There’s a big problem with the Eye Part metaphor right at the beginning, which propagates through many ideas/examples in the rest of the post: the real world is high-dimensional.
The Eye Part metaphor imagines three types of organisms, all arranged along one dimension: No Eye → Eye Part 1 → Eye Part 2 → Whole Eye. In that picture, the main problem in getting from No Eye to Whole Eye is just getting “over the hill”.
But the real world doesn’t look like that. Evolution operates in a space with (at least) hundreds of thousands of dimensions—every codon in every gene can change, genes/chunks of genes can copy or delete, etc. The “No Eye” state doesn’t have one outgoing arrow, it has hundreds of thousands of outgoing arrows, and “Eye Part 1″ has hundreds of thousands of outgoing arrows”, and so forth.
As we move further away from the starting state, the number of possible states increases exponentially. By the time we’re as-far-away as Whole Eye (which, in practice, is a lot more than three steps), the number of possible states will outnumber the atoms in the universe. If evolution is uniformly-randomly exploring that space, without any pressure toward the Whole Eye state specifically, it will not ever stumble on the Whole Eye—no matter how much slack it has.
Point is: the hard part of getting from “No Eye” to “Whole Eye” is not the fitness cost in the middle, it’s figuring out which direction to go in a space with hundreds of thousands of directions to choose from at every single step.
Conversely, the weak evolutionary benefits of partial-eyes matter, not because they grant a fitness gain in themselves, but because they bias evolution in the direction toward Whole Eyes.
Let’s apply that to some of the examples.
Tariffs example: from the perspective of a policy-maker, the hard part of evolving successful companies is not giving them plenty of slack, it’s figuring out which company-designs are actually likely to succeed. If policy makers just grant unconditional slack, then they’ll end up with random company-designs, and the exponentially-vast majority of random company-designs suck. They need to strategically select company-designs which will work if given slack. I don’t have a reference, but IIRC, both Korean and Japanese policy makers did think about the problem this way.
Monopolies/Research example: the hard part of a successful research lab is not giving plenty of slack, it’s figuring out which research-directions are actually likely to succeed. If management just funds research indiscriminately, then they’ll end up with random research directions, and the exponentially-vast majority of random research directions suck. Xerox and Bell worked in large part because they successfully researched things targeted toward their business applications—e.g. programming languages and solid-state electronics.
Rand/Sears: the hard part of a successful company is not giving internal components plenty of slack, it’s figuring out what each component needs to do in order to actually be useful to the company (i.e. alignment). A good example here is Amazon: they try to expose all of their internal-facing products to the external world. That’s how Amazon’s cloud compute services started, for instance: they sold their own data center infrastructure to the rest of the world. That exposes the data center infrastructure to ordinary market pressure, and forces them to produce a competitive product. The external market tells the data infrastructure team “which direction to go” in order to be maximally-useful. On the other hand, if Amazon’s data center team had to compete with the warehouse team without external market pressure, then we’d effectively have two monopolies bargaining—not actually market competition at all.
Thus reminds me of the machine learning point that when you do gradient descent in really high dimensions, local minima are less common than you’d think, because to be trapped in a local minimum, every dimension has to be bad.
Instead of gradient descent getting trapped at local minima, it’s more likely to get pseudo-trapped at “saddle points” where it’s at a local minimum along some dimensions but a local maximum along others, and due to the small slope of the curve it has trouble learning which is which.
If management just funds research indiscriminately, then they’ll end up with random research directions, and the exponentially-vast majority of random research directions suck. Xerox and Bell worked in large part because they successfully researched things targeted toward their business applications—e.g. programming languages and solid-state electronics.
That said, I think there’s still a compelling point in slack’s favor here; my impression is that Bell Labs (and probably Xerox?) put some pressure on people to research things that would eventually be helpful, but put most of its effort into hiring people with good taste and high ability in the first place.
That sounds plausible; hiring people with good taste and high ability is also a good way to filter out the exponentially-vast number of useless research directions (assuming that one can recognize such people). That said, I wouldn’t label that a point in favor of slack, so much as another way of filtering. It’s still mainly solving the problem of “which direction to go” rather than “can we get over the hill”.
If you can’t recognize who’s already done some good work autonomously, how can you reasonably hope to extract good work from people who haven’t been selected for that?
Alan Key of Xerox Parc makes the argument that hiring great people and giving them freedom is a key to get good innovation and that’s the principle on which Parc worked.
The funders are only supposed to provide a vision but not goals which are supposed to be picked by individual researchers.
This shows why I don’t trust the categories. The ability to let talented people go in whatever direction seems best will almost always be felt as freedom from pressure.
From The Sources of Economic Growth by Richard Nelson, but I think it’s a quote from James Fisk, Bell Labs President:
If the new work of an individual proves of significant interest, both scientifically and in possible communications applications, then it is likely that others in the laboratory will also initiate work in the field, and that people from the outside will be brought in. Thus a new area of laboratory research will be started. If the work does not prove of interest to the Laboratories, eventually the individual in question will be requested to return to the fold, or leave. It is hoped the pressure can be informal. There seems to be no consensus about how long to let someone wander, but it is clear that young and newly hired scientists are kept under closer rein than the more senior scientists. However even top-flight people, like Jansky, have been asked to change their line of research. But, in general, the experience has been that informal pressures together with the hiring policy are sufficient to keep AT&T and Western Electric more than satisfied with the output of research.
[Most recently brought to my attention by this post from a few days ago]
There’s a big problem with the Eye Part metaphor right at the beginning, which propagates through many ideas/examples in the rest of the post: the real world is high-dimensional.
The Eye Part metaphor imagines three types of organisms, all arranged along one dimension: No Eye → Eye Part 1 → Eye Part 2 → Whole Eye. In that picture, the main problem in getting from No Eye to Whole Eye is just getting “over the hill”.
But the real world doesn’t look like that. Evolution operates in a space with (at least) hundreds of thousands of dimensions—every codon in every gene can change, genes/chunks of genes can copy or delete, etc. The “No Eye” state doesn’t have one outgoing arrow, it has hundreds of thousands of outgoing arrows, and “Eye Part 1″ has hundreds of thousands of outgoing arrows”, and so forth.
As we move further away from the starting state, the number of possible states increases exponentially. By the time we’re as-far-away as Whole Eye (which, in practice, is a lot more than three steps), the number of possible states will outnumber the atoms in the universe. If evolution is uniformly-randomly exploring that space, without any pressure toward the Whole Eye state specifically, it will not ever stumble on the Whole Eye—no matter how much slack it has.
Point is: the hard part of getting from “No Eye” to “Whole Eye” is not the fitness cost in the middle, it’s figuring out which direction to go in a space with hundreds of thousands of directions to choose from at every single step.
Conversely, the weak evolutionary benefits of partial-eyes matter, not because they grant a fitness gain in themselves, but because they bias evolution in the direction toward Whole Eyes.
Let’s apply that to some of the examples.
Tariffs example: from the perspective of a policy-maker, the hard part of evolving successful companies is not giving them plenty of slack, it’s figuring out which company-designs are actually likely to succeed. If policy makers just grant unconditional slack, then they’ll end up with random company-designs, and the exponentially-vast majority of random company-designs suck. They need to strategically select company-designs which will work if given slack. I don’t have a reference, but IIRC, both Korean and Japanese policy makers did think about the problem this way.
Monopolies/Research example: the hard part of a successful research lab is not giving plenty of slack, it’s figuring out which research-directions are actually likely to succeed. If management just funds research indiscriminately, then they’ll end up with random research directions, and the exponentially-vast majority of random research directions suck. Xerox and Bell worked in large part because they successfully researched things targeted toward their business applications—e.g. programming languages and solid-state electronics.
Rand/Sears: the hard part of a successful company is not giving internal components plenty of slack, it’s figuring out what each component needs to do in order to actually be useful to the company (i.e. alignment). A good example here is Amazon: they try to expose all of their internal-facing products to the external world. That’s how Amazon’s cloud compute services started, for instance: they sold their own data center infrastructure to the rest of the world. That exposes the data center infrastructure to ordinary market pressure, and forces them to produce a competitive product. The external market tells the data infrastructure team “which direction to go” in order to be maximally-useful. On the other hand, if Amazon’s data center team had to compete with the warehouse team without external market pressure, then we’d effectively have two monopolies bargaining—not actually market competition at all.
Thus reminds me of the machine learning point that when you do gradient descent in really high dimensions, local minima are less common than you’d think, because to be trapped in a local minimum, every dimension has to be bad.
Instead of gradient descent getting trapped at local minima, it’s more likely to get pseudo-trapped at “saddle points” where it’s at a local minimum along some dimensions but a local maximum along others, and due to the small slope of the curve it has trouble learning which is which.
That said, I think there’s still a compelling point in slack’s favor here; my impression is that Bell Labs (and probably Xerox?) put some pressure on people to research things that would eventually be helpful, but put most of its effort into hiring people with good taste and high ability in the first place.
That sounds plausible; hiring people with good taste and high ability is also a good way to filter out the exponentially-vast number of useless research directions (assuming that one can recognize such people). That said, I wouldn’t label that a point in favor of slack, so much as another way of filtering. It’s still mainly solving the problem of “which direction to go” rather than “can we get over the hill”.
If you can’t recognize who’s already done some good work autonomously, how can you reasonably hope to extract good work from people who haven’t been selected for that?
Alan Key of Xerox Parc makes the argument that hiring great people and giving them freedom is a key to get good innovation and that’s the principle on which Parc worked.
The funders are only supposed to provide a vision but not goals which are supposed to be picked by individual researchers.
This shows why I don’t trust the categories. The ability to let talented people go in whatever direction seems best will almost always be felt as freedom from pressure.
From The Sources of Economic Growth by Richard Nelson, but I think it’s a quote from James Fisk, Bell Labs President:
[Most recently brought to my attention by this post from a few days ago]