# The Future of Everything

## March 12, 2021

### Quantum economics and finance – video series

Filed under: Quantum Economics and Finance — Tags: , — David @ 8:40 pm

This series of short videos introduces the key ideas of quantum economics and finance. The only background assumed is basic linear algebra. The material is based on the book Quantum Economics and Finance: An Applied Mathematics Introduction.

Notes:

QEF01 – Introduction to Quantum Economics and Finance

QEF02 – Quantum Probability and Logic

QEF03 – Basics of Quantum Computing

QEF04 – Quantum Cognition

QEF05 – The Quantum Walk

QEF06 – The Penny Flip Game

QEF07 – The Prisoner’s Dilemma

QEF08 – Quantizing Propensity

QEF09 – Threshold Effects in Quantum Economics

QEF10 – A Quantum Option Pricing Model

QEF11 – The Money Bomb

## May 17, 2021

### Shifting the Economyths

Filed under: Economics, Quantum, Talks — David @ 3:29 pm

This is the text for my contribution to the online conference Beyond the False Dichotomy: Shifting the Narrative

I wrote Economyths a little over ten years ago, and in part the book was my response to the financial crisis. The thesis was that mainstream economics is based on a number of what I called economyths which were defined as beliefs or stories that have shaped economic thought. There were ten in total but to give you an idea here I will just mention four:

One said that the economy is made up of independent individuals. This is the idea that people are like the classical picture of a self-contained atom, and there is “no such thing as society” as Thatcher said.

Another is the idea that the economy is stable and self-correcting. This is the main story of economics from Adam Smith’s invisible hand, to modern equilibrium models.

A third is the idea that the economy is rational and efficient. The assumption that people make rational decisions to optimise utility is connected to the ideas of stability and efficiency. Corporations are defined to be an incarnation of rational economic man.

And then consistent with these is the next economyth which is that the economy is balanced and symmetrical. The issues of power and distribution are viewed as “soft” and peripheral to the subject, and therefore tend to be ignored. As one economist said in an interview, “economists are not good at what’s fair, right?”

Economists have always insisted that their theories are far more complex than these economyths would suggest, but the reality is that these assumptions have been extremely influential and in particular form the basis of the mathematical models used to simulate the economy and make policy recommendations.

In 2017 I did a revised version of the book which included an additional economyth, which was the idea that the economy boils down to barter. This is the myth which in many ways justifies the others, because it means that money isn’t important. Adam Smith for example focused on the “real” economy of labour and commodities and saw money as a kind of veil or a distraction. Economists since then have treated money as just a metric or an inert medium of exchange, and ignored its confounding properties – its dual real/virtual nature, its ability to entangle people through debt, its inherent instability, its tendency to cluster and create inequality, and its psychoactive effects on the human mind.

The drawbacks of omitting things like money and banks from the model became evident after the financial crisis of 2007, when it turned out – as one central banker explained ten years later – that “In the prevalent macro models, the financial sector was absent, considered to have a remote effect on the real economic activity.” And today, the continuing problems of financial instability, social inequality, and environmental degradation can all be largely traced to the money system, which is unstable, unfair, and is reliant on continuous growth to pay off debt.

One reason I called these ideas economyths is because, like myths, their legacy goes back a long way. We could start with post-war economists like Milton Friedman; or go back further to the neoclassical economists who first tried to establish economics as a kind of social physics in the late nineteenth century; or to Adam Smith, who didn’t try to quantify economics but was inspired by Newton. But I would argue that we can actually go back much further. My reason for saying this is that the ten economyths were based directly on a list of opposites, divided into good and evil, from the philosopher Pythagoras, who believed that the universe was based on number, and who lived at a time when coin money was changing the world of commerce. As he said, “number is all” and money is a way of putting numbers on the world.

In order to understand the narrative appeal of this model, it is interesting to compare it with another model which was very influential for a long time, and also reflected the Pythagorean ideals of symmetry and mathematical elegance, namely the Greek model of the cosmos. This model incorporated two main assumptions. The first was that the celestial bodies moved in circles, which were considered the most perfect and symmetrical of forms. The other assumption was that the circles were centered on the Earth. In Aristotle’s version, the planets and stars were thought to be encased in crystalline spheres which rotated around us at different speeds. The fact that planets did not follow perfect circles around the Earth, but sometimes tended to loop back on themselves, was handled by adding epicycles – circles around circles.

This geocentric model was complemented by a theory of physics. According to Aristotle, all matter consisted of the five elements Earth, Water, Air, Fire, and Ether which was reserved for the heavens. His theory was less a theory of motion, than a theory of stability: each element sought its own level, following the same order with Earth on the bottom and Ether on top, and in fact would do so instantaneously in a vacuum. Aristotle deduced from this that a vacuum could not exist: nature abhors a vacuum.

The Greek model lasted for well over a thousand years – it was adopted by the Church, and was not finally overturned until the Renaissance. How did it manage to last for such a long time? And what lessons are there for economics?

One reason for its durability was that it could make accurate predictions of important events such as eclipses. Another reason, though, is related to aesthetics and the fact that, as Aristotle put it, man is a political animal. There was a strong parallel between the perceived order of the cosmos and the order of society. Greek society was structured as a well-ordered hierarchy, with slaves at the base, followed in ascending order by ex-slaves, foreigners, artisans, and finally the land-owning, non-working upper class. These men alone could be citizens, and oversaw everything from above, like the stars in the firmament (women did not take part in political life and took their social class from their male partner). A model of the universe which suggested that everything has its natural place in a beautiful, geometrically-governed cosmic scheme therefore supported the status quo. For this reason it would certainly have appealed to the male leisure class that ruled ancient Athens, and later to the Catholic Church.

The first cracks in the model appeared in 1543, when Copernicus proposed that the Earth might go around the Sun, rather than vice versa. European astronomers began to observe comets which passed between the planets, so if Aristotle’s crystalline spheres had actually existed, they would have broken through them. Finally, in the late seventeenth century, Isaac Newton derived his three laws of motion and the law of gravity. The static circles of classical geometry were replaced with dynamical equations, which had a different but equally powerful aesthetic appeal. Sometimes it takes a model to defeat a model.

So what does this have to do with economics? Well, there is an obvious parallel between the Greek circle model, and our modern model of the economy, because it too pictures the world as rational, stable, ordered, and efficient, and therefore favours the elite. Indeed mainstream economics, in its obsession with rationality and efficiency, sometimes sounds like the PR wing of the financial sector. The model can’t make predictions of things like crises, or indeed the economy in general, but it goes a step further by predicting that it can’t predict, as per efficient market theory. The main difference is that while Aristotle thought that a vacuum could not exist, because otherwise things would find their equilibrium immediately, efficient market theory assumes that prices do reach equilibrium instantaneously – so a vacuum does exist, and it is the market.

Both the Greek circle model and the economics model also show the strength of a mathematical model. Even something like the financial crisis only made a dent. As Paul Krugman wrote in 2018, “Neither the financial crisis nor the Great Recession that followed required a rethinking of basic ideas.” So like the Greek model, the orthodox model of the economy is incredibly resilient.

I think if there was a specific point where this narrative became compelling in economics, it was when economists changed their theory of value, while leaving the rest of the theory mostly the same. The switch was announced 150 years ago by William Stanley Jevons, in the second paragraph of his 1871 Theory of Political Economy, where he wrote that “Repeated reflection and inquiry have led me to the somewhat novel opinion that value depends entirely upon utility.” Here utility was a kind of energy-like quality which roughly equated to happiness. Classical economists such as Smith had followed a labour theory of value which acknowledged the role of power. Utility created a new narrative which flipped this on its head. Economics was now about pleasure and good times.

Of course utility couldn’t be measured directly. Another approach though was to simply assume that utility is reflected by price. Or as Jevons put it: “just as we measure gravity by its effects in the motion of a pendulum, so we may estimate the equality or inequality of feelings by the decisions of the human mind. The will is our pendulum, and its oscillations are minutely registered in the price lists of the markets.”

A side-effect of this emphasis on subjective utility, ironically, was that by reducing value to a number, subjective things like emotion or social power or dignity or ethics were usurped by the theory and thus stripped of all weight. As Tomáš Sedláček wrote for example in his 2011 book The Economics of Good and Evil, “The issue of good and evil was dominant in classical debates, yet today it is almost heretical to even talk about it.” Another thing which also of course didn’t fit into this rational utility approach – it doesn’t compute – was money, with its economyth-defying properties of entanglement, instability, and irrationality.

But still, why is it that more than a decade after the crisis economics still remains rooted in the past? Why is the orthodox narrative so resistant to change?

One reason again is that as with Aristotelian physics, the economyths form part of a connected structure. The story they tell is consistent and self-reinforcing – markets are efficient, stable, rational – and you can’t change one part without changing them all.

Instead, what happens is that economists attempt to fold in new ideas from other areas such as complexity or behavioural psychology, without changing the structure too much. Behavioural economics for example has in my view won acceptance exactly because it can be incorporated in this way, and viewed as an epicycle that can be wheeled out for particular situations. Economists also try to add in so-called “frictions” to their equilibrium models, but assume the equilibrium exists in the first place. Paul Krugman again: “We start with rational behaviour and market equilibrium as a baseline, and try to get economic dysfunction by tweaking that baseline at the edges.” A few more epicycles and it will be perfect, the thinking goes.

The story also offers a powerful restoration narrative, because it says that crises and upsets are caused by external events, and economic forces bring the economy back to this imagined equilibrium. It therefore taps into the human desire for order which is important in politics but also in fiction. And the idea that markets are rational and efficient also justifies the powerful position of the financial sector and the wealthy, while at the same time distracting from the workings of power and the role of money.

To change the narrative it isn’t enough to modify details of the model, instead we need to go back as the neoclassicals did to the fundamental idea of value and its relation with price – in other words, the question of how much something is worth, its numerical cost. There are a number of ways of going about this, but I would argue that a good place to start is with the math.

This might seem counter-intuitive. After all, it is something of a cliché to say that economics is too mathematical. As one recent book put it, “today mainstream economics follows a path of great mathematical rigor that . . . does not make much room for other accounts of economic life.” However rigor isn’t useful if you are using the wrong kind of mathematics in the first place. And while Keynes wrote that “practical men … are usually the slaves of some defunct economist” we could also say that those economists are themselves slave to beliefs about number and value that are inherited in large part from a mathematical model.

Orthodox theory for example is based on the core idea that markets are stable and price is a measure of inherent value. It is best represented by the X-shaped figure of supply and demand. This plots supply as a line which increases with price, and demand as a line which decreases with price. The point where they match in an X represents the stable equilibrium where the market clears. This diagram appears in all introductory economics textbooks, but it is also there in the mathematical models used to simulate the economy.

An odd feature of the graph is that price appears on the vertical axis, and is assumed to be determined uniquely and passively by unknown forces of supply and demand which are in balance, so cancel out. While neoclassical economics is often described as Newtonian, it assumes equilibrium and has no real concept of mass or dynamics or force. The problem though is we never observe supply or demand independently, we only observe transactions. Like the crystalline spheres, these are imaginary constructs. Indeed the whole idea of representing a complex dynamic system as the stable intersection of two lines is very strange and is not done in other areas such as biology, where the only things that are at equilibrium are dead.

To make the diagram more scientific, a first step is to flip it around so that price is the independent variable on the horizontal axis. This seems a trivial change but is actually key to the whole story. Instead of utility, we can then plot the propensity curves for the buyer and seller, which represent the probability of transactions as a function of price. The fact that price is somewhat arbitrary and decisions are subject to effects such as context means these curves are inherently probabilistic. The X-shaped lines of supply and demand from the classical diagram are therefore replaced by probabilistic waves which only collapse down to a particular price during a transaction. This model therefore acknowledges that value is a soft and fuzzy concept, while price is a measure which is subject to intrinsic uncertainty.

The next step is to acknowledge that people are not separate atoms who only communicate by bouncing off one another, instead they are entangled beings who talk to each other, and share things like social norms. An example is the prisoner’s dilemma game, another staple from the textbooks: in classical theory everyone defects and rats out on the other person, but experimental results show people choose to collaborate between a third and a half of the time, which suggests a high degree of entanglement.

We also need to address the fact that people make decisions based on a mix of objective and subjective factors which are entangled in the mind and may interfere with one another. And above all we need to include the dynamics of money, which behaves more like a kind of information than a classical physical object.

The correct mathematical framework for this theory has already been developed by a group of radical thinkers over a century ago. Unfortunately this mathematics has until recently been reserved for the esoteric area of subatomic particles. I’m talking of course about the quantum formalism. For our purposes this refers, not to quantum physics, but to a kind of logic and probability which allows for effects such as interference, entanglement, and the idea of a measurement procedure. The word quantum is from the Latin for “how much” which applies naturally to the economy, where prices are measured through transactions. The point is not that there is a direct map between subatomic particles and humans, but that we can use similar mathematical tools to model each, which is a subtle but important distinction.

While quantum ideas have been around for a while, they are only now starting to reach critical mass in the social sciences. This new quantum narrative is under construction, in economics and finance but also in other areas such as psychology and political science. For example the Carnegie Foundation is funding a series of quantum bootcamps for social scientists at Ohio University starting this summer. There is a new anthology coming up this year from Oxford University Press on Quantizing International Relations. Danah Zohar has been bringing quantum principles into management theory for some time. The area which is seeing the most rapid adoption of quantum ideas is finance, because of quantum computing. As far as I know you can’t take a university course in quantum finance, but you can get a job in it right now in financial centers such as Paris, New York, Toronto, and so on. The development of classical computers in the post-war era changed the way we model and think about systems including the economy, and quantum computers – which have entanglement built in – are now doing the same thing.

What counts for the purposes of today’s discussion though is not the math, but the story told by the math. To summarise, the core narrative of mainstream economics is that people behave like classical atoms: hard, independent, stable. The economy can therefore be modelled as an equilibrium system. The main message of quantum economics is that people are entangled: with their own subjective feelings, with other people, with what they read in the news, and above all through the money system. The economy is a complex, dynamic, living system which can be modelled using a mix of techniques, such as ones from complexity science or systems dynamics, so long as they respect the indeterministic and entangling nature of both mind and money; and particularly the ability of the money system to scale up cognitive and financial entanglements to the societal level.

So as a one word description of the new narrative I would choose quantum or maybe entanglement. If you don’t want to do quantum mathematics, which is understandable, it doesn’t matter because what counts is the idea that the economy is best seen as a complex system which is entangled through a mix of financial and social effects. In practical terms this means that all the so-called “soft” ideas such as subjectivity, emotion, social dynamics, power, money, value, fairness and ethics that have been exiled from economics are now back in play. Debtors and creditors are entangled, shareholders and stakeholders are entangled, and we are all entangled with the climate system. Obviously the quantum approach doesn’t have all the answers, but its built-in emphasis on uncertainty can be liberating, and encourages a pluralistic response. Perhaps it is the model which teaches us to sometimes at least let go of models, because they can’t capture enough of the complex reality. And my hope is that the quantum approach and the idea of entanglement resonates with some of the other ideas and narratives discussed today.

Again, the idea that a new narrative should begin with our system of logic and probability may seem strange but history shows that mathematical models have great influence. And sometimes, as mentioned, it takes a model to beat a model. A new narrative which marks the next evolutionary step of capitalism is going to need a new mathematical framework, if only to better define its language, and help to do an audit on what ideas one may inherited, perhaps unconsciously, from the classical model. It is ironic that we live in an age characterised by volatility, uncertainty, complexity, and ambiguity but our economic theory assumes a deterministic state of placid equilibrium. It is therefore well overdue for an upgrade.

## March 4, 2021

### QEF08 – Quantizing Propensity

Perhaps the biggest difference between quantum economics and classical economics is that classical economics is based on the idea of a utility function, while quantum economics is based on the idea of propensity which is our probability of transacting. So instead of having a utility curve to model a consumer or supplier we’re going to use a propensity curve which describes the probability of buying or selling at a particular price. Now, let’s say that we start with a probability distribution, how would we get the quantum model from that? Well, a propensity curve describes information which is related to energy through the concept of entropic force, and in order to quantize the system the first step is to derive the entropic dynamics.

This concept of an entropic force was illustrated by the physicist Leo Szilard who imagined a thought experiment involving a simplified heat engine. A single particle is in a chamber at a particular temperature and we’re going to divide the chamber into two parts, denoted 0 and 1, so you can imagine this as a kind of minimal representation of a logical bit where the particle can be in the state 0 or it can be in the state 1. Now, let’s say that we know the particle is in state 1 so we have information about this system. In that case we could move the piston, with no force because it’s not going to encounter the particle, and then we could allow it to open up again and by doing that extract work from the system. The formula for the work done depends on the logarithm of the final volume over the initial volume which in this case is going to be logarithm of 2.

This thought experiment showed that having information means we can get a kind of a force out of it. Conversely a probability distribution can be viewed as the product of a corresponding entropic force. For example if we can say that there’s a likelihood that a particular particle is going to be located within a certain zone but not outside that zone you can imagine there’s a force which is acting on that particle to keep it in that area. And instead of a particle it could be an idea or in economics something like a price estimate. The equation for the entropic force is $F \left( x \right) = \gamma \frac{P'(x)}{P(x)}$ where $P$ is the probability curve. The energy involved in moving from one position $x_1$ to another $x_2$ is again going to involve a logarithm of the final propensity divided by the initial propensity $\Delta E = \gamma \log \frac{P(x_2)}{P(x_1)}$.

In the case of a normal propensity curve the entropic force turns out to be linear. It’s given by the equation s $F \left( x \right) = \frac{-\gamma \left(x - \mu \right)}{\sigma^2}$ which of course is the equation for a spring system, so you can imagine there’s a sort of spring force which is constraining the probability to stay within a certain range.

So let’s say that we have this entropic force – how can we then quantize the system to get a probabilistic wave function? Well the quantum version of a spring system is just the quantum harmonic oscillator. The ground state is a normal distribution with $\gamma = \frac{\hbar \omega}{2}$ and the associated mass is $\gamma = \frac{\hbar}{2 \omega \sigma^2}$ which is quite nice because it allows us express mass in terms of the inverse variance.

When you get a buyer and a seller coming together, the propensity curve for the seller is going to be at a higher price and the propensity curve for the buyer is going to be shifted towards lower prices, and the active part of these curves is going to be the parts near the mid price. The probability of a transaction occurring is going to be the product of the individual propensity curves, and that turns out to be a scaled normal curve. The net associated entropic force is just the sum of the buyer and seller forces.

This is a very intuitive way of understanding transactions. The buyer has a certain force pulling down towards the lower price, and the seller is trying to pull it up to a higher price. The probability of transacting is going to scale depending on a number of factors including the spread or the distance between the buyer and the seller optimal prices – if there’s a big gap between them then the probability of a transaction occurring will be lower.

The propensity diagram that we get is in some ways similar to the classical X-shaped supply and demand diagram, but in other ways it is quite different. The curves are now representing a probabilistic propensity so there’s no unique static equilibrium. There’s also no assumption that the market will clear and and so on. Simulations are obviously going to be stochastic because there’s only a probability of transactions occurring. Stochastic models are used very widely in areas such as systems biology where it’s important to take this kind of randomness into account. The video screenshot below shows results from a simple model of a supply chain where the amount of units sold in a particular week fluctuates up and down randomly because the system is inherently stochastic.

One difference between the quantum harmonic oscillator and a classical oscillator is that it has excited states with higher energies. The ground state is a normal curve but at very high energies we get a kind of jagged shape which is a bit reminiscent of the quantum walk. The higher states are not going to be used too much here but just by using the ground state and a couple of the next higher energy states we find that it’s possible to fit things like asset price fluctuations in stock markets very well.

Orrell D (2020) A Quantum Model of Supply and Demand. Physica A 539: 122928.

Orrell D, Houshmand M (2021) Quantum propensity in economics. Arxiv.org/abs/2103.10938.

For an online app demonstrating the quantum supply and demand algorithm, see here.

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Next: QEF09 – Threshold Effects in Quantum Economics

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