Rechercher

Homo Economicus to Machina Economica by Ben Sadyalunda

Mis à jour : mars 3



Let us go back to the year 1929. October 24th, to be exact.

It was 6 am.

You could feel the knots in your stomach as you lay there assessing your past decisions in the piercing morning silence. Not even the hummingbirds sang that day, as if in awe of the tense atmosphere. As you creep your eyes open you sense the presence of the shadows before you see them, looming in the darkness of dawn. Having been one of the most intuitive investors of the 1920s, you were still yet to encounter this much restlessness.


After deliberating the plan for that Thursday, in front of a black coffee, you decided to follow your gut as you had done so often in the past. It was time to cut your losses and sell everything. It never occurred to you that almost every other banker was thinking the same thing that day. As you made your way to the brokerage you noticed the electric tension in the air rise almost in accordance with the chaos around you.


The more anxious you felt the stronger the smell of thunder got. Had it been the early 2000s, the song “when it rains it pours ..” would have been more accurate. As soon as your foot made contact with the ground you felt the first droplets. Slow but heavy, clear but obscure, so insignificant, yet it felt like there was an underlying message. Had you been a weather forecaster, maybe you would have been able to determine the meaning of this.


The line stretched out of the building and onto the sidewalks. It seemed like everyone was interconnected by some invisible force beyond understanding. It was like a herd of sheep gathered in fear of the wolves that closed in slowly.


From the moment 10:00 hit all hell broke loose. Chaos. The fierce desire of people wanting to get to the front of the line as fast as possible to minimise the damage to their bank accounts, yourself included.


On that day, the 24th of October 1929, 12.9 million shares were traded and five days later 16 million more shares changed hands.


This was the beginning of the end for many people in the 1930s, from this day onward, until sometime in 1939, global economies experienced the worst economic downturn in the history of the 20th century. After stock markets crashed unemployment surged, with consumer spending and investing deteriorating causing steep declines in industrial output.

One of the fruits of this historic event was the boom of economists and the development of economic analysts that we see today, by use of different tools to try and predict market movements. Yet today, many say economists are like weather forecasters when it comes to predicting economic states. Prakash Loungani of the IMF, for instance, revealed that economists failed to predict 148 of the 150 of the past recessions. Even the weather forecasters aren’t that bad I’m sure you’re thinking.


The reason behind this is the nature of economies, mainly based on how the population feels. The difference between the two disciplines is when a weather forecaster tells you to expect rain, the rain is independent of how you react to not get drenched. Whereas when an economic forecaster predicts rain, the amount of downpour is very much dependent on the feelings of the consumer.


Although economic forecasting has many similarities to the natural sciences, Friedrich Hayek mentioned it to be a flawed science while accepting his Nobel Peace Prize. The reason for this is the fact that a simple change in a few variables results in predictions quickly becoming incredibly complex. Furthermore, forecasters do not always seek the truth but instead may forecast information to affect the market in certain ways.


The development of Artificial Intelligence in recent years means we can expect these predictions to improve drastically, especially in the field of behavioural economics, but to what extent you may ask. Well, JP Morgan, for example, has already been using algorithms to track the impacts of Donald Trump’s tweets on financial markets. We might even predict changes in supply and demand to implement necessary changes in order to avoid economic downturns.


Essentially instead of relying on emotion, intuition, bias, and impulse when making decisions, investors and economists could make decisions based on data and AI algorithms.


“In theory, scholars of the social sciences aim to attain the truth as well. Unfortunately, this rarely holds.”