In economics today, it is often assumed that human behaviour follows the model of ‘Homo Economicus’ or ‘the economic man’. This refers to a form of behaviour where an individual acts rationally to optimise their own utility, or the total satisfaction gained from the consumption of a good or service. While this approach has stood the test of time, cognitive psychologists have since shown that these assumptions don’t always hold true. Instead of being perfectly rational economic agents, human beings usually employ heuristics or ‘mental shortcuts’ to make decisions. This in turn leads to irrational economic behaviour, such as fallacies and biases. Behavioural economists have since attempted to modify the model to account for these effects, such as loss aversion and herd behaviour. Despite this, a range of effects still could not be accounted for leading to remaining anomalies whereby these models were still unable to reliably predict the outcome of human decisions. 

The Homo Economicus model has a range of uses; businesses can use it to predict consumer behaviour, banks can employ it to estimate financial risk, while policy makers can draw on the model to predict the outcomes of new policies. Quantum economics is a new economic model which can be used for the exact same purpose, but taking into account irrational economic behaviours, to make a better, more reliable model. It uses concepts derived from quantum physics to express a value in terms of a propensity function. This propensity function allows us to view the behaviours of the economy as a quantised probability. It allows subjective factors to have just as large an effect on the prediction of behaviour as objective factors making the model extremely reliable. Although it is impossible to tell exactly what somebody is thinking or what their motivations are, it is possible to quantify the propensity for a person to make a particular decision. Simply put, quantum probability is a set of mathematical rules that allows us to the predict the outcome of an event as a probability. 

As you may know, bits are used in modern computers to represent information. They can be represented as either a 1 or a 0 and a string of these 1s and 0s are used to represent this information. Unlike your everyday computer, quantum computers use quantum bits, more commonly referred to as qubits. These qubits, like your regular bit can be represented as a 1 or 0 but can also be a superposition of both. This superposition will be a random complex coefficient of both states which will collapse upon one state when measured. This maps to real life in, for example, selling a house. You may have a fuzzy idea of how much the house is worth, yet you never know it’s actual price until the moment it is sold. This idea is the foundation to which quantum economics is built on. Money is a way to collapse value down to a number. So why is this useful? 

Suppose we were to simply predict the outcome of a man choosing between one of two options. A binary choice. If the person is equally likely to choose either, this can be also represented by a random coin toss. When the coin is in the air, it is in a state of superposition. These conditions can be represented by a diagonal ray as shown in figure 1 as A. The x-axis represents heads and the y-axis represents tails. Using the ray, S1, the height of ray represents the probability of receiving tails, T1, and the width represents the probability of receiving heads, H1. If the ray has length 1, we can find these lengths, using simple Pythagoras Theorem, by taking the square of the projections onto the respective axis. Using this, the ray can now be used to find the probability of either of the two outcomes. This is a very simple model, however, we can scale this up to incorporate many factors, including our behavioural irrationality. The superposition of waves allows these waves to cancel out if the forces are in opposite directions. This is similar to the human mind where we create arguments for and against a decision, where good cancels out bad, ultimately deciding upon the one that outweighs the other. It is this interference that allows us to explain the psychological phenomena that otherwise, would not be able to be incorporated into our previous models. Because the square root of a probability relies on the square, we can  use these negative projections, as displayed in B. This negative projection allows for the possibility of interference where probabilities are able to cancel out, as demonstrated in C, giving us an outcome.

Now we understand how a function can be derived from a set of statistics and the use of superposition to cancel out opposing probabilities, we can now build models to display this information as a probability wave. We do this by switching our original principle, that supply and demand determine price, to making price the object that determines propensity. Remember, money is a way to collapse value down to a number. Unlike supply and demand, price is a fixed value allowing it to be our independent variable. With price as our fixed value, we are able to compare outcome to it through our propensity function to derive a model. As shown in figure 2, the probability density is highest at price 1 meaning this is our best estimate, and as we move further away from this peak, it describes the decrease in probability. We can use it to model market phenomena such as the optimal pricing/value of goods and services to maximise demand. It can also give us insights into market vulnerabilities, helping to protect financial institutions and unlocking endless possibilities. This model gives us a consistent set of equations which we can utilise to describe the potential outputs of a system. 

To summarise, quantum modelling is the incorporation of superposition from quantum physics to create a propensity function, which is a set of mathematical rules which allows us to predict behaviours as a probability. This probability negates the effect of anomalies deriving from behavioural economics, which is not incorporated in the current economic model, resulting in a more accurate and reliable model. However, this quantum model can only be used by comparing with a large number of previous experiments with the same boundary conditions.