Jan 30, 2018
One of the big criteria used for cause area selection is scale and importance of the issue. This has been used by 80,000 Hours and OpenPhil amongst others. This is often defined as the size and intensity of the problem. For example, if an issue affects 100,000 people deeply, that would be considered higher scale than an issue that minorly affects 1,000 people. Although this version of scale is pretty common in EA, I think there are some major problems with it, the biggest of which being bottlenecking.
The broad idea of measuring scale this way has an implication baked into it that the total scale of the problem is the factor to most consider. However, with almost all large problems this seems very unlikely to be true as they are almost always going to be capped or bottlenecked by something much faster than they will by the total capacity of the problem. Take for example bednets. If AMF only gets the funding to give out 10 million bednets a year it doesn't really matter if the total scale of the malaria burden would require 20 million or 500 million. Effectively AMF is capped by money before it hits other scaling considerations. If you were a billionaire perhaps you could give enough money to make money no longer the capping feature, but even in that situation it’s likely another factor would cap before reaching all those in need of nets. In AMF’s case it would likely be number of partners who can effectively be worked with or the political stability of remaining countries.
This concern of bottlenecking is even more dramatic in fields that have tighter caps or very large scale. To take an example in animal rights, if your organization can only raise $100,000 it doesn't really matter how big the population is from a scale perspective as long as it’s much larger than you are likely to effectively help with $100,000. When comparing a cause like animal rights vs bednets, clearly the total size of the animal rights issues hits a lot more individuals, but its “true scale,” in many cases, will be more strictly capped than a more popular, better funded, and more well understood poverty intervention that affects fewer individuals.
Money is one of the most common capping features to scale but it’s not the only one. Sometimes it can be logistical factors like partners or total production in the market of a certain good. Sometimes it can be people capped (it seems likely a charity focused on surgeries would run into a skilled people shortage before running into the problem of not having enough people to do surgeries on). A capping feature could also tied to crowdedness of a space. It could also be our understanding of the problem. For example, wild animals may suffer enormously, but if we don’t know how to help all of them. In general, when looking at a cause area or charity, it seems one should consider what factor is most likely to cap scale first rather than just looking at the total size of the problem and assuming no other capping features happen.
A counter argument to this might be that scale is just used as a proxy to narrow down cause selection. This use of scale I have far less concerns with, but many people, including major organizations, explicitly use scale in the way I described to make end line calls about what causes to support.
Another counter argument is that if you think your intervention has a small chance of helping all the population. For example, if you think your action produces a 0.000001% increase in the chance of ending all factory farming, then a more normal understanding of scale makes sense. However given the huge scale of most problems EAs work on, few of our solutions are aimed at solving the whole problem (e.g. we cannot even fill fill AMF’s room for funding which is only one of many charities working on malaria). We want to be careful not being far overconfident about our ability to affect change and let that change our cause selection.