Thoughts by Casebash

Many people misunderstand what is at stake with hypotheticals. If a principle is only shown to have issues in extreme hypotheticals, then it is natural for many people to assume that the principle may still work perfectly well for normal cases. My aim here is to show why these should not be ignored.

The first reason is that extreme hypotheticals are often required in order to get around the rationalisation process. It is relatively easy for someone to maintain that they wouldn’t push the person onto the track in the trolley problem, when 5 or 10 or 20 lives are at stake. But when a million lives are at stake, people are much more likely to re-evaluate whether their principle is an absolute. If they decided that it isn’t, then their cognition immediately changes. Instead of being, “Can I argue for this principle with *some* credibility?” to actually having to think through the situation a bit more. If you don’t allow people to challenge you with extreme hypotheticals, then it is much harder for someone to point out your biases and pre-conceptions. 

The second reason why this is important, is because a principle usually hasn’t just been shown to be flawed in one very specific set of circumstances, but often in all but an infinitesimally section of the range of possible values. Consider the trolley problem again. If it fails for 1 million, then it also fails for 10 million and 100 million and a billion and so on. Let’s imagine that our model that fails at 1 million can be fixed to work at that range by only making a change that only has a small effect at much more reasonable values less than 50. Let’s suppose we then have to make another small change for 10 million, then 100 million, and so it. It seems viable that all these small changes might be adding up. It is possible that all these small changes have the equivalent of a finite limit, but it is also possible that they don’t. The result of ignoring a discrepancy in an extreme case is not to allow a model to be potentially flawed in extreme cases, but to allow that model to potentially be flawed at any range.

Just because a hypothetical is extreme, we can’t just ignore it and pretend that our theory will work fine in more realistic cases. We should try to confront it head on and if the logic checks out, we should admit its strength. Some people will argue that these extreme cases can often be misleading and that we should ignore then, along the lines of Epistemically Learned Helplessness. However, the key point I am making here is to not ignore them without consideration. Even if we continue to act exactly same, having allowed ourselves to be exposed to the theory has the benefit of increasing our general open mindedness.