[The following is a lightly edited response I gave in an email conversation.]
What is your overall intuition about positive feedback loops and the importance of personal fit? Do they make it (i) more important, since a small difference in ability will compound over time or (ii) less important since they effectively amplify luck / make the whole process more noisy?
My overall intuition is that the full picture we paint suggests personal fit, and especially being in the tail of personal fit, is more important than one might naively think (at least in domains where ex-post output really is very heavy-tailed). But also how to “allocate”/combine different resources is very important. This is less b/c of feedback loops specifically but more b/c of an implication of multiplicative models:
[So one big caveat is that the following only applies to situations that are in fact well described by a multiplicative model. It’s somewhat unclear which these are.]
If ex-post output is very heavy-tailed, total ex-post output will be disproportionately determined by outliers. If ex-post output is multiplicative, then these outliers are precisely those cases where all of the inputs/factors have very high values.
So this could mean: total impact will be disproportionately due to people who are highly talented, have had lots of practice at a number of relevant skills, are highly motivated, work in a great & supportive environment, can focus on their job rather than having to worry about their personal or financial security, etc., and got lucky.
If this is right, then I think it adds interesting nuance on discussions around general mental ability (GMA). Yes, there is substantial evidence indicating that we can measure a ‘general ability factor’ that is a valid predictor for ~all more specific cognitive abilities. And it’s useful to be aware of that, e.g. b/c almost all extreme outlier performers in jobs that rely crucially on cognitive abilities will be people with high GMA. (This is consistent with many/most people having the potential to be “pretty good” at these jobs.) However, conversely, it does NOT follow that GMA is the only thing paying attention to. Yes, a high-GMA person will likely be “good at everything” (because everything is at least moderately correlated). But there are differences in how good, and these really matter. To get to the global optimum, you really need to allocate the high-GMA person to a job that relies on whatever specific cognitive ability they’re best at, supply all other ‘factors of production’ at maximal quality, etc. You have to optimize everything.
I.e. this is the toy model: Say we have ten different jobs, J1 to J10. Output in all of them is multiplicative. They all rely on different specific cognitive abilities C1 to C10 (i.e. each Ci is a factor in the ‘production function’ for Ji but not the otherJj). We know that there is a “general ability factor” g that correlates decently well with all of the Ci. So if you want to hire for any Ji it can make sense to select on g, especially if you can’t measure the relevant Ci directly. However, after you selected on g you might end up with, say, 10 candidates who are all high on g. It does NOT follow that you should allocate them at random between the jobs because ”g is the only thing that matters”. Instead you should try hard to identify for any person which Ci they’re best at, and then allocate them to Ji. For any given person, the difference between their Ci and Cj might look small b/c they’re all correlated, but because output is multiplicative this “small” difference will get amplified.
(If true, this might rationalize the common practice/advice of: “Hire great people, and then find the niche they do best in.”)
I think Kremer discusses related observations quite explicitly in his o-ring model. In particular, he makes the basic observation that if output is multiplicative you maximize total output by “assortative matching”. This is basically just the observation that if Y=LK and you have four inputs Lhigh,Llow,Khigh,Klow with Lhigh>Llow etc., then
LhighKhigh+LlowKlow>LhighKlow+LlowKhigh
- i.e. you maximize total output by matching inputs by quality/value rather than by “mixing” high- and low-quality inputs. It’s his explanation for why we’re seeing “elite firms”, why some countries are doing better than others, etc. In a multiplicative world, the global optimum is a mix of ‘high-ability’ people working in high-stakes environments with other ‘high-ability’ people on one hand, and on the other hand ‘low-ability’ people working in low-stakes environments with other ‘low-ability’ people. Rather than a uniform setup with mixed-ability teams everywhere, “balancing out” worse environments with better people, etc.
(Ofc as with all simple models, even if we think the multiplicative story does a good job at roughly capturing reality, there will be a bunch of other considerations that are relevant in practice. E.g. if unchecked this dynamic might lead to undesirable inequality, and the model might ignore dynamic effects such as people improving their skills by working with higher-skilled people, etc.)
[The following is a lightly edited response I gave in an email conversation.]
My overall intuition is that the full picture we paint suggests personal fit, and especially being in the tail of personal fit, is more important than one might naively think (at least in domains where ex-post output really is very heavy-tailed). But also how to “allocate”/combine different resources is very important. This is less b/c of feedback loops specifically but more b/c of an implication of multiplicative models:
[So one big caveat is that the following only applies to situations that are in fact well described by a multiplicative model. It’s somewhat unclear which these are.]
If ex-post output is very heavy-tailed, total ex-post output will be disproportionately determined by outliers. If ex-post output is multiplicative, then these outliers are precisely those cases where all of the inputs/factors have very high values.
So this could mean: total impact will be disproportionately due to people who are highly talented, have had lots of practice at a number of relevant skills, are highly motivated, work in a great & supportive environment, can focus on their job rather than having to worry about their personal or financial security, etc., and got lucky.
If this is right, then I think it adds interesting nuance on discussions around general mental ability (GMA). Yes, there is substantial evidence indicating that we can measure a ‘general ability factor’ that is a valid predictor for ~all more specific cognitive abilities. And it’s useful to be aware of that, e.g. b/c almost all extreme outlier performers in jobs that rely crucially on cognitive abilities will be people with high GMA. (This is consistent with many/most people having the potential to be “pretty good” at these jobs.) However, conversely, it does NOT follow that GMA is the only thing paying attention to. Yes, a high-GMA person will likely be “good at everything” (because everything is at least moderately correlated). But there are differences in how good, and these really matter. To get to the global optimum, you really need to allocate the high-GMA person to a job that relies on whatever specific cognitive ability they’re best at, supply all other ‘factors of production’ at maximal quality, etc. You have to optimize everything.
I.e. this is the toy model: Say we have ten different jobs, J1 to J10. Output in all of them is multiplicative. They all rely on different specific cognitive abilities C1 to C10 (i.e. each Ci is a factor in the ‘production function’ for Ji but not the otherJj). We know that there is a “general ability factor” g that correlates decently well with all of the Ci. So if you want to hire for any Ji it can make sense to select on g, especially if you can’t measure the relevant Ci directly. However, after you selected on g you might end up with, say, 10 candidates who are all high on g. It does NOT follow that you should allocate them at random between the jobs because ”g is the only thing that matters”. Instead you should try hard to identify for any person which Ci they’re best at, and then allocate them to Ji. For any given person, the difference between their Ci and Cj might look small b/c they’re all correlated, but because output is multiplicative this “small” difference will get amplified.
(If true, this might rationalize the common practice/advice of: “Hire great people, and then find the niche they do best in.”)
I think Kremer discusses related observations quite explicitly in his o-ring model. In particular, he makes the basic observation that if output is multiplicative you maximize total output by “assortative matching”. This is basically just the observation that if Y=LK and you have four inputs Lhigh,Llow,Khigh,Klow with Lhigh>Llow etc., then
LhighKhigh+LlowKlow>LhighKlow+LlowKhigh
- i.e. you maximize total output by matching inputs by quality/value rather than by “mixing” high- and low-quality inputs. It’s his explanation for why we’re seeing “elite firms”, why some countries are doing better than others, etc. In a multiplicative world, the global optimum is a mix of ‘high-ability’ people working in high-stakes environments with other ‘high-ability’ people on one hand, and on the other hand ‘low-ability’ people working in low-stakes environments with other ‘low-ability’ people. Rather than a uniform setup with mixed-ability teams everywhere, “balancing out” worse environments with better people, etc.
(Ofc as with all simple models, even if we think the multiplicative story does a good job at roughly capturing reality, there will be a bunch of other considerations that are relevant in practice. E.g. if unchecked this dynamic might lead to undesirable inequality, and the model might ignore dynamic effects such as people improving their skills by working with higher-skilled people, etc.)