The flaw is the same; estimating a depreciation rate based on data points 5-6 years apart won't tell you whether there was an abrupt dip that took only a few days or weeks.
But comparing peak prices to prices years later does tell you that any 'abrupt dip' must have been compensated for by other price increases or maintenance of prices. If prices, from the peak, abruptly go down and then abruptly go up, and then follow their usual depreciation curve, that's not a very bubbly story.
Although I don't think the magnitude of the tulipmania has much bearing on whether tech stocks, Bitcoins, or real estate are/were bubbling; for those last three things, there are time series data that're a lot more relevant than what happened to Dutch tulip bulbs 376 years ago.
Sure. It drives me nuts how people constantly bring up Tulipomania. Whether or not one agrees with Garber's findings, it should still be obvious to them that arguing about modern finance based on Tulipomania is like trying to criticize American government based on ancient Greek politics - the sources are bad and don't answer the questions we want to know, and even if we did have perfect knowledge of what happened so long ago, the circumstances were so different and the world was so different that it can tell us very little about vaguely similar modern situations.
I also wonder whether I updated too much on the basis of one economist's contrarianism.
Maybe! I wonder that sometimes myself. But honestly, Tulipomania has the feel of one of those parables which are too good to be true, so I don't expect a later economist to come along and say 'everything you thought you knew from Garber is false! yes, the stuff about tulip-breaking virus is false! and tulip bulbs don't depreciate extremely fast! the futures contracts weren't canceled! there were no extenuating circumstances like plague!' etc
But comparing peak prices to prices years later does tell you that any 'abrupt dip' must have been compensated for by other price increases or maintenance of prices.
I don't follow. Garber's data are consistent with the scenario I sketched in the penultimate paragraph of this comment, where I assume away any compensation for the initial dip.
If prices, from the peak, abruptly go down and then abruptly go up, and then follow their usual depreciation curve, that's not a very bubbly story.
Yeah, Garber's data are also consistent with an initial rebound.
...
In an unrelated thread, one thing led to another and we got onto the subject of overpopulation and carrying capacity. I think this topic needs a post of its own.
TLDR mathy version:
let f(m,t) be the population that can be supported using the fraction of Earth's theoretical resource limit m we can exploit at technology level t
let t = k(x) be the technology level at year x
let p(x) be population at year x
What conditions must constant m and functions f(m,k(x)), k(x), and p(x) satisfy in order to insure that p(x) - f(m,t) > 0 for all x > today()? What empirical data are relevant to estimating the probability that these conditions are all satisfied?
Long version:
Here I would like to explore the evidence for and against the possibility that the following assertions are true:
Please note: I'm not proposing that the above assertions must be true, only that they have a high enough probability of being correct that they should be taken as seriously as, for example, grey goo:
Predictions about the dangers of nanotech made in the 1980's shown no signs of coming true. Yet, there is no known logical or physical reason why they can't come true, so we don't ignore it. We calibrate how much effort should be put into mitigating the risks of nanotechnology by asking what observations should make us update the likelihood we assign to a grey-goo scenario. We approach mitigation strategies from an engineering mindset rather than a political one.
Shouldn't we hold ourselves to the same standard when discussing population growth and overshoot? Substitute in some other existential risks you take seriously. Which of them have an expectation2 of occuring before a Malthusian Crunch? Which of them have an expectation of occuring after?
Footnotes:
1: By carrying capacity, I mean finite resources such as easily extractable ores, water, air, EM spectrum, and land area. Certain very slowly replenishing resources such as fossil fuels and biodiversity also behave like finite resources on a human timescale. I also include non-finite resources that expand or replenish at a finite rate such as useful plants and animals, potable water, arable land, and breathable air. Technology expands carrying capacity by allowing us to exploit all resource more efficiently (paperless offices, telecommuting, fuel efficiency), open up reserves that were previously not economically feasible to exploit (shale oil, methane clathrates, high-rise buildings, seasteading), and accelerate the renewal of non-finite resources (agriculture, land reclamation projects, toxic waste remediation, desalinization plants).
2: This is a hard question. I'm not asking which catastrophe is the mostly likely to happen ever while holding everything else constant (the possible ones will be tied for 1 and the impossible ones will be tied for 0). I'm asking you to mentally (or physically) draw a set of survival curves, one for each catastrophe, with the x-axis representing time and the y-axis representing fraction of Everett branches where that catastrophe has not yet occured. Now, which curves are the upper bound on the curve representing Malthusian Crunch, and which curves are the lower bound? This is how, in my opinioon (as an aging researcher and biostatistician for whatever that's worth) you think about hazard functions, including those for existential hazards. Keep in mind that some hazard functions change over time because they are conditioned on other events or because they are cyclic in nature. This means that the thing most likely to wipe us out in the next 50 years is not necessarily the same as the thing most likely to wipe us out in the 50 years after that. I don't have a formal answer for how to transform that into optimal allocation of resources between mitigation efforts but that would be the next step.