From Stanford to Monte Carlo and Beyond

In a Stanford interview, Tobias Wolff once said that – as a teacher – he tries to  focus on why individual stories are good rather than what it is that makes a story good in general.

The intuition here is similar to that of the Monte Carlo method; when the phenomenon of interest is too complex to describe in a deterministic fashion, one alternative is to feed a more basic and incomplete model with a large number of random scenarios and then analyze the distribution of the model’s outputs.

It is the same idea behind the claim “I don’t know much about art, but I know what I like”. If a person holding this position exposed himself to a large number of works of art and noted his reactions, he would learn much about art and about what he likes in it. But would he learn what makes art good in general?

The adjective “good” begs the question “good for what?”: a theory that tells us what makes art good is necessary. There are as many theories as theorists, so we must choose one – we now need a theory that tells us what makes a good theory of art. Follow the chain and we end up wondering what makes any action or set of actions (such as a life) good.

So we arrive at Hume’s is-ought problem: no matter how much we learn about any particular area of human endeavour or how elegant our logic, any statement about what “good” is  will be arbitrary, just a reflection of a particular viewpoint. How do we agree – as a family, as a society, as a species – on which viewpoint to take?

There are two problems embedded in this question: (1) how will we agree on the best value system and (2) how we will find it.

For (1), the best thing we’ve come up with so far to aggregate preferences is Democracy (see here for a good discussion on democracy as a cognitive mechanism).

For (2), it is clear there is no closed-form solution. The space of possibilities is n-dimensional, so any narrowly systematic search will be very inefficient. Our best bet then would seem to be – that’s right! –  the Monte Carlo method.

These ideas suggest that our time would be best spent not in comparing belief systems (atheism vs. religion, Christianity vs. Islam, social liberalism vs. social conservatism, etc) but simply in evaluating our collective responses to each of them. Further, exploration is essential – we should experiment with as many different kinds of value systems as possible. We should take “successful” (i.e. democratically elected) components from value systems and reject the rest.

Crucially, we should abandon the notion that any currently existing value system will be the “end-state” system. Even assuming that as a species we will arrive at some agreement on a value system at some point in the future – a beautiful thought – this system will be different from any currently available. All options currently on the table are too flawed.

When considering what is a good life, then, let us not waste our time in arguing what should be. Instead, let’s expose ourselves to as wide a variety of lives as possible and observe our reaction. This will not only be more productive – it will be immensely more enjoyable.

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