not the quantum version . )
Meanwhile the physicist and hedge fund manager Jean – Philippe Bouchaud , who , as mentioned in the Introduction , called for a ‘ scientific revolution ’ for economics back in 2008 , told the Financial Times a decade on that : ‘ Following the financial crisis many of us hoped that the economics profession had finally realised that their models were not representative of how the real economy works and that their flawed methods would quickly change . That assumption was wrong … no radical change has been made to the workhorse models used by central bankers , which assume that the economy can be represented by a single agent with perfect access to information and infinite foresight . This is a wild oversimplification of a highly complex , interacting system where feedback loops can trigger crises . ’ 117 He concludes that : ‘ If we don’t embrace new methods of modelling the economy [ such as agent – based models ] we will be as blind to the next crisis as we were to the last one . ’

The most obvious difference between the weather and the economy , from a forecasting perspective , is that we create , and have some direct control over , the latter . Recessions are not random storms that come out of nowhere , as economists like to portray them , but are things that we take part in and can take steps to actively prevent . Economists are also entangled financially with the system they are studying . Viewed this way , it is true that it is not completely fair to compare economics with weather forecasting . Economists ’ responsibility is far greater , and is more like that of engineers or doctors – instead of predicting exactly when the system will crash , they should warn of risks , incorporate design features to help avoid failure , know how to address problems when they occur , and be alert for conflicts of interest , ethical violations , and other forms of professional negligence . Its failings in these areas , rather than any particular forecast , are the real reason so many are calling for a genuinely new paradigm in economics , as opposed to a rehashed version of the old one . And the danger is not pluralism ( doctors don’t always agree either ) , but a monoculture based on flawed ideas . Macroeconomic forecasting might be a relatively small part of economics , but its missed predictions and mis – analysis , with their dramatic real – world consequences , are just the most visible and concerning symptom of a deeper problem which starts with the basic assumptions , and affects other branches of mainstream economics . Instead of finding new applications for their theories , and confusing this with genuine broadness and diversity , economists should focus on doing the important things right .

The transition in thinking required today in economics , while not technically difficult , seems as much a challenge to orthodoxy as the one a century earlier when classical physics collided with quantum reality . Perhaps I am biased , but Wolfgang Munchau  may have been right when he wrote in the Financial Times in 2015 that ‘ The mainstream invested a life’s work in developing their DSGE models . They will not let go easily , but continue to tinker with their models … the successful challenge will come from outside the discipline . ’ 119 Sometimes , entanglements can hold back progress – but at least these models are no longer the only game in town . In the next chapter , we consider our entanglement with a larger organic system that is of interest to economists and weather forecasters alike – the planet.

David Orrell’s  Quantum Economics.

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