I’d say that a “cause” is something analogous to an academic field (like “machine learning theory” or “marine biology”) or an industry (like “car manufacturing” or “corporate law”), organized around a problem or opportunity to improve the world. The motivating problem or opportunity needs to be specific enough and clear enough that it pays off to specialize in it by developing particular skills, reading up on a body of work related to the problem, trying to join particular organizations that also work on the problem, etc.
Like fields and industries, the boundaries around what exactly a “cause” is can be fuzzy, and a cause can have sub-causes (e.g. “marine biology” is a sub-field of “biology” and “car manufacturing” is a sub-industry within “manufacturing”). But some things are clearly too broad to be a cause: “doing good” is not a cause in the same way that “learning stuff” is not an academic field and “making money” is not an industry. Right now, the cause areas that long-termist EAs support are in their infancy, so they’re pretty broad and “generalist”; over time I expect sub-causes to become more clearly defined and deeper specialized expertise to develop within them (e.g. I think it’s fairly recently that most people in the community started thinking of “AI governance and policy” as a distinct sub-cause within “AI risk reduction”).
Both within Open Phil and outside it, I think “cause prioritization” is a type of intellectual inquiry trying to figure out how many resources (often money but sometimes time / human resources) we would want going into different causes within some set, given some normative assumptions (e.g. utilitarianism of some kind).
I’d say that a “cause” is something analogous to an academic field (like “machine learning theory” or “marine biology”) or an industry (like “car manufacturing” or “corporate law”), organized around a problem or opportunity to improve the world. The motivating problem or opportunity needs to be specific enough and clear enough that it pays off to specialize in it by developing particular skills, reading up on a body of work related to the problem, trying to join particular organizations that also work on the problem, etc.
Like fields and industries, the boundaries around what exactly a “cause” is can be fuzzy, and a cause can have sub-causes (e.g. “marine biology” is a sub-field of “biology” and “car manufacturing” is a sub-industry within “manufacturing”). But some things are clearly too broad to be a cause: “doing good” is not a cause in the same way that “learning stuff” is not an academic field and “making money” is not an industry. Right now, the cause areas that long-termist EAs support are in their infancy, so they’re pretty broad and “generalist”; over time I expect sub-causes to become more clearly defined and deeper specialized expertise to develop within them (e.g. I think it’s fairly recently that most people in the community started thinking of “AI governance and policy” as a distinct sub-cause within “AI risk reduction”).
Both within Open Phil and outside it, I think “cause prioritization” is a type of intellectual inquiry trying to figure out how many resources (often money but sometimes time / human resources) we would want going into different causes within some set, given some normative assumptions (e.g. utilitarianism of some kind).