Arbitrary reduction

Choosing between things is hard and, unfortunately, more hard the more things there are to choose between. But I don't think this just applies to massively multiple choices like picking a movie or an ice cream from a needlessly comprehensive menu, it also comes from the logical generalisations of simple choices. Say you walk past a homeless man on the street and he asks you for money. Do you give it to him? Do you give it to every homeless person? Is that the best use of your money compared to, say, sending money to starving African children?

There are lots of similar scaling problems, where a little easy choice generalises into a big hard choice. You're a policeman, do you let someone off with a warning because they give you a sob story about running late? That's not going to scale; everyone's late, everyone has a sad story. But if you knew that it would just be this one person, just this one time, the decision would be easy. At scale, deciding who deserves warnings and who doesn't becomes a very complex process worthy of an entire judicial system.

But cops still give out warnings, people still give money to the homeless, and we make it through daily decisions about ice cream and other important things. I think the way we do this is by applying arbitrary reductions to the problem to scale it back down to a size where it's manageable. So you ignore the problem of all homeless people and just consider the situation in front of you. Why is the person in front of you more important than the person two blocks away? No reason. It's arbitrary, but the full problem is too hard, and arbitrary reduction gets us through the day.

The problem with arbitrary reduction is that it's often not very fair. We use simple reductions like distance, similarity, or wealth, and those often have a self-reinforcing bias. All the people in the world is too many to think about, so you help people in your comparatively well-off neighbourhood. When you're looking for people to hire, finding the best person out of everyone is hard, finding the best in your existing network is easy. If you want to invest, there are a lot of companies out there, but much fewer if you only consider those founded by friends from university. If you start on the outside of that, you stay on the outside.

Sometimes we can do without arbitrary reduction by just tackling the big problem head-on. Effective altruism is an attempt to do that for the space of charity and general do-goodery. Even without a specific framework or movement, though, it's possible to just take the hard road. Sit down and enumerate the options and goals in as much detail as necessary. If that means thinking on the scope of all people worldwide, then so be it. If it takes a week, a month, a year, so be it. That's the cost of making the right decision with all the information.

Unfortunately, it's often just not feasible. Effective altruism is a worldwide effort by many people from different disciplines all collaborating to answer that question, and there's still a fair bit of disagreement. For something like charity, where you can just make the decision once and keep benefiting from executing that decision for a long time, that might be worth it, but in other cases there's not enough time or resources to avoid an arbitrary reduction. However, that doesn't mean we have to settle for the biased reductions we have now.

So I'd like to propose a fair arbitrary reduction: randomness. It sounds strange, but why not? If the goal is to reduce your options, it's the most representative and equitable way to do so. Can't decide between ice cream flavours? Flip a coin, heads is the top of the menu, tails is the bottom. Congratulations, you just made your decision 50% easier! Looking to hire but don't have time for a full application process? Get the list of attendees for your next industry meetup, shuffle it, and try to talk to those people in order.

I'm not saying to make the actual decisions randomly, that would be chaos. But if you need to throw information away, the right way to do it is randomly. Every time we make a decision easier for ourselves by arbitrary reduction, we create an opportunity for hidden information, hidden bias, to enter the decision. Sometimes that doesn't matter, but often it does, and it's hard to know for sure. If we have to be arbitrary, we may as well be the fairest kind of arbitrary: purely random.