“ Science is the father of knowledge, but opinion breeds ignorance.” Arguably, this aphorism from the Ancient Greek physician Hippocrates is the foundational principle of scientific enquiry, pursued and advocated during the enlightenment period. Countless scientists have attempted to understand the world and facilitate certainty. To do so, a scientific method of explaining physical occurrences in the world has been created over time, which largely tries to dispense subjectivity and broadly ensure objective analysis.While there are permutations and nuances of this scientific method, it broadly consists of observing a physical phenomenon, making a hypothesis of why it works in this way, experimentation to validate or invalidate the hypothesis, prediction of whether these phenomenon will happen again, or in a different way, and finally an overall theory.
The scientific method is strengthened by the utilisation of mathematic symbols. Maths has structure and logic, and together this creates a dialectic, which cannot be argued with. Opinion or feelings is often flawed, and conjecture based on these subjectivities often does not lead to certainty. Today, the scientific method has co-opted maths to not only explain the physical sciences (biology, physics and chemistry, but also the human sciences (economics). Today, economics and finance is dominated by statistics. To understand the economy, and make predictions as to the performance of an economy, one has to use econometrics. Financial modelling is fundamental for an investor to determine which asset to invest in. Mathematics provides the right tool to not only explain a situation, but to also predict a future event, and more importantly, to predict human behaviour.
The problem, however, is when attempting to predict human behaviour, randomness is a constant. Human behaviour can often be surprising and against habit. This randomness arises from the power of choice: in any given situation, human beings have a myriad of options. Certainly, there are trends in behaviour for particular groups. Statistics helps in assessing those trends, but it can only work if assumptions are made, which brings in subjectivity.
So randomness and subjectivity on part of the scientist will always play a role when using maths to predict human behaviour. The recent revelations that the central theory justifying austerity measures by governments is based upon mistakes in statistical analysis have caused much brouhaha for both economists and policy makers. Harvard economists Carmen Reinhart and Kenneth Rogoff’s paper in 2010 showed that when debt to GDP is 90%, economic growth levels fall to negative figures. This in turn provided fodder for austerity supporters to justify reining in public spending and increasing tax. Part of the paper’s conclusion has now been debunked (although the authors claim that the overall conclusion remains valid) as it was arrived at though a coding error on excel.
Economists will argue that methodological mistakes such these will occur, but the usage of the paper to justify a certain course of action highlights two related concerns: firstly, the interrelationship between politics and academic enquiry, and secondly, the assured belief that statistics holds fundamental answers to predicting human behaviour. In the paper, Rogoff and Reinhart imply that increasing spending (and debt) will have an adverse effect on the economy. There is the assumption that spending less will regulate the economy. Spending decreases, governments and people get out of debt, they will then start to spend more. But a potential drawback is that if governments are not spending, or people are not spending, there will be less consumption and investment, which means companies cannot produce thereby reducing employment resulting in less money in people’s pockets.
Both scenarios can be justified by the utilisation of statistics. It means that there is no overall certainty, and ultimately, one person’s educated guess can be as good as the next persons. The precise problem in a human world is that regardless of how much effort is expended to avoid it, uncertainty will always prevail. We can never be too certain about human behaviour, but statistics has given us an illusion that one can be. It has also assumed individuals as units, as numbers that are related to each other. This relationship is than valued by economists accordingly. But humans are not units. Their behaviour may not be logical. We need to accept the uncertainty this imposes on our financial and economic prophets.
Economists can try predicting, and policy makers can debate potential courses of action. This is fine, if we accept we are starting with a degree of subjectivity in our mathematical analysis. We have to start any prediction of behaviour with the humility to accept uncertainty. Using maths does not give us certainty; it is simply a tool, a sometimes flawed tool. But it is used so extensively to justify much because there is a discomfort with not knowing, with not understanding. Human behaviour will always remain complex, complicated, and more often than not, very surprising.