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why the logistics curve is appropriate ...
But there is: essentially, a logistics curve
says that the growth of [take your choice:]
* number of trans-Atlantic voyages of
discovery before 1700,
* number of Hitchock films,
* quantity of oil produced,
tends to increase based on previous actions, but
is held back by the increasing amount learned or
the increasing difficulty of finding and doing.
Mostly, people look at it as an `S' shaped
growth curve. In biological models, the maximum
comes from the carrying capacity of the niche.
It is somewhat odd to see linearization.
Usually, graphs are for an `S' or a bell.
I don't know of an equal mechanical argument for
an oil-based Gaussian. The mechanical argument
for a Gaussian is that a central value has
errors that are equal on both sides and that
occur less frequently the further away from the
central value. Gauss invented the curve for
analysing astronomical observations. He figured
that a celestial object was in a defined orbit,
but that astronomers made mistakes or lacked
good equipment, but did not try to sway their
observations one way or another.
A 15 year old book,
The Rise and Fall of Infrastructures
Dynamics of Evolution and Technological
Change in Transport by Arnulf Grübler
reprinted 1999, ISBN 3-7045-0135-2
gives examples of numbers of cars registered,
kilometers of roads blacktopped, and the like.
It uses logistic curves extensively.
Theodore Modis wrote a book on logistic curves
called "Predictions" (that is where I got the
choices listed above). That book was copyright in
1992, ISBN 0-617-75917-5
Modis quantified the uncertainties in
determining logistic curve fits, given just the
beginning of a curve. This could be useful.
The logistics curve has a logical mechanical reason for applying to bacterial growth where in an unconstrained situation the rate of increase is proportional to the current bacteria population and the availability of the constraining resource.
However for oil production it isn't the past cumulative historical production (Q=sum(P)) that determines the rate of production growth but the current size of the exploration industry (I). Here is an alternative simple model that has to my mind some more relevant significance to the terms.
dI/dt = (h(Qt-Q)-j)I,
dP/dt= r(Qt-Q)I - nP,
where:
r is a parameter related to the exploratory success rate
n is the average infield decline rate
j is a depreciation factor
h is a parameter associated with the insentives to increase exploratory effort.
If we add some apropriate parameters this also forms a nice bell curve. Is this a better model than the logistic curve? I couldn't say, but it does have the advantage that it makes some mechanistic sense.
I(t) = Discoveries,
dQ/dt = I(t) - n Q(t),
P(t) = a Q
I go through a few more 1st-order transforms because you have to consider latencies corresponding to fallow periods, construction periods, and maturation periods. I have the math all worked out, have the source code to do the numerical integration, and it basically looks like this if you assume that the Discoveries curve follows a quadratic growth curve for the USA (peaking after 1930)
That blue curve comes out to a quadratic (i.e. peaked parabola) convolved with a 4-th order gamma curve (i.e. 4 exponentials of the same rate convolved sequentially). It accurately maps over a range 5 orders of magnitude.
http://mobjectivist.blogspot.com/2006/01/would-you-believe.html