I have long suspected that the problem of bad writing in movies is largely driven by questions of completeness and adaptation. For example:
We rarely see the whole story. Even if it is was shot exactly as written, what we wind up seeing is an edited-down cut of the film; which chunks of the writing get left out makes a big difference to me in my perception of the writing. Consider the case of the character that suddenly sprouts new abilities, which is shit writing. Watching a later director’s cut, they often include the scene which includes the crucial explanation of why they have these.
The writing doesn’t stay the same over the course of filming. There may be practical impediments to a key scene, like the weather ruining outdoor shots; it may prove infeasible to get a good enough set/costume/stunt arrangement to drive a part of the story; maybe the actor just can’t pull it off to save their lives. This necessitates re-writes. I strongly expect these to lack the coherency of the original screenplay, because now there are lots of people with input rather than the group accepting a completed script with a single author.
I feel like there is no conflict here; in fact it is widely considered a deal-breaker for a client to be guilty and lie to their attorney about it. A client lying to a lawyer is one of the ethically accepted reasons to dump a client they have already agreed to serve. This isn’t even pro-forma; in practice, lawyers don’t blame one another for dumping clients that lie to them. Nor is it considered a black mark for future hiring with other law firms.
The important variables here are that the lawyer is constrained by the evidence, but they have a duty to their client. This is because lawyers are not fact finders; they are advocates. The American trial system employs the ‘arguments are soldiers’ system specifically and deliberately, then it has a lot of rules for setting a floor on how bad the arguments can be and relies on nominally-neutral third parties (a judge and/or jury) to assess them.
Consider that a lawyer can represent themselves, their family, or parties in whom they have a financial stake without conflict of interest. However it is considered a conflict of interest if they have a financial stake in the other party, or anything else that might compromise their commitment to advocacy of their client.
So at least in the American system, I put it to you that accepting the case with total certainty your client is guilty is both ethical and rational.
I am not versed in economics literature, so I can’t meet your need. But I have also encountered ergodicity economics, and thought it was interesting because it had good motivations.
I am skeptical for an entirely different reason; I encountered ergodic theory beforehand in the context of thermodynamics, where it has been harshly criticized. I instinctively feel like if we can do better in thermodynamics, we can employ the same math to do better in other areas.
Of course this isn’t necessarily true: ergodic theory might cleave reality better when describing an economy than gas particles; there is probably a significant difference between the economics version and the thermodynamics version; the criticism of ergodic theory might be ass-wrong (I don’t think it is, but I’m not qualified enough for strong confidence).
Okay that makes sense, but now I’m confused on exactly how the real prices relate to the predictions. I expect the details of that mechanism to be the crux of the issue; exactly how the price updating is done will determine who the winners and losers are relative to the desired outcome.
It still feels like solving that problem well would be tantamount to solving the unimproved value problem, but I’m perfectly happy to be wrong.
A lens is a more structured form of perspective, the way we use the term in the community; the emphasis is on being able to move between different ones. We tend to use it for different analytical frameworks (CS, econ, engineering, finance, etc).
The industrial facility effects will be variable depending on what the facility is. For example, a nuclear power plant or a microchip factory will tend to increase property values because of an influx of good paying jobs but a coal power plant or a meat factory will tend to decrease property values because they reek and are miserable to live near.
But the broader point is that the predictions are based on the price history of the last 10 years, when there was no such major price impact. I expect the prediction software to continue predicting stable prices, which means the people who live with a new meat factory are paying taxes based on too high a value, and the people who live next to a new microchip factory are paying based on too low a value. These would eventually even out as the actual sales enter the price history, but this is a long lead time. I also expect that the price would be distorted by the understanding that some places are tax bargains, and some tax banes, which would extend the time for the predictions to correct back to true value.
It looks to me like the same mechanism that preserves the incentive to improve your land works to exclude significant changes in land value more generally; all the directions I can envision for solving this look suspiciously like solving the problem of assessing the unimproved value of land.
I appreciate first stabs at improvements in governance; upvoted. Would I be correct in inferring you are thinking about this largely through a computer science lens, rather than an economic one?
What do you think about dealing with price discontinuities? I have in mind things like: major housing development in formerly rural areas due to urban expansion; the construction of a new industrial facility; the discovery of natural resources beneath the land; the major local industry collapsing; critical infrastructure failures like the water supply being contaminated.
I’m confused about the relationship between the bids on the property (the value) and the output of the prediction engine (the predicted value) as a consequence of the above. It looks like the prediction software is designed to exclude price discontinuities in its value estimate, which is how it preserves the incentive to improve the land; but this means we are systematically predicting wrong on purpose, which feels weird. The bids themselves don’t appear to have any importance.
I think this is captured in Section 5, the Maximum Caliber Principle:
We are given macroscopic information A which might consist of values of several physical quantities . . . such as distribution of stress, magnetization, concentration of various chemical components, etc. in various space time regions. This defines a caliber . . . which measures the number of time dependent microstates consistent with the information A.
So the idea is that you take the macro information A, use that to identify the space of possible microstates. For maximum rigor you do this independently for A and B, and if they do not share any microstates then B is impossible. When we make a prediction about B, we choose the value of B that has the biggest overlap with the possible microstates of A.
He talks a little bit more about the motivation for doing this in the Conclusion, here:
We should correct a possible misconception that the reader may have gained. Most recent discussions of macrophenomena outside of physical chemistry concentrate entirely on the dynamics (microscopic equations of motion or an assumed dynamical model at a higher level, deterministic or stochastic) and ignore the entropy factors of macrostates altogether. Indeed, we expect that such efforts will succeed fairly well if the macrostates of interest do not differ greatly in entropy.
Emphasis mine. So the idea here is that if you don’t need to account for the entropy of A, you will be able to tackle the problem using normal methods. If the normal methods fail, it’s a sign that we need to account for the entropy of A, and therefore to use this method.
I can’t do the physics examples either except in very simple cases. I am comforted by this line:
Although the mathematical details needed to carry it out can become almost infinitely complicated...
This is glorious. On the flip side of the coin, I struggle with outrage that we had copped to the problem of presenting information and basically had it licked in the middle of the 19th century, and then apparently systematically purged such knowledge during the 20th. For example, there’s this interesting piece about Emma Willard, who drew gorgeous visuals providing perspective to history. She began in ~1837. Good use of images seems only now to be undergoing a renaissance, and that owing to the availability of computer graphics more than anything else.
What the devil happened erstwhile?
Well that sucks. Take care of yourself and stay sane during isolation!
I feel like this is evidence for the natural experiment interpretation. This means we will get a steady stream of new findings as each maturation window approaches, for decades to come.
To be more exact, if you have a group, then the group provides social incentives; but social incentives do not imply a group. For example, if I were publicly humiliated in front of strangers, they might mock me if they saw me later in a restaurant. This is a social (dis)incentive, but the fact remains that we aren’t in a group.
What qualifies people as a group in the sense that I intend is at least twofold: they have to share the same set of incentives; this fact has to be common knowledge among them.
I do agree that if person trains successfully it would improve long-run discipline, but doing military training won’t meaningfully change the outcome from non-military training because the group context is what does the extra work. If that is not the focus, ie veterans are just an example disciplined population, then my comments are probably not relevant to the true concern.
It seems to me precisely the opposite: my reading is that Benquo is driving exactly at how to talk about the problem of systemic falsification of information.
If the post is noncentral, what is the central thing instead?
I am a veteran, and my inside view suggests two things: one, the least disciplined members of the population are filtered out by the military (which is to say they are not accepted or kicked out early); two, the military experience pushes veterans towards the extremes.
Reasons to consider that veterans would be more productive than average:
Acclimated to long and/or strenuous work periods.
Better access to education through veterans programs and admission boosts.
Direct boost to employability in a variety of industries.
Reasons to consider that veterans would be less productive than average:
Higher rates of homelessness
Higher rates of mental illness and suicide
Higher rates of substance abuse
My expectation is that the productivity advantage is highest when veterans enter a civilian industry that matches military tasks closely, like compliance with regulations or uncomfortable work environments. I also expect that the veterans who fail to re-adapt to civilian life suffer an almost complete collapse of productivity.
Turning to the question of discipline, I think we will benefit from a little context. Discipline in the military is very much a team phenomenon; Army training is focused overwhelmingly on establishing and maintaining a group identity. Most of the things people associate with military discipline require other people to make sense, like the chain of command, pulling security, and how tasks are divided. Even the individual things like physical fitness or memorizing trivia, are thoroughly steeped in the team environment because they are motivated by being able to help your buddy out and are how status is sorted in the group.
I believe your friend’s statement:
If you can just train yourself like you’re in the army, then you can become just as self disciplined as a soldier
is wrong as a consequence, because you can never train yourself like you are in the Army. That fundamentally needs a group, entirely separate from the question of social incentives and environment. Outside of the group context, discipline doesn’t really mean anything more than habit formation.
If the same type of facility works for almost every kind of vaccine, do we think there would be interest in constructing the facilities as a speculative venture? Consider:
1. The economy is in chaos and may remain so, which I expect to produce unusually affordable access to design firms, construction crews, raw materials, and land.
2. There will be a strong incentive for regulators/inspectors to move with best speed, and the current administration at least in the US has a track record of being friendly to shortcuts.
3. If the facilities are already built, this allows a limit to the risk the companies producing the vaccines need to absorb in order to increase supply.
4. We could squeeze out unscrupulous opportunists.
My model for this is that China is achieving success largely by ignoring externalities. Environmental pollution is a prime example, like in the case of their previous recycling policy and mining of rare earth minerals. It is actually against the law for the US to build as quickly or as cheaply as China, but this is reasonably motivated by trying to account for things like pollution and safety, and avoiding things like resettling entire towns.
Chinese success looks a lot like the WWII and postwar years in the US, and for much the same reasons.
because why was it that the conquistadors were able to exploit the locals and not the other way around?
Have you considered the possibility that it was a case of mutual exploitation? The Aztec allies of the conquistadors weren’t there out of the goodness of their hearts; they had found a new angle that would help them defeat Tenochtitlan. They lost the post-victory power struggle, but it was always going to be someone.
You make good points here. Any ideas why those other shifts happened and how can we help reverse them or prevent them from happening elsewhere?
Mostly it looks to me like a series of unrelated changes built up over time, and the unintended consequences were mostly adverse.
An example is the War on Cancer and the changes that came with it to funding. It had long been the case that funding was mostly handed out on a project-by-project basis, but in order to get the funding dedicated to cancer research it was necessary to explain how cancer research would benefit. The obvious first-order impact is an increase in administrative overhead for getting the money.
Alongside this science sort of professionalized. I expect that when the sense of how important something is permeates, professionalization is viewed as a natural consequence, but it seems to have misfired here. Professionalization, like other forms of labor organization, isn’t about maximizing anything but about ensuring a minimum. This means things like more metrics, which is why our civilization formally prefers a lot of crappy scientific papers to a few good ones, and doesn’t want any kind of non-paper presentation of scientific progress at all. Science jobs become subject to Goodharting, because people start thinking that the right way to get more science is just to increase the number of scientists, on account of them all being interchangeable professionals with a reliable minimum output.
The university environment also got leaned on as a lever for progress; the student loan programs all grew over this same period, which seems to have driven a long period of competition for headcount. This shifted universities’ priorities from executing their nominal mission towards signalling desirability among students/parents/etc. I am certain at least part of that came at the expense of faculty, even if only by increasing the administrative burden still further by yet more metrics.
On the fixing side, I am actually pretty optimistic. A few simple things would probably help a lot, two examples being funding and organization. Example: Bell Labs and Xerox PARC have been discussed here a lot. Both cases deviated significantly from the standard university/government system of funding individual projects case by case. Under the project/grant system being a scientist reduces to being able to successfully get funding for a series of projects over time. At Bell and at PARC, they rather made long-term investments on a person-by-person basis. I think this has wide-ranging effects, but not least among them is that there wasn’t a lot of administrative overhead to a given investigation; rather they could all be picked up, put down, or adapted as needed. Another effect, maybe intentional but seemingly happenstance, is that they built a community of researchers in the colloquial sense. This is pretty different from the formal employee relationships that dominate now. Around 7 years ago I listened to a recruiting pitch from Sandia National Laboratories for engineering students, and asked how communication was between different groups in the lab. The representative said that she knew of a case where two labs right across the hall from each other were investigating the same thing for over a year before they realized it, because nobody talks.
This suggests to me that a university that was struggling financially, or maybe just needed to take a gamble on moving up in the world, could cheaply implement what appears to be a superior research-producing apparatus, just by shifting their methods of funding and tracking results.
Are math proofs useful at all for writing better algorithms? I saw on Reddit recently that they proved Batchelor’s Law in 3D, the core idea of which seems to be using stochastic assumptions to prove it cannot be violated. The Quanta article does not seem to contain a link to the paper, which is weird.
Batchelor’s Law is the experimentally-observed fact that turbulence occurs at a specific ratio across scales, which is to say when you zoom in on a small chunk of the turbulence it looks remarkably like all of the turbulence, and so on. Something something fractals something.
Looking up the relationship between proofs and algorithms mostly goes to proofs about specific algorithms, and sometimes using algorithms as a form of proof; but what I am after is whether a pure-math proof like the above one can be mined for useful information about how to build an algorithm in the first place. I have read elsewhere that algorithmic efficiency is about problem information, and this makes intuitive sense to me; but what kind of information am I really getting out of mathematical proofs, assuming I can understand them?
I don’t suppose there’s a list somewhere that handily matches tricks for proving things in mathematics to tricks for constructing algorithms in computer science?