This module provides a mathematical model for the study of world population growth. It compares the "natural" and "coalition" differential equation models as possible descriptions of the growth pattern.
About this Module and its Authors
Click on the button corresponding to your preferred computer algebra system (CAS). This will download a file which you may open with your CAS.
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Published December, 2001
This module provides a mathematical model for the study of world population growth. It compares the "natural" and "coalition" differential equation models as possible descriptions of the growth pattern.
About this Module and its Authors
Click on the button corresponding to your preferred computer algebra system (CAS). This will download a file which you may open with your CAS.
Ver. 6 or higher |
Ver. 3.0 or higher |
Ver. 5.1 or higher |
Published December, 2001
Only in the 20th century has it become possible to make reasonable estimates of the entire human population of the world, current or past. The following table lists some of those estimates, based in part on data considered "most reliable" in a 1970 paper and in part on both overlapping and more recent data from the U. S. Census Bureau. Of course, the earliest entries are at best educated guesses. The later entries are more likely to be correct -- at least to have the right order of magnitude -- but you should be aware that there is no "world census" like the decennial U. S. census, in which an attempt is made to count every individual in this country.
Year (CE) |
Population |
Year |
Population |
|
1000 | 200 | 1940 | 2295 | |
1650 | 545 | 1950 | 2517 | |
1750 | 728 | 1955 | 2780 | |
1800 | 906 | 1960 | 3005 | |
1850 | 1171 | 1965 | 3345 | |
1900 | 1608 | 1970 | 3707 | |
1910 | 1750 | 1975 | 4086 | |
1920 | 1834 | 1980 | 4454 | |
1930 | 2070 | 1985 | 4850 |
The natural growth model for biological populations suggests that the growth rate is proportional to the population, that is,
dP/dt = k P,
where k is the productivity rate, the (constant) ratio of growth rate to population. We know that the solutions of this differential equation are exponential functions of the form
P = P0 ekt,
where P0 is the population at whatever time is considered to be t = 0.
In 1960 Heinz von Foerster, Patricia Mora, and Larry Amiot published a now-famous paper in Science (vol. 132, pp. 1291-1295). The authors argued that the growth pattern in the historical data can be explained by improvements in technology and communication that have molded the human population into an effective coalition in a vast game against Nature -- reducing the effect of environmental hazards, improving living conditions, and extending the average life span. They proposed a coalition growth model for which the productivity rate is not constant, but rather is an increasing function of P, namely, a function of the form kPr, where the power r is positive and presumably small. (If r were 0, this would reduce to the natural model -- which we know does not fit.) Since the productivity rate is the ratio of dP/dt to P, the model differential equation is
dP/dt = k Pr+1.
In Part 3 we consider the question of whether such a model can fit the historical data. Note that there is no particular scientific reason to expect that "coalition" will be expressed in a growth rate that is proportional to a power of the population. This is merely the simplest model for expressing the observation that the historical growth has been faster than the exponential model would suggest.
The von Foerster paper argues that the differential equation modeling growth of world population P as a function of time t might have the form
dP/dt = k P1+r,
where r and k are positive constants. Before attempting to solve this differential equation, we explore whether it can reasonably represent the historical data.
The model asserts that the rate of change (derivative) of P should be proportional to a power of P, that is, the rate of change should be a power function of P. We can test that assertion by looking at a log-log plot of dP/dt versus P. But first we have to estimate the rate of change from the data. We do this by calculating symmetric difference quotients.
We now take up the solutions of the coalition model and consider the implications of faster-than-exponential growth. We may separate the variables in the differential equation
dP/dt = k P1+r
to write it in the form
P-(1+r) dP = k dt.
Then we may integrate both sides to get an equation relating P and t.
This calculation shows that there is a finite time T at which the population P becomes infinite -- or would if the growth pattern continues to follow the coalition model. The von Foerster paper calls this time Doomsday.
It's clear that Doomsday hasn't happened yet. To assess the significance of the population problem, it's important to know whether the historical data predict a Doomsday in the distant future or in the near future. We take up that question in the next Part.
The parameter T in the model function
P = 1/[r k (T - t)]1/r
is obviously important. It is just as obviously not directly observable from measurements or estimates of population. However, we can take logs of both sides of this equation to find the equivalent form
log P = (-1/r) [log (rk) + log (T - t)]
If this model fits the data (and we have seen some evidence that it does), then we should find that log P is a linear function of log (T - t).
While the Doomsday authors wrote with their tongues firmly in their respective cheeks, it's rather remarkable that they also constructed the most accurate predictor of real population growth for almost two generations. Historical data is a good predictor when the behavior that produced it does not change. Only now are we beginning to see any substantial change in this behavior on a global scale.
Here are some additional links for further study: