How well did it handle tech stocks in 1999?
How high are the stock prices of Amazon, Google, and Apple now?
Bit of a glib response. (One could ask, equally rhetorically, "How high are the stock prices of Tiscali, lastminute.com, and InfoSpace/Blucora now?") But since you elaborated below with actual arguments I won't press this point.
Tulip bulbs back in the day?
Everything you know about Tulipomania is false or incomplete. I suggest reading Famous First Bubbles.
Does the book go beyond Garber's papers on tulipmania? My reading of Garber's argument in those papers is:
Most people get their ideas about the tulip market from Charles Mackay, but he plagiarized his account, and it ultimately comes from "three anonymously written pamphlets".
Mackay exaggerated, among other things, the amount of national-level economic distress resulting from the tulip mania.
"Mackay did not report transaction prices for the rare bulbs immediately after the collapse", which are the prices one would need to establish the popping of a bubble. Instead he quoted high prices from before the bubble popped, and prices "from 60 or 200 years after the collapse". But what he found could be consistent with the prices accurately reflecting changes in fundamentals. Why? Because a new & attractive variety of flower might gradually come into fashion (raising its price) and then suffer a glut over time as more bulbs become available (lowering its price).
One can confirm that's how things normally worked by looking at changes over time in prices long after the bubble. Even in non-bubble times, bulb prices would consistently start high and then fall steadily.
I don't disagree with those claims, as far as they go, but highlighting a lack of conclusive evidence for a bubble doesn't mean there wasn't a bubble. Even Garber's seemingly damning review of the price data doesn't mean much, because Garber (like Mackay) fails to quote prices from immediately after the collapse.
What Garber actually does is calculate that tulip bulb prices depreciated by 24%-76% per year over the 5-6 years after the peak. He compares that to the 2%-40% annual depreciation of bulb prices in the next century, says the earlier rates are only modestly higher than the later rates, and so there wasn't a bubble-indicating deviation from normal depreciation.
But Garber would likely have seen the same thing even if there had been an abrupt bubble pop. Suppose a tulip bulb's price peaked at 1000 guilders, crashed to 200 guilders within a week, then sank gradually to 100 guilders over the next five years. An economist who, knowing only the start & end points, interpolated to estimate the annual depreciation would (if I've done the sums right) get a 37% rate, which gives no sign of the initial crash. Observing a normal depreciation rate isn't good evidence against a bubble; one has to know prices closer to the event.
Does Garber's book have those data, or at least a novel argument missing from his papers?
Bit of a glib response.
Yes, but it hopefully wakes up people who glibly point at one stock or one price change as proof positive of bubbles: the claim for bubbles is a long-term statistical claim, and cannot be supported by simply going "Tulips!"
Does the book go beyond Garber's papers on tulipmania?
I don't know. Not really interested in taking the time to compare them in detail. Presumably the book form includes much more detail than space-restricted papers.
...I don't disagree with those claims, as far as they go, but highlighting a lack of
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.