Believe it or not: machine learning, a computational discipline to make our beloved little computers more “intelligent”, actually teaches me some lessons about politics. These are no super new insights, but I sincerely love how I was able to transport my knowledge about machines to my experience of daily politics – and thus wanted to share my thoughts with you.
When machines try to learn, they always need to figure out some model of their respective environment. A robot, for instance, may have to derive a model how to get from one point A to another point B without using too much energy. Which movements are costly? Which ones are not? Where to go to reach the goal at all?
Another robot may stand in front of a box with a lot of sandwiches, handing them to fascinated students. Its task: predict the next sandwich flavour. Will it be turkey? Or cheese? Or just pure cucumber?
Well, you may find better examples of what robots can do, it doesn’t matter at all. In all those cases, the basic principle is the same: we have a bunch of hypotheses about our environment and want to figure out the most probable one. To do so, we use two major ingredients: prior (expert) knowledge about our hypotheses and observations, which may update our knowledge.
Actually, politics are just the same. The more you deal with or read about our daily politics, the more you learn. You gain some expert knowledge, which allows you to predict whether some events are more likely to happen or not. At the same time, you watch our politicians, given our global environment, and observe how they react, what ideas they produce, which strategies they apply to convince people. The political stage is the environment, the politicians are the actors, and their actions are observations.
In a grown-up political system, there is little which might surprise us. Think of ongoing elections: is it surprising that politicians of all parties claim themselves to have the only possible solution while all the others are horrible losers? Are we shocked that politicians transform the biggest loss into a magnificent victory after an election night? Are we surprised that politicians constantly repeat the very same sentences, as if they would become reality afterwards? Not really. This matches perfectly with our expectations.
There are some elements, though, which don’t fit. Think of the American President Donald Trump, for example. Nobody, me included, believed in his victory. My prior knowledge said: the Americans cannot vote such a man. They proved me wrong. Another example: Brexit. I was sure the British people could not be so stupid to leave the European Union. They were. Surprise, surprise.
These events, these results happened, because we all – including our politicians – were so sure about what might happen. Our prior knowledge has been confirmed and confirmed multiple times, and thus our prediction was super clear. However, reality sometimes doesn’t fit the model. We got surprised. Wow.
The result: the certainty of our prior knowledge went down drastically. Think of the elections in the Netherlands. People were quite sure that Geert Wilders might lead his party towards victory. Polls were ignored, said to be wrong all the time. In the end, they were right, and Wilders lost – a little healing confirmation to our wounded prior knowledge.
Next stop: France. The closer we are, the more worried we become. Our prior knowledge tells us that Marine Le Pen has no chance at all – but does reality know that? Will the French people actually vote against the European Union, the European friendship? We don’t know yet. Dozens of articles are written, some of them saying “Why Marine Le Pen will never win!”, others “Why Marine Le Pen could actually win!”. Yeah. Whatever.
We see: our observations of reality have drastically changed our prediction of the future. The once so clear posterior probabilities were reduced, new hypotheses joined the game. Suddenly, there is movement, there is a change in data.
And at the same time, people start to become political again. They see that things are moving around, and they either want to throw it over – or they want to rescue the system. They were inactive as long as the machine was running smoothly. Now it doesn’t. Now they interfere.
Politics are the environment, and we are the machines. The agents. We constantly update our knowledge based on what we observe – the daily news, political statements, our own experience in the streets of the cities. There is a little twist, though: we are part of the environment. We interfere with it. Our data is not independent. Which makes things super complicated.