On Progressive Risk-Taking

When I was a young driver, I discovered, to my delight, that the car was narrower than my perception from the driver’s seat.   I found that the car could fit through narrow spaces; I could thread the needle past obstacles in alleys and parking lots.  My confidence grew as I successfully navigated through difficult places.  Until, at last, the inevitable happened.  I tried to drive between a lamp-post and a pickup truck.  The car did not fit.

That’s the nature of progressive risk-taking.   In some situations, we receive no incremental feedback; no warning that things are about to go disastrously wrong.   Outcomes are discrete values of positive or negative, and repeated positive experience only encourages more risk-taking.   [Example: “Well, you didn’t get pregnant the LAST time….”]

A beautiful example of progressive risk-taking is the TV game show “Deal—or No Deal”.   There are 32 suitcases bearing prizes, and a highly skewed statistical distribution of prizes.  In the early rounds, it is a virtual certainty that the contestant will increase the mean (expected) value of his prize by guessing and eliminating suitcases of low value.   Players gain confidence from their early success.  But abruptly, and without warning, the game changes.   Suitcases with high value are inevitably eliminated.   The mean value of the prize declines.   The player, filled with confidence from the early rounds, persists.  Generally, he plays to destruction, to the absolute end of the game where the statistical chance of a substantial prize is very low.   Early success is seductive, and it encourages poor decisions in absolute defiance of all evidence and reason.

Progressive risk-taking is exacerbated when operating outside of the envelope of previous experience.

On a cold Florida morning in 1986, mission controllers decided to launch the Space Shuttle Challenger, despite objections from project engineers.     With a history of 50 successful launches: "What could go wrong?"   But the temperatures that day were without precedent in the shuttle program, and outside of the range for which components had been tested.   Outside of the envelope of experience, the prior record of success is meaningless; the probability of failure cannot be calibrated.  The result was a disaster which scarred a generation, and devastated the American space program.

Humanity is now engaged in a great experiment, outside of the envelope of previous experience.   Near the end of October, 2011, world population will exceed 7 billion people, as compared to about 1.6 billion people in the year 1900.   To date, human ingenuity and technology have enabled mankind to feed and shelter the growing population.   Within the past 50 years, in fact, standards of living, lifespan and health have improved dramatically, coincident with industrialization and rising per capita GDP.  

However, the forecast population is outside of the envelope of previous experience.  Some of the technologies that have enabled the current population, such as irrigation from fossil aquifers, are not sustainable.   We cannot simply assume that technology will always solve the problems of growing population, simply because we have been successful in the past.   There will come a point where we can no longer squeak through the gap.

The Limits to Growth

I just finished reading "The Limits to Growth", 1972 edition, which I should have read a long time ago.  It's an excellent book.   The principal authors were Dennis and Donella Meadows, Carleton College '64 and '63, respectively (yay, Carleton).

The book presents the results of a world economic and social computer simulation.  At the time of the 1972 model development, the authors did not claim that the model was either predictive or quantitative.  Instead, the focus was simply to construct the model and observe the behavior of the simulation.  After the "standard run" base case was created, the model was perturbed in various ways, and the variation between runs was considered.  Model inputs matched historical data from 1900 to 1970, and matched the exponential rates of growth observed through most of the 20th century.  The number of parameters was very small:  Population, Food Supply, Arable Land, Non-renewable Natural Resources, Industrial Production, Pollution, etc.  Feedback loops were created which established relationships between the parameters, and provided for limits on exponential growth.

In almost every case considered, population, food supply, and industrial production rise to a peak, and then fall, sometimes catastrophically, before the end of the 21st century.

According to the 1972 model, world population was forecast to reach 7 Billion by about 2017.   According to current UN estimates, population will reach 7 Billion earlier, sometime in October, 2011.  The LTG estimate was pretty good for a model that was not intended to be predictive or quantitative.  However, we can note some changes in the current picture, as compared to the simulation from 1972.   First, global birth rates have declined much faster than assumed in the model runs.  Secondly, lifespan has increased faster then the model, and I believe that global wealth has risen higher and faster than imagined in the model.  I also believe there we have a much greater ability to expand production of non-renewable resources than considered in the models, although I expect diminishing returns from that expanded production (as described in my blog post regarding Energy Return On Investment).
http://dougrobbins.blogspot.com/2011/09/energy-return-on-investment.html
And the model generalizations regarding "pollution" appear to be unfounded, or uncalibrated.  But there is a good correspondence between atmospheric CO2 and the pollution parameter of the LTG models.

Graham Turner of CSIRO presented a 30-year lookback on the LTG findings, presented here:
http://www.csiro.au/files/files/plje.pdf.
Matthew Simmons, author of "Twilight in the Desert" (R.I.P., 2010), also presented a look-back on the 1972 LTG.  http://www.greatchange.org/ov-simmons,club_of_rome_revisted.pdf

So we are left with the question:  Will food, wealth and population collapse in the 21st century, as predicted in the 1972 world model?  Time will  tell.