The population conundrum

The world’s population is nearing 7 billion and will perhaps hit 9 billion by 2050.  In a previous post,  I estimated that the earth could feed up to 15 billion based on the amount of arable land and current farm yields.  Ever since Thomas Malthus, people have predicted that we would eventually reach saturation resulting in massive famine and global unrest.  However, technology  keeps coming along to make farming more and more efficient, pushing off the Malthusian crisis into the future.  The green revolution led by Norman Borlaug, saved over a billion people from starvation in the mid-twentieth century.  In fact, food production is so efficient now that it has led to an obesity epidemic in the developed world (e.g. see here).

The question then is what is a sustainable population.  Currently, food production requires a lot of fossil fuels, which comes with its own set of issues.  Scientific American recently published an article arguing that phosphorous, one of the three components of fertilizer, with nitrogen and potassium, may run out by the end of this century.  Obviously, our current means of food production is not sustainable indefinitely.  I think our situation is like the person walking across a railroad bridge with a train bearing down on her. She can either run towards the train or away from it to get off the bridge.  Depending on the speed of the train and how fast she can run, there is a critical point on the bridge where running in one of the directions is optimal.  For us, going backwards away from the train is to try to reduce population growth and try to find a sustainable level.  Running towards the train is to rely on technological progress to  increase food production.  Given that good ideas seem to grow linearly with the population and possibly slower (e.g. see here), going towards the train actually means we should keep growing as fast as we can and hope that another Norman Borlaug comes along.  Where we are on that bridge is anybody’s guess.

The cost of commuting

It is about 45 miles (70 km) from Baltimore to the NIH campus in Bethesda, MD.  If I were to travel the entire distance using public transit it would cost over 20 dollars for a return trip (one way bus fare in Baltimore is $1.60, commuter rail (Marc train) fare is $7.00, and Metro fare in DC is $3.65 ($3.85 during peak hours)).  That amounts to over $100 per week and $5000 per year.  If I bought a  monthly rail pass, then I could cut the cost down by 75% or so.  Now if instead I were to drive everyday,  ninety miles per day is equivalent to 22,500 miles per year.  A car that could travel 30 miles per gallon of gasoline would use 750 gallons a year.  At the current price of $3 per gallon, this would be $2250 per year.  If I drive my car for ten years and it cost twenty thousand dollars then that is an additional $2000 per year.  Insurance, fees, maintenance and repairs probably costs another $2000 per year so driving would cost about $6000 per year.  If I drove a cheaper and more efficient car then I could bring this cost down to $5000 per year.  Thus, driving is economically competitive with public transit.  Add in the fact that I would own a car anyway even if I didn’t use it to commute to work and driving is the less expensive choice.

How is this possible?  Well one cost that I didn’t account for is parking.  The NIH happens to have a large campus where parking is nominally free.  Although if I chose not to drive, I could receive a public transit subsidy of  up to $110 per month or $1320 per year.  If the NIH were located in downtown Washington DC, parking could cost over $400 per month or $5000 per year.  So the real reason driving is competitive with public transit is because parking is subsidized.  If I  worked in an urban center  where parking is expensive then driving would be much more expensive than public transit.  Driving is further subsidized because roads and highways are funded by tax dollars while the cost of maintaining transit stations and tracks are only partially funded by taxes.  If transportation infrastructure were publically funded or if subsidies for roads and parking did not exist then public transit would be the prohibitive cost effective option.




Bayesian parameter estimation

This is the third post on Bayesian inference.  The other two are here and here. This probably should be the first one to read if you are completely unfamiliar with the topic.  Suppose you are trying to model some system and you have a model that you want to match some data.  The model could be a function, a set of differential equations, or anything with parameters that can be adjusted.  To make this concrete, consider this classic differential equation model of the response of glucose to insulin in the blood:

\dot G = -S_I X G - S_G(G-G_b)

\dot X = c_X[I(t) - X -I_b]

where G is glucose concentration, I is insulin, X is the action of insulin in some remote compartment, and there are five free parameters S_I, S_G, G_b, c_X, I_b.  If you include the initial values of G and X there are seven free parameters.  The data consist of measurements of glucose and insulin at discrete time points \{t_1,t_2,\dots\}.  The goal is to find a set of free parameters so that the model fits the data at those time points.

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High energy physics

I was asked a while ago what I thought of the Large Hadron Collider at CERN.  Although I’ve been critical of high energy physics in the past (see for example here), I strongly support the LHC and think it is a worthwhile endeavor.  My reason is because I think it will be important for future technology.  By this I don’t just mean spin offs, like the World Wide Web, which was invented at CERN by Tim Berners Lee.  What I mean is that knowledge gained at the high energy scale could be useful for saving the human race one day.

Let me elaborate.  My criticism of high energy or particle physics in the past was mostly because of the claim that it was more “fundamental” than other areas of science like condensed matter physics or psychology.  Following noble laureate Philip Anderson’s famous article “More is Different” (Science 177:393-396, 1971), what is fundamental to me is a matter of perspective.  For example, the fact that I can’t find a parking spot at the mall a week before Christmas is not because of particle physics but because of the pigeonhole principle, (i.e. if you have more things than boxes, then if you try to put the things into the boxes at least one box must contain more than one thing).  This is as fundamental to me as any high energy theory.  The fact that you can predict an election using polling data from a small sample of the electorate is because of  the central limit theorem, (i.e. the sum of a bunch of random events tends to obey a normal distribution), and is also independent of what particles that comprise the electorate.  Ironically, the main raison d’etre of the LHC is to look for the Higgs boson, which is thought to give  masses to some subatomic particles.  The Higgs mechanism is based on the idea of spontaneous symmetry breaking, which came from none other than Phil Anderson who was studying properties of magnets.

So how could high energy physics be pertinent to our existence some day?  Well, some day in the very distant future the sun will expand into a red giant and swallow the earth.  If humans, or whatever our descendants will be called, are to survive they are going to need to move.  This will take space faring technology that could rely on some yet unknown principle of high energy physics that could be discovered by the LHC.  And in the very, very distant future the universe will end either in a big crunch or by expanding so much that matter won’t be able to persist.  If and when that time comes and life forms still exist, then to survive they’ll have to figure out how to “tunnel” into a new universe or new existence.  This will take real science fiction-like stuff that will likely depend on knowledge of high energy physics.  So although high energy physics does not hold a monopoly on fundamental concepts, it may still be absolutely necessary for life saving future technology.