The Future of Everything

October 31, 2017

Why economists can’t predict the future

Filed under: Economics, Forecasting — David @ 12:11 pm

NewsweekJapanCover

Cover article in Newsweek Japan on why economists can’t predict the future. Read an extract in Japanese here.

 

Original English version:

 

The quantum physicist Niels Bohr is attributed with the saying that “prediction is hard, especially about the future.” Still, economists seem to have more trouble than most.

For example, mainstream economists uniformly failed to predict the global financial crisis that began in 2007. In fact, that was the case even during the crisis: 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).[1] That is like a weather forecaster saying the storm that is raging outside their window isn’t actually a storm.

In 2014, Haruhiko Kuroda, Governor of the Bank of Japan, predicted that inflation should “reach around the price stability target of 2 percent toward the end of fiscal 2014 through fiscal 2015.”[2] It apparently didn’t get the memo, preferring to remain well under one percent.[3] In Britain, economists confidently predicted that Brexit would cause an immediate economic disaster, which similarly failed to materialise.

This forecasting miss prompted the Bank of England’s Andrew Haldane to call for economics to become more like modern weather forecasting, which has a somewhat better track record at prognostication.[4] So can economists learn from weather forecasters – or is predicting the economy even harder than predicting the weather?

In many respects the comparison with meteorology seems apt, as the two fields have much in common. “Like weather forecasters,” said former Chairman of the US Federal Reserve Ben Bernanke in 2009, “economic forecasters must deal with a system that is extraordinarily complex … and about which our data and understanding will always be imperfect.”[5] The two fields also take a similar mechanistic approach to making predictions – with a few important differences.

Weather models work by dividing the atmosphere up into a 3D grid, and applying Newtonian laws of motion to track its flow. The mathematical models are complicated by things like the formation and dissipation of clouds, which are complex phenomena that can only be approximated by equations. The fact that clouds, and water vapour in general, are one of the most important features of the weather is the main reason weather prediction is so difficult (not the butterfly effect).[6]

Economic models similarly divide the economy into groups or sectors that are modelled with representative consumers and producers, whose homogeneous behaviour is simulated using economic “laws” such as supply and demand. However, unlike the weather which obviously moves around, these “laws” are assumed to drive prices to a stable equilibrium – despite the fact that the word “equilibrium” is hardly what comes to mind when discussing financial storms.

Furthermore, the economy is viewed as a giant barter system, so things like money and debt play no major role – but the global financial crisis was driven by exactly these things. One reason central banks couldn’t predict the 2007 banking crisis was because their model didn’t include banks. And when models do incorporate the effects of money, it is only in the form of “financial frictions” which as the name suggests are minor tweaks that do little to affect the results, and fail to properly reflect the entangled nature of the highly-connected global financial system, where a crisis in one area can propagate instantly across the world.

Predicting the economy using these tools is therefore rather like trying to predict the weather while leaving out water. This omission will seem bizarre to most non-economists, but it makes more sense when we take the subject’s history into account.

Adam Smith, who is usually considered the founding father of economics, assumed that the “invisible hand” of the markets would drive prices of goods or services to reflect their “real intrinsic value” so money was just a distraction.[7] As John Stuart Mill wrote in his 1848 Principles of Political Economy, “There cannot, in short, be intrinsically a more insignificant thing, in the economy of society, than money.”[8] According to Paul Samuelson’s “bible” textbook Economics, “if we strip exchange down to its barest essentials and peel off the obscuring layer of money, we find that trade between individuals and nations largely boils down to barter.”[9]

In the 1950s, economists showed – in what is sometimes called the “invisible hand theorem” – that such a barter economy would reach an optimal equilibrium, subject of course to numerous conditions. In the 1960s, efficient market theory argued that financial markets were instantaneously self-correcting equilibrium systems. The theory was used to develop methods for pricing options (contracts to buy or sell assets at a fixed price in the future) which led to an explosion in the use of these and other financial derivatives.

Today, economists use so-called macroeconomic models, which are the equivalent of weather models, to compute the global economic weather, while continuing to ignore or downplay money, debt, and financial derivatives. Given that the quantitative finance expert Paul Wilmott estimated the notional value of all the financial derivatives in 2010 at $1.2 quadrillion (so $1,200,000,000,000,000) this seems a bit of an oversight – especially since it was exactly these derivatives which were at the heart of the crisis (see our book The Money Formula).[10]

Now again, it may seem strange that economists think they can reliably model the whole economy while leaving out such a large amount of it – but it gets stranger. Because according to theory, not only is money not important, but much of it shouldn’t even exist.

Perhaps the most basic thing about money in a modern capitalist economy is that nearly all of it is produced by private banks, when they make loans. For example, when a bank gives you a mortgage, it doesn’t scrape the money together from deposits – it just makes up brand new funds, which get added to the money supply. But you wouldn’t know this from a training in mainstream economics, which treats the financial sector as little more than an intermediary; or until recently from central banks.

According to economist Richard Werner – who first came up with the idea of quantitative easing for Japan in the 1990s – “The topic of bank credit creation has been a virtual taboo for the thousands of researchers of the world’s central banks during the past half century.”[11] The first to break this taboo was the Bank of England, which created a considerable stir in the financial press in 2014 when it explained that most of the money in circulation – some 97% in the UK – is created by private banks in this way.[12] In 2017 the German Bundesbank agreed that “this refutes a popular misconception that banks act simply as intermediaries at the time of lending – ie that banks can only grant credit using funds placed with them previously as deposits by other customers.”[13]

This money creation process is highly dynamic, because it tends to ramp up during boom times and collapse during recessions, and works “instantaneously and discontinuously” as a Bank of England paper notes (their emphasis), which makes it difficult to incorporate in models.[14] The money thus created often goes into real estate or other speculative investments, so may not show up as inflation. And as Vítor Constâncio of the European Central Bank told his audience in a 2017 speech, its omission helped explain why economists failed to predict the crisis: “In the prevalent macro models, the financial sector was absent, considered to have a remote effect on the real economic activity … This ignored the fact that banks create money by extending credit ex nihilo within the limits of their capital ratio.”[15]

So to summarise, ten years after the crisis, central banks are finally admitting that the reason they didn’t predict it was because their models did not include how money is created or used. This is like a weather forecaster admitting a decade after the storm of the century that they couldn’t have predicted it, even in principle, because they had left out all the wet stuff.

Central bankers are also increasingly admitting that they have no satisfactory model of inflation – but that is obvious, because they have no satisfactory model of money.[16] Their policy of near-zero interest rates has created, not the expected inflation, but only asset bubbles and a destabilising global explosion in private sector debt.

How could we have reached this point? One reason, paradoxically, is that economists are all too familiar with the financial sector (who are happy to be kept out of the picture), not through their models but through consulting gigs and other perks, though they tend to be less than up-front about this. A 2012 study in the Cambridge Journal of Economics observed that, “economists almost never reveal their financial associations when they make public pronouncements on issues such as financial regulation.”[17] It also noted that “Perhaps these connections helped explain why few mainstream economists warned about the oncoming financial crisis.” This is like weather forecasters failing to include water or predict a storm because doing so would upset their sponsors.

Another reason, though, is that it is not possible to simply bolt a financial sector onto existing mainstream models, because as discussed above these are based on a mechanistic paradigm which – in part for ideological reasons – assumes that the actions of independent rational agents drive prices to a stable and optimal equilibrium.[18] Money however has remarkable properties which make it fundamentally incompatible with assumptions such as rationality, stability, efficiency, or indeed the entire mechanistic approach.

As we have seen, the creation or transfer of money is not a smooth or continuous process but takes place “instantaneously and discontinuously” which is as easy to model as a lightning strike. Money and debt act as entangling devices by linking debtors and creditors – and derivatives act as a kind of super-entanglement of the global financial system – which means that we cannot treat the system as made up of independent individuals.

Money is fundamentally dualistic in the sense that it combines the real properties of an owned object, with the virtual properties of number, which is why it can take the form of solid things such as coins, or of virtual money transfers as when you tap your card at a store. These dualistic properties, combining ownership and calculation, are what make it such a psychologically active substance. And prices in the economy are fundamentally indeterminate until measured (you don’t know exactly how much your house is worth until you sell it).[19]

To summarise, money is created and transmitted in discrete parcels, it entangles its users, it is dualistic, and prices are indeterminate. Haven’t we seen this before?

Niel Bohr’s speciality of quantum physics was initially inspired by the observation that at the quantum level matter and energy move not in a continuous fashion, but in discrete leaps and jumps. Pairs of quantum particles can become entangled, so they become part of a unified system, and a measurement on one instantaneously affects its entangled twin – an effect Einstein described as “spooky action at a distance.” Bohr’s “principle of complementarity” says that entities such as electrons behave sometimes like “real” particles, and sometimes like virtual waves. And Heisenberg’s uncertainty principle says that quantitites such as location are fundamentally indeterminate.

Bohr’s contemporary, the English economist John Maynard Keynes wrote in 1926, “We are faced at every turn with the problems of Organic Unity, of Discreteness, of Discontinuity – the whole is not equal to the sum of the parts, comparisons of quantity fails us, small changes produce large effects, the assumptions of a uniform and homogeneous continuum are not satisfied.”[20] He was speaking about the economy, but he was inspired also by the developments in physics – he met Einstein, and the title of his General Theory of Employment, Interest and Money was inspired by Einstein’s General Theory of Relativity.

Which leads one to think: if a century ago economics had decided to incorporate some insights from quantum physics instead of aping mechanistic weather models, the economy today might be rather better run.

Or if not, at least we would have a perfect excuse for forecast error: predicting the economy isn’t just harder than predicting the weather, it’s harder than quantum physics.

References

[1] Ahir, H., & Loungani, P. (2014, March). Can economists forecast recessions? Some evidence from the Great Recession. Retrieved from Oracle: forecasters.org/wp/wp-content/uploads/PLoungani_OracleMar2014.pdf.

[2] https://www.boj.or.jp/en/announcements/press/koen_2014/data/ko140320a1.pdf

[3] https://www.reuters.com/article/us-japan-economy-boj-kuroda/bojs-kuroda-still-far-to-go-to-reach-2-percent-inflation-target-idUSKBN18Z2VQ?il=0

[4] Inman, P. (2017, January 5). Chief economist of Bank of England admits errors in Brexit forecasting. The Guardian.

[5] Bernanke, B. (2009, May 22). Commencement address at the Boston College School of Law. Newton, Massachusetts.

[6] Orrell, D. (2007). Apollo’s Arrow: The Science of Prediction and the Future of Everything. Toronto: HarperCollins.

[7] Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. London: W. Strahan & T. Cadell.

[8] Mill, J. S. (1848). Principles of Political Economy. London: Parker.

[9] Samuelson, P. A. (1973). Economics (9th ed.). New York: McGraw-Hill, p. 55.

[10] Wilmott, P., & Orrell, D. (2017). The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets. Chichester: Wiley.

[11] Werner, R. A. (2016). A lost century in economics: Three theories of banking and the conclusive evidence. International Review of Financial Analysis, 46, 361-379.

[12] McLeay, M., Radia, A., & Thomas, R. (2014, March 14). Money Creation in the Modern Economy. Quarterly Bulletin 2014 Q1. Bank of England.

[13] Deutsche Bundesbank. (2017). How money is created. Retrieved from https://www.bundesbank.de/Redaktion/EN/Topics/2017/2017_04_25_how_money_is_created.html.

[14] Jakab, Z., & Kumhof, M. (2015). Banks are not intermediaries of loanable funds – and why this matters. Bank of England working papers(529), 1.

[15] Constâncio, V. (2017, May 11). Speech at the second ECB Macroprudential Policy and Research Conference, Frankfurt am Main. Retrieved from European Central Bank: https://www.ecb.europa.eu/press/key/date/2017/html/ecb.sp170511.en.html.

[16] Fleming, S. (2017, October 4). Fed has no reliable theory of inflation, says Tarullo. Financial Times. Giles, C. (2017, October 11). Central bankers face a crisis of confidence as models fail . Financial Times.

[17] Carrick-Hagenbarth, J., & Epstein, G. A. (2012). Dangerous interconnectedness: economists’ conflicts of interest, ideology and financial crisis. Cambridge Journal of Economics, 36(1), 43–63.

[18] Orrell, D. (2017). Economyths: 11 Ways That Economics Gets it Wrong. London: Icon Books.

[19] Orrell, D. (2016). A quantum theory of money and value. Economic Thought, 5(2), 19-36; Orrell, D., & Chlupatý, R. (2016). The Evolution of Money. New York: Columbia University Press.

[20] Keynes, 1926.

 

 

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February 14, 2018

Of minds and money

Filed under: Economics — Tags: — David @ 3:04 pm

Post written for the Rebuilding Macroeconomics blog of the UK’s Economic and Social Research Council (ESRC), 23 February 2018

In 2017, when the ESRC announced that it was setting up a network of experts from different disciplines to “revolutionise” the field of economics, I thought it sounded like a very good idea. But in the spirit of that disruptive mandate, what if the revolution has already happened – and economics just needs to recognise it? I am talking about the quantum revolution which overturned physics at the start of the last century.

Of course, the thought that economics has anything to do with quantum physics will sound to many economists (or physicists) like a particularly severe case of physics envy. But my point is not that economics can be reduced to quantum physics (in my book Economyths I argue against such reductionist approaches). Nor is to assert that we can use quantum physics as a fuzzy metaphor to understand the economy. Instead, it is to say that the economy is a complex quantum system in its own right and should be treated as such. This recognition has the potential to disrupt all the pillars of mainstream economics, including its models of human behaviour, markets, and the macroeconomy.

To see why (and starting with the obvious), recall that quantum physics grew out of the empirical discovery by scientists that energy is transmitted, not continuously, but in discrete chunks they called quanta, from the Latin for “how much”. This violated the basic principle that Natura non facit saltum (nature makes no sudden leaps), as the epitaph of Alfred Marshall’s Principles of Economics read at the time (and until the final 1920 edition). But of course the same is true of money. When you pay for an item with a card, the money doesn’t drain out in a continuous flow, it goes as a discrete transmission.

Quantum objects can magically appear out of the void, and disappear back into it. In the economy, we have the creation of money by private banks issuing loans, and its destruction when loans are repaid. Again, these are discrete processes that take place “instantaneously and discontinuously” as a 2015 Bank of England working paper observed, so don’t fit easily with the continuous nature of conventional models – one reason they are typically excluded.

Another discovery of quantum theory is that matter has two complementary aspects, a “real” particle aspect and a “virtual” wave aspect. Money too combines the virtual properties of a number, with the real properties of an owned object – a bitcoin seems pretty virtual, unless you lose the computer it’s on – and it is this dualistic nature which gives money its confounding properties.

In quantum mechanics, a particle doesn’t have a well-defined location or momentum. In the economy, prices of something like a stock, or a house, are similarly indeterminate, and there is no unique intrinsic value (or eigenvalue in the quantum jargon). This is why some traders, along with researchers in the field of quantum finance, model the markets as a quantum system, with its own version of an uncertainty principle.

One of the more mysterious features of quantum reality is that subatomic particles can become entangled so that a measurement on one instantaneously affects the state of the other. Suppose I take a mortgage out with a bank. If I choose to default, then from that moment on the state of the loan has changed. Of course it will take time for the bank to learn what happened and settle the matter, but it takes time to measure particles too, during which other effects may come into play. It is a financial version of the social entanglement studied in quantum social science (see for example the 2015 book Quantum Mind and Social Science by political scientist Alexander Wendt).

Finally, another key property of quantum systems is interference: as in the double-slit experiment, where shining a light beam through two slits produces an interference pattern on the other side, even if only one photon passes at a time. Of course, money objects don’t interfere with each other, which is good because otherwise they might cancel out in your pocket. But the purpose of money is to put a number on the idea of value, and dissonance between these two things – objective number and subjective value – does produce interference patterns in our minds, of the sort modelled by researchers working in the area of quantum cognition.

In fact, instead of following classical logic when making decisions, it turns out that we are better described as following a version of quantum logic. As physicists Vyacheslav Yukalov and Didier Sornette note, “It is the appearance of interference terms that makes the structure of quantum expressions richer than the related classical ones and that allows one to explain those psychological phenomena that, otherwise, are inexplicable in classical decision making.”

As an example of a quantum economics phenomenon, suppose that you come under financial stress and are deciding whether to pay the latest installment on your mortgage, or default. Your mental state – and that of the loan – can then be treated, following the methods of quantum cognition, as being in an indeterminate superposition of two states. The emergent consequences in the entangled economy can be modelled at a societal level using complexity methods, including quantum agent-based models, though as always the system eludes reduction or exact computation.

Until now, money has played no more role in the quantum social sciences than in neoclassical economics, where it has traditionally been treated as an inert medium of exchange. But as seen above, quantum ideas are perfectly suited to things like the creation of money, entanglement through loans and other contracts, and credit default – all of which were at the heart of the 2007/8 crisis, but nowhere in the macroeconomic models.

As I argue in a discussion paper and in more detail in a forthcoming book, the quantum approach has implications not just for finance, or macroeconomic modelling, but for all the basic assumptions and conclusions of mainstream economics; and points the way to a new economics which is to its neoclassical version what quantum physics was to its classical version. Economics isn’t physics, but can learn from it; and if the intention is to rebuild macroeconomics, the natural place to start is with the quantum, dualistic nature of both mind and money.

January 12, 2018

Aeon piece on quantum economics – responses

Filed under: Economics, Physics — David @ 12:32 pm

Thanks to readers for taking the time to comment (via Twitter) on my recent Aeon piece Economics is quantum. Since it is hard to reply in strings of 140 characters, this post addresses some of the questions and criticisms that came up (edited for clarity).

After physicist Richard Jones wrote that the piece was “confused & misleading” (not a completely unusual reaction, I must admit) I asked him to clarify which parts of the text he was referring to. 

Jones: “Why do you think the brain is a quantum phenomenon? Direct involvement of consciousness in wave-function collapse is not mainstream view, & I’m very sceptical of any role of coherence in mental phenomenon.”

What I wrote was: “a number of scientists believe that the problem is not so much that people are being irrational; it is just that they are basing their decisions, not on classical logic, but on quantum logic. After all, quantum systems, such as us, are intrinsically uncertain and affected by history and context.” [As an example, see: Wang, Z., Solloway, T., S., M., R., & Busemeyer, J. R. (2014). Context effects produced by question orders reveal quantum nature of human judgments. Proceedings of the National Academy of Sciences, 111(26), 9431-9436.] Note that the fact that a system shows quantum properties does not mean it can be reduced to quantum mechanics. I don’t claim that consciousness is the product of quantum coherence, though it may well be. See for example: Atmanspacher, Harald and Thomas Filk (2014) “Non-Commutative Operations in Consciousness Studies,” Journal of Consciousness Studies.

Why is it helpful to compare the Scholes-Black equation to the Schrodinger equation? It’s a diffusion equation. I understand the math correspondence between the diffusion equation and the Schrodinger equation – it’s very useful in my own field as a formal device – but it involves imaginary time which seems unphysical here. In any case the Scholes-Black equation isn’t actually accurate as a representation of markets, as a diffusion equation doesn’t describe a random walk with step sizes whose variance is undefined (a Levy flight).”

This refers to the section: “It turned out that many of the formulas regularly used by ‘quants’ to value derivatives such as options (the right to buy or sell a security for a set price at a future date) could be restated as quantum effects. The Black-Scholes equation, for example, can be expressed as a version of the Schrödinger wave equation from quantum physics.” I am not claiming that this is particularly useful. My book The Money Formula (written with quant Paul Wilmott) discusses how the Black-Scholes equation and others have been abused in finance. Such equations were influenced in large part by quantum physics and the nuclear program, but only took part of the message (randomness). The quantum finance approach can address some of these shortcomings, by relaxing assumptions such as high liquidity, efficiency, etc.

“But why would you think a Schrodinger equation would help us understand economics better anyway? It’s a deterministic, linear equation of exactly the same class that you criticise economics for relying on.”

Not sure which part of the text this refers to, and I am not making exactly this claim. The main advantage of the quantum approach in areas such as decision making is that it allows us to consider effects such as context and history. Interesting that this was argued back in 1978 by Qadir, before behavioural economics was invented. Qadir, A. (1978). Quantum Economics. Pakistan Economic and Social Review, 16(3/4), 117–126.

“You compare money both to an observable – which has an uncertainty relationship with a conjugate variable (which is?) & also with a particle, so it has wave-particle duality. Isn’t this confusing an object with a property of the object?”

I define money objects to be transferable entities, created by a trusted authority, which have the special property of a defined monetary value, specified by a number and a currency unit (see The Evolution of Money, and this paper). They therefore combine the mental idea of a numerical quantity of money – the virtual wave attribute – with the physical idea of an object that can be possessed or transferred – the real particle attribute. Money objects are unique in that they have a defined value, so there is no chance of e.g. interference effects (a five-dollar bill doesn’t interfere with a ten-dollar bill in your wallet). However money objects are used to price goods through transactions in markets, where such effects can occur. In quantum finance, the conjugate variables for something like a stock are price and momentum. And one of the main findings of quantum cognition is that many behavioural economics effects can be explained in terms of interference. See e.g. Yukalov, V. I., & Sornette, D. (2015). Preference reversal in quantum decision theory. Frontiers in Psychology, 6, 1-7.

“I agree with you that economics needs to put more thought into understanding what kind of things money and value are & agree that money’s trying to do more than a simple scalar variable can manage. But it seems to me that invoking quantum mechanics doesn’t bring much more to the problem than a bunch of rather forced analogies.”

The piece does not evoke forced analogies,  it is saying that the money system is a quantum system in its own right, with its own versions of duality, indeterminacy, entanglement, and so on. This is not the same as saying that it is identical to quantum physics or reduces to it.

“E.g. quantum entanglement means something really very precise that isn’t at all the same as saying that people are connected, or even that one persons credit is someone else’s debt.”

To quote my textbook (Rae), “the word ‘entanglement’ refers to a quantum state of two or more variables, where the probabilities of the outcome of measurements on one of them depend on the state of the other – even though there is no interaction between them.” The classic example is of two entangled photons A and B, where a result of positive spin measured along a certain axis for A implies that a later measurement of B “can now yield only a negative result.” Suppose now that person A has a loan from person B. From the viewpoint of quantum decision theory (or just a mathematical model), we can view A’s decision to default as a measurement process similar to the measurement of a particle’s spin; and this decision changes the status of the loan instantaneously – it can only yield a negative result – as B will find if they try to get their money back. Obviously it is hard to think how one could perform something like a Bell’s test (designed to tease out relationships between entangled particles) on a loan agreement, since such tests require measurements along different spin axes, but that doesn’t mean entanglement does not occur.

“So I agree with you that there’s lots to be done in economics & it should learn from physics, but I think (as has already begun) the place to look is in stat mech (including complexity & network theory) rather than qm.”

I have been advocating for a complexity approach in several books (Economyths, Truth or Beauty, etc.), but I see the quantum approach as backing up that claim.The point is not that economists should try to build elaborate quantum models of the economy – indeed, the neoclassical emphasis on microfoundations is misplaced. Instead, prices are best seen as emergent properties (see this paper). At the same time, the quantum effects of money can scale up and affect the economy as a whole. An example is the quadrillion dollars worth of derivatives which have been strangely absent from mainstream macroeoconomic models – now that is confused and misleading.

Update: response to more twitter comments.

Economist David Harbord: “This bit of silliness by @d_orrell should be enough to keep @Noahpinion wound up for a day or two …”

Niels Bohr asked of one physics theory, “The question is whether it is crazy enough to be have a chance of being correct.” The economics version is that this theory might be silly – but is it silly enough?

“I recall someone showing up at the LSE when I was a graduate student claiming that the problem with economic theory was the assumption Newtonian time & space, hence we needed an Einsteinian revolution in economics! Might be worth checking up on progress in that research program.”

Over a century ago some people showed up claiming that economics should be based on Newtonian mechanics. We all know how that research program is going.

“Both suggestions focus on phenomena at the wrong scale for modelling economic activity. If you can produce a model in which quantum effects matter & make predictions that ‘Newtonian’ economics cannot (lots of scope there!), then I’ll admit you are on to something.”

I’m not saying economics reduces to quantum physics – I am saying that money has its own quantum properties, which do scale up. But read the book when it comes out and judge for yourself. For my thoughts on prediction, see this recent article for Newsweek Japan.

Thread begun by science communicator Natalie Wolchover from Quanta:

What a load of hogwash.”

Like traditional economics then – with the nice difference that it doesn’t act as the PR wing for an out-of-control financial sector.

Further comments by various people on the same thread:

“I thought these guys were starting to get passed the physics envy.”

I actually trained in math and physics, so perhaps this should be economics envy. In my previous books such as Economyths I have done as much as most people to argue against the idea that economics can be simply transposed from physics. However metaphor is intrinsic to our thought processes, and neoclassical economics has long been replete with metaphors from Victorian mechanics – one of its founders Vilfredo Pareto for example said that “pure economics is a sort of mechanics or akin to mechanics” – so perhaps it is time to expand our mental toolbox. After all, it isn’t just quantum mechanics which has been “misused and abused” (to quote Sean Carroll). Also, while I did study quantum mechanics, and use it in my work (my early career was spent designing superconducting magnets which rely on quantum processes for their function), my intention is not to further mathematicise economics – quite the opposite. The core ideas of the theory proposed are very simple.

“Can hardly wait for the forthcoming quantum astrology piece.” “Or auto repair done by quantum mechanics.” “I would be happy to have dollars tunnel from pockets of rich folks to mine.” “That’s about two steps away from Deepity Chakra.” “I think he means a paradigm shift.” “To quote physicist Wolfgang Pauli, this is not even wrong.”

Not even funny … Physicists have been very careful to put up a lot of filters around quantum ideas, which is understandable, but is also one reason social scientists are stuck in an oddly mechanistic view of the world. So we need more discussion between these areas.

For more comments and responses, see the comments thread in the article.

 

October 20, 2017

A Quantum Theory of Money and Value, Part 2: The Uncertainty Principle

Filed under: Economics, Forecasting — Tags: — David @ 4:53 pm

New paper in Economic Thought

Abstract: Economic forecasting is famously unreliable. While this problem has traditionally been blamed on theories such as the efficient market hypothesis or even the butterfly effect, an alternative explanation is the role of money – something which is typically downplayed or excluded altogether from economic models. Instead, models tend to treat the economy as a kind of barter system in which money’s only role is as an inert medium of exchange. Prices are assumed to almost perfectly reflect the ‘intrinsic value’ of an asset. This paper argues, however, that money is better seen as an inherently dualistic phenomenon, which merges precise number with the fuzzy concept of value. Prices are not the optimal result of a mechanical, Newtonian process, but are an emergent property of the money system. And just as quantum physics has its uncertainty principle, so the economy is an uncertain process which can only be approximated by mathematical models. Acknowledging the dynamic and paradoxical qualities of money changes our ontological framework for economic modelling, and for making decisions under uncertainty. Applications to areas of risk analysis, forecasting and modelling are discussed, and it is proposed that a greater appreciation of the fundamental causes of uncertainty will help to make the economy a less uncertain place.

Published in Economic Thought Vol 6, No 2, 2017. Read the full paper here.

July 17, 2017

On straw men

Filed under: Uncategorized — David @ 2:59 pm

From the preface to Economyths: 11 Ways Economics Gets It Wrong

As anticipated in the 2010 version of Economyths, many economists have argued that the economyths are an unfair caricature of their field – a ‘straw man’ I am setting up to easily defeat. Four things to add. First, this argument is a little over-used. ‘Read any review of a heterodox book by an economist’, noted Cahal Moran in 2011, and ‘you will find the exact same rhetoric’: the author is ‘attacking straw men, he doesn’t understand economics, etc.’ An external investigation into the economics department at the University of Manitoba in 2015 found that ‘the insistence by the mainstreamers that the heterodox are attacking a straw man could be labelled “gaslighting” [i.e. psychologically manipulating someone into doubting their own sanity]. Even as some heterodox are subject to unfriendly discrimination, ridicule, hostility, and censure, some mainstreamers simply deny it and insist the others are making it all up.’ Call me crazy, but I think they have a point.

Secondly, economists have long deflected criticism by claiming that key assumptions such as the rational behaviour of ‘economic man’, as Lionel Robbins put it in 1932, are ‘only an expository device – a first approximation used very cautiously at one stage in the development of arguments’. (As seen in the Appendix, economists repeat the identical argument today.) But that same ‘economic man’ – which as a view of human behaviour is less a first approximation than a severe distortion – reached perhaps its most gloriously exaggerated form in the Arrow-Debreu model (Chapter 5) well after Robbins dismissed it as a ‘bogey’ (the expression ‘straw man’ was not yet in vogue), and remains at the heart of much economic modelling, which is why eight decades later we could name a book after its impending twilight with no fear of redundancy.

Thirdly, there is also a longstanding tradition in which, as Moran and his co-authors Joe Earle and Zach Ward-Perkins put it in The Econocracy: ‘The concerns of critics are said to be addressed when economists find some way of incorporating their critiques into existing frameworks. The result is often a highly stylised version of what the critic had in mind, and may drop the things that are most important while conforming to certain assumptions that the critic may reject.’ When economists consider small departures from something like equilibrium – they would have to, wouldn’t they? – or arrange patches for the more egregious examples of ‘market failure’ – such as the environmental crisis – they are like the ancient astronomers who added extra epicycles to their geocentric models of the cosmos to better fit observations, while still assuming that the universe was based on circles and the sun went around the earth. In fact it is economists who have set up a highly simplified version of the real world – but instead of destroying it, they hold it up as an ideal to which real economies can only aspire. (And if that is a ‘caricature’ or a ‘straw man’, we will stop attacking it when it stops threatening to blow up the world.)

Finally, I take pains in the book to show that the arguments apply not just to this pure textbook version of the theory, but to anything near it, epicycles and all. And as we’ll see, supposedly sophisticated models may deviate from these foundational assumptions, but they can never stray too far without losing internal consistency – which is exactly why the field finds itself in a state of crisis.

July 16, 2017

Time for critics of economics critics to move on!

Filed under: Uncategorized — David @ 3:28 pm

There is a growing trend for economists to write articles criticising the critics of economics. These articles follow a similar pattern. They start by saying that the criticisms are “both repetitive and increasingly misdirected” as economist Diane Coyle wrote, and might complain that they don’t want to hear one more time Queen Elizabeth’s question, on a 2008 visit to the London School of Economics: “Why did nobody see it coming?”

Economist Noah Smith agrees that “blanket critiques of the economics discipline have been standardized to the point where it’s pretty easy to predict how they’ll proceed.” Unlike the crisis then! “Economists will be castigated for their failure to foresee the Great Recession. Some unrealistic assumptions in mainstream macroeconomic models will be mentioned. Economists will be cast as priests of free-market ideology, whose shortcomings will be vigorously asserted.” And so on.

The articles criticising critics then tell critics it is time to adopt a “more constructive tone” and “focus on what is going right in the economics discipline” (Smith) because “only if today’s critics of economics pay more attention to what economists are actually doing will they be able to make a meaningful contribution to assessing the state of the discipline” (Coyle). If the critics being criticised are not economists, the articles often point out or imply that they don’t know what they are talking about, are attacking a straw man, etc., or even (not these authors) compare them to climate change deniers.

Speaking as an early adopter of the Queen Elizabeth story (in my 2010 book Economyths, recently re-released in extended form), allow me to say that I agree completely with these critic critics. Yes, economists failed to predict the most significant economic event of their lifetimes. Yes, their models couldn’t have predicted it, even in principle, based as they were on the idea that markets are inherently self-stabilising. And yes, economists didn’t just fail to predict the crisis, they helped cause it, through their use of flawed risk models which gave a false sense of security.

But it is time for us critics to move on, and accentuate the positive. Only by doing so can we make a meaningful contribution. And as Smith points out, calls for “humility on the part of economists” are getting old (Tomáš Sedláček, Roman Chlupatý and I wrote Bescheidenheit – für eine neue Ökonomie five years ago). It’s like asking Donald Trump to admit that he once lost at something.

Of course, some people might say that it isn’t up to economists to tell everyone else when they should stop talking about economists’ role in the crisis, or bring up what the former head of the UK Treasury memorably called in 2016 their “monumental collective intellectual error.”

Some stick-in-the-muds note that “No one took any responsibility or blame for a forecasting failure that led to a policy disaster” and have called for a public inquiry into their role in the crisis. Instead of telling everyone else to move on, they argue, it is time for economists to own their mistakes. Well guess what, people – it’s not going to happen! And stop asking for a public apology. Let’s focus on what is going right and hand out some gold stars.

For example, there is the “data revolution” heralded by Smith. As he notes, “econ is paying a lot more attention to data these days.” Sure, economists are literally the last group of researchers on earth to have realised the usefulness of data. In physics the “data revolution” happened back when astronomers like Tycho Brahe pointed their telescopes at the sky and began to question the theories of Aristotle. But better late than never!

Oh, here’s a data point – all the orthodox theories failed during the crisis! But you knew that.

Or there is behavioral economics, which Coyle notes is “one of the most popular areas of the discipline now, among academics and students alike.” Critics again might note that progress in this area has been painfully slow and has had little real impact. Tweaks such as “hyperbolic discounting” are equivalent to ancient astronomers appending epicycles to their models to make them look slightly more realistic. But that rational economic man thing is so over – straw man walking.

Admittedly, there has been less progress on a few things. The equilibrium models used by policy makers, for example, still rely on the concept of equilibrium – and so have nothing to say on the cause or nature of financial crises. Risk models used by banks and other financial institutions still view markets as governed by the independent actions of rational economic man investors, and are more useful for hiding risk than for estimating it, as quant Paul Wilmott and I have argued.

As Paul Krugman noted in 2016, “we really don’t know how to model personal income distribution,” even though social inequality – along with financial instability – is one of the biggest economic issues of our time. Some insiders such as World Bank chief economist Paul Romer – who compared a chain of reasoning in the field of macroeconomics to “blah blah blah” – describe the area as “pseudo-science”. And economics education still concentrates almost solely on the discredited neoclassical approach, complete with rational economic man, according to the student authors of The Econocracy.

But these are details. As Coyle notes, some economists are finally getting to grips with ideas from areas such as “complexity theory, network theory, and agent-based modeling” which of course are exactly those areas that critics have long been suggesting they learn from.

Or the UK’s Economic and Social Research Council recently let it be known that it is setting up a network of experts from different disciplines including “psychology, anthropology, sociology, neuroscience, economic history, political science, biology and physics,” whose task it will be to “revolutionise” the field of economics. Again, that is nice, since Economyths called in its final chapter for just such an intervention by non-economists back in 2010.

So, yes, it is time to celebrate the new dawn of economics! But critics of critics – do try to move on from the same criticisms, we’ve heard it all before, in fact for decades now.

April 13, 2017

Review of The Evolution of Money

Filed under: Books, Economics, Reviews — David @ 8:56 pm

The Evolution of Money is reviewed in News Weekly by Colin Teese, former deputy secretary of the Australian Department of Trade:

“Who would have thought of linking money and quantum physics? Well, Orrell and Chlupaty  have done just that in The Evolution of Money, perhaps the best book on money I have  ever read …

The authors have set themselves the dauntingly difficult task of explaining money, as it  were, from the ground up, cutting the cant that has surrounded the subject for centuries.  Blending a happy combination of skills and experience, they have recorded a satisfying and  entertaining account of how money has impacted, of course, on economics, but no less on  politics and society. But that is not the end of it. They make a persuasive case, at least to this reader’s satisfaction, on how the evolution of money has tracked that of science …

A reasonable and benign dictator might demand that those engaged in activities relating to economic management should, as a condition of employment, be compelled to read The Evolution of Money and pass a written examination based on an understanding of its contents.”

Read the full review at News Weekly.

April 4, 2017

The Money Formula – New Book By Paul Wilmott And David Orrell

Filed under: Books, Economics — Tags: , — David @ 3:09 pm

The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets

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Explore the deadly elegance of finance’s hidden powerhouse

The Money Formula takes you inside the engine room of the global economy to explore the little-understood world of quantitative finance, and show how the future of our economy rests on the backs of this all-but-impenetrable industry. Written not from a post-crisis perspective – but from a preventative point of view – this book traces the development of financial derivatives from bonds to credit default swaps, and shows how mathematical formulas went beyond pricing to expand their use to the point where they dwarfed the real economy. You’ll learn how the deadly allure of their ice-cold beauty has misled generations of economists and investors, and how continued reliance on these formulas can either assist future economic development, or send the global economy into the financial equivalent of a cardiac arrest.

Rather than rehash tales of post-crisis fallout, this book focuses on preventing the next one. By exploring the heart of the shadow economy, you’ll be better prepared to ride the rough waves of finance into the turbulent future.

  • Delve into one of the world’s least-understood but highest-impact industries
  • Understand the key principles of quantitative finance and the evolution of the field
  • Learn what quantitative finance has become, and how it affects us all
  • Discover how the industry’s next steps dictate the economy’s future

How do you create a quadrillion dollars out of nothing, blow it away and leave a hole so large that even years of “quantitative easing” can’t fill it – and then go back to doing the same thing? Even amidst global recovery, the financial system still has the potential to seize up at any moment. The Money Formula explores the how and why of financial disaster, what must happen to prevent the next one.

PRAISE FOR THE MONEY FORMULA

“This book has humor, attitude, clarity, science and common sense; it pulls no punches and takes no prisoners.”
Nassim Nicholas Taleb, Scholar and former trader

“There are lots of people who′d prefer you didn′t read this book: financial advisors, pension fund managers, regulators and more than a few politicians. That′s because it makes plain their complicity in a trillion dollar scam that nearly destroyed the global financial system. Insiders Wilmott and Orrell explain how it was done, how to stop it happening again and why those with the power to act are so reluctant to wield it.”
Robert Matthews, Author of Chancing It: The Laws of Chance and How They Can Work for You

“Few contemporary developments are more important and more terrifying than the increasing power of the financial system in the global economy. This book makes it clear that this system is operated either by people who don′t know what they are doing or who are so greed–stricken that they don′t care. Risk is at dangerous levels. Can this be fixed? It can and this book full of healthy skepticism and high expertise shows how.”
Bryan Appleyard, Author and Sunday Times writer

“In a financial world that relies more and more on models that fewer and fewer people understand, this is an essential, deeply insightful as well as entertaining read.”
Joris Luyendijk, Author of Swimming with Sharks: My Journey into the World of the Bankers

“A fresh and lively explanation of modern quantitative finance, its perils and what we might do to protect against a repeat of disasters like 2008–09. This insightful, important and original critique of the financial system is also fun to read.”
Edward O. Thorp, Author of A Man for All Markets and New York Times bestseller Beat the Dealer

April 2, 2017

Why Toronto house prices keep going up

Filed under: Economics — Tags: , — David @ 7:08 pm

Ever wonder why prices in cities such as Toronto keep going up? The reasons given are many – foreign buyers, low interest rates, lack of supply, and so on – but while these are all contributing factors, the real reason is much simpler.

It’s because there is more money.

housepricemoneysupply

The solid line shows the Teranet 6-city index which goes back to 1999, the dashed line is a broad measure of money supply (M2++).

And why is there more money? It’s because house prices have gone up. Most of the money in our economy is generated by bank loans, usually against real estate – and when prices go up, they can make larger loans.

Thus house prices and money supply increase in tandem. Of course, at some point they can also go down in tandem …

February 7, 2017

Big data versus big theory

Filed under: Forecasting — Tags: — David @ 4:05 pm

The Winter 2017 edition of Foresight magazine includes my commentary on the article Changing the Paradigm for Business Forecasting by Michael Gilliland from SAS. A longer version of Michael’s argument can be read on his SAS blog, and my response is below.

Michael Gilliland argues convincingly that we need a paradigm shift in forecasting, away from an “offensive” approach that is characterized by a reliance on complicated models, and towards a more “defensive” approach which uses simple but robust models. As he points out, we have been too focussed on developing highly sophisticated models, as opposed to finding something that actually works in an efficient way.

Gilliland notes that part of this comes down to a fondness for complexity. While I agree completely with his conclusion that simple models are usually preferable to complicated models, I would add that the problem is less an obsession with complexity per se, than with building detailed mechanistic models of complexity. And the problem is less big data, than big theory.

The archetype for the model-centric approach is the complex computer models of the atmosphere used in weather forecasting, which were pioneered around 1950 by the mathematician John von Neumann. These weather models divide the atmosphere (and sometimes the oceans) into a three-dimensional grid, and use equations based on principles of fluid flow to compute the flow of air and water. However many key processes, such as the formation and dissipation of clouds, cannot be derived from first principles, so need to be approximated. The result is highly complex models that are prone to model error (the “butterfly effect” is a secondary concern) but still do a reasonable job of predicting the weather a few days ahead. Their success inspired a similar approach in other areas such as economics and biology

The problem comes when these models are pushed to make forecasts beyond their zone of validity, as in climate forecasts. And here, simple models may actually do better. For example, a 2011 study by Fildes and Kourentzes showed that, for a limited set of historical data, a neural network model out-performed the conventional climate model approach; and a combination of a Holt linear trend model with a conventional model led to an improvement of 18 percent in forecast accuracy over a ten-year period.[1]

As the authors noted, while there have been many studies of climate models, “few, if any, studies have made a formal examination of their comparative forecasting accuracy records, which is at the heart of forecasting research.” This is consistent with the idea that complex models are favored, not because they are necessarily better, but for institutional reasons.

Another point shown by this example, though, is that models associated with big data, complexity theory, etc., can actually be simpler than the models associated with the reductionist, mechanistic approach. So for example a neural network model might run happily on a laptop, while a full climate model needs a supercomputer. We therefore need to distinguish between model complexity, and complexity science. A key lesson of complexity science is that many phenomena (e.g. clouds) are emergent properties which are not amenable to a reductionist approach, so simple models may be more appropriate.

Complexity science also changes the way we think about uncertainty. Under the mechanistic paradigm, uncertainty estimates can be determined by making random perturbations to parameters or initial conditions. In weather forecasting, for example, ensemble forecasting ups the complexity level by making multiple forecasts and analysing the spread. A similar approach is taken in economic forecasts. However if error is due to the model being incapable of capturing the complexity of the system, then there is no reason to think that perturbing model inputs will tell you much about the real error (because the model structure is wrong). So again, it may be more appropriate to simply estimate error bounds based on past experience and update them as more information becomes available.

Complexity versus simplicity

An example from a different area is the question of predicting heart toxicity for new drug compounds. Drug makers screen their compounds early in the development cycle by testing to see whether they interfere with several cellular ion channels. One way to predict heart toxicity based on these test results is to employ teams of researchers to build an incredibly complicated mechanistic model of the heart, consisting of hundreds of differential equations, and use the ion channel inputs as inputs. Or you can use a machine learning model. Or, most complicated, you can combine these in a multi-model approach. However my colleague Hitesh Mistry at Systems Forecasting found that a simple model, which simply adds or subtracts the ion channel readings – the only parameters are +1 and -1 – performs just as well as the multi-model approach using three large-scale models plus a machine learning model (see Complexity v Simplicity, the winner is?).

Now, to obtain the simple model Mistry used some fairly sophisticated data analysis tools. But what counts is not the complexity of the methods, but the complexity of the final model. And in general, complexity-based models are often simpler than their reductionist counterparts. Clustering algorithms employ some fancy mathematics, but the end result is clusters, which isn’t a very complicated concept. Even agent-based models, which simulate a system using individual software agents that interact with one another, can involve a relatively small number of parameters if designed carefully.

People who work with big data, meanwhile, are keenly aware of the problem of overfitting – more so it would appear then the designers of reductionist models which often have hundreds of parameters. Perhaps the ultimate example of such models is the dynamic stochastic equilibrium models used in macroeconomics. Studies show that these models have effectively no predictive value (which is why they are not used by e.g. hedge funds), and one reason is that key parameters cannot be determined from data so have to be made up (see The Trouble With Macroeconomics by Paul Romer, chief economist at the World Bank).

One reason we have tended to prefer mechanistic-looking models is that they tell a rational cause-and-effect story. When making a forecast it is common to ask whether a certain effect has been taken into account, and if not, to add it to the model. Business forecasting models may not be as explicitly reductionist as their counterparts in weather forecasting, biology, or economics, but they are still often inspired by the need to tell a consistent story. A disadvantage of models that come out of the complexity approach is that they often appear to be black boxes. For example the equations in a neural network model of the climate system might not tell you much about how the climate works, and sometimes that is what people are really looking for.

When it comes to prediction, as opposed to description, I therefore again agree with Michael Gilliland that a ‘defensive’ approach makes more sense. But I think the paradigm shift he describes is part of, or related to, a move away from reductionist models, which we are realising don’t work very well for complex systems. With this new paradigm, models will be simpler, but they can also draw on a range of techniques that have developed for the analysis of complex systems.

[1] Fildes, R., and N. Kourentzes. “Validation and forecasting accuracy in models of climate change.” International Journal of Forecasting 27 (2011): 968–995.

 

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