Like many young people in the EA community, I often find myself paralyzed by career planning and am quick to second-guess my current path, developing an unhealthy obsession for keeping doors open in case I realize that I really should have done this other thing.
Many posts have been written recently about the pitfalls of planning your career as if you were some generic template to be molded by 80,000 Hours [reference Holden’s aptitudes post, etc.]. I’m still trying to process these ideas and think that the distinction between local and global optimization may help me (and hopefully others) with career planning.
Global optimization involves finding the best among all possible solutions. By its nature, EA is focused on global optimization, identifying the world’s most pressing problems and what we can do to solve them. This technique works well at the community level: we can simultaneously explore and exploit, transfer money between cause areas and strategies, and plan across long timescales. But global optimization is not as appropriate in career planning. Instead, perhaps it is better to think about career choice in terms of local optimization, finding the best solution in a limited set. Local optimization is more action-oriented, better at developing aptitudes, and less time-intensive.
The differences between global and local optimization are perhaps similar to the differences between sequence-based and cluster-based thinking [reference Holden’s post]. Like sequence-based thinking, which asks and answers questions with linear, expected-value style reasoning, global optimization is too vulnerable to subtle changes in parameters. Perhaps I’ve enrolled in a public health program but find AI safety and animal suffering equally compelling cause areas. Suppose I’m too focused on global optimization. In that case, a single new report by the Open Philantrophy Project suggesting shorter timelines for transformative AI might lead me to drop out of my program and begin anew as a software engineer. But perhaps the next day, I find out that clean meat is not as inevitable as I once thought, so I leave my job and begin studying bioengineering.
Global optimization makes us more likely to vacillate between potential paths excessively. The problem, though, is that we need some stability in our goals to make progress and develop the aptitudes necessary for impact in any field. Adding onto this the psychological stress of constant skepticism about one’s trajectory, it seems that global optimization can be a bad strategy for career planning. The alternative, local optimization, would have us look around our most immediate surroundings and do our best within that environment. Local optimization seems like a better strategy if we think that “good correlates with good,” and aptitudes are likely to transfer if we later become convinced that no, really, I should have done this other thing.
I think the difficult thing for us is to find the right balance between these two optimization techniques. We don’t want to fall into value traps or otherwise miss the forest for the trees, focusing too much on our most immediate options without considering more drastic changes. But too much global optimization can be similarly dangerous.
Local vs. global optimization in career choice
Like many young people in the EA community, I often find myself paralyzed by career planning and am quick to second-guess my current path, developing an unhealthy obsession for keeping doors open in case I realize that I really should have done this other thing.
Many posts have been written recently about the pitfalls of planning your career as if you were some generic template to be molded by 80,000 Hours [reference Holden’s aptitudes post, etc.]. I’m still trying to process these ideas and think that the distinction between local and global optimization may help me (and hopefully others) with career planning.
Global optimization involves finding the best among all possible solutions. By its nature, EA is focused on global optimization, identifying the world’s most pressing problems and what we can do to solve them. This technique works well at the community level: we can simultaneously explore and exploit, transfer money between cause areas and strategies, and plan across long timescales. But global optimization is not as appropriate in career planning. Instead, perhaps it is better to think about career choice in terms of local optimization, finding the best solution in a limited set. Local optimization is more action-oriented, better at developing aptitudes, and less time-intensive.
The differences between global and local optimization are perhaps similar to the differences between sequence-based and cluster-based thinking [reference Holden’s post]. Like sequence-based thinking, which asks and answers questions with linear, expected-value style reasoning, global optimization is too vulnerable to subtle changes in parameters. Perhaps I’ve enrolled in a public health program but find AI safety and animal suffering equally compelling cause areas. Suppose I’m too focused on global optimization. In that case, a single new report by the Open Philantrophy Project suggesting shorter timelines for transformative AI might lead me to drop out of my program and begin anew as a software engineer. But perhaps the next day, I find out that clean meat is not as inevitable as I once thought, so I leave my job and begin studying bioengineering.
Global optimization makes us more likely to vacillate between potential paths excessively. The problem, though, is that we need some stability in our goals to make progress and develop the aptitudes necessary for impact in any field. Adding onto this the psychological stress of constant skepticism about one’s trajectory, it seems that global optimization can be a bad strategy for career planning. The alternative, local optimization, would have us look around our most immediate surroundings and do our best within that environment. Local optimization seems like a better strategy if we think that “good correlates with good,” and aptitudes are likely to transfer if we later become convinced that no, really, I should have done this other thing.
I think the difficult thing for us is to find the right balance between these two optimization techniques. We don’t want to fall into value traps or otherwise miss the forest for the trees, focusing too much on our most immediate options without considering more drastic changes. But too much global optimization can be similarly dangerous.