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 conclusive evidence for a bubble doesn't mean there wasn't a bubble.
Given how many people cite Tulipomania as a irrefutable smackdown in these sorts of discussions ('Bitcoins are worthless - at least you could plant tulips!'), learning that there is minimal evidence for what is popularly considered to be a large, irrefutable, historically established, unquestionable bubble should badly damage one's confidence in other claims relating to bubbles since it tells one a lot about what passes for evidence in those discussions.
Observing a normal depreciation rate isn't good evidence against a bubble; one has to know prices closer to the event.
It's been a while since I read the book, but doesn't he do exactly that and does compare depreciation from peak prices in places? For example, on pg64 of my copy:
Even from the peaks of February 1637, the price declines of the rarer bulbs, English Admiral, Admiral van der Eyck, and General Rotgans, over the course of six years was not unusually rapid. We shall see below that they fit the pattern of decline typical of a prized variety...Prices for these bulbs declined at an average annual percentage rate of 28.5 percent. From table 9.1, the three costly bulbs of February 1637 (English Admiral, Admirael van der Eyck, and General Rotgans) had an average annual price decline of 32 percent from the peak of the speculation through 1642. Using the eighteenth-century price depreciation rate as a benchmark also followed by expensive bulbs after the mania, we can infer that any price collapse for rare bulbs in February 1637 could not have exceeded 16 percent of peak prices. Thus, the crash of February 1637 for rare bulbs was not of extraordinary magnitude and did not greatly affect the normal time series pattern of rare bulb prices.
Presumably the book form includes much more detail than space-restricted papers.
I'd hope so, although I can imagine an academic padding things out with irrelevant side detail or other yakkety-yak-yak. In those cases one may as well stick with the papers.
Observing a normal depreciation rate isn't good evidence against a bubble; one has to know prices closer to the event.
It's been a while since I read the book, but doesn't he do exactly that and does compare depreciation from peak prices in places? For example, on pg64 of my copy:
Not based on that ...
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.