There Is Always Another Explanation

Even in mathematics there is always another way to describe a set of data. We fall in love with the first one we see and then build reasons why it matters most. In mathematics, it might not matter. In life, overlooking alternatives can be costly.

An example

I follow Marginal Revolution because I usually learn something. A post on 4 November is a good example. Sugar Consumption.

History tells us that sugar consumption (including corn derivatives) per person from 1980 to 2000 rose from around 90 grams per day to about 110. Obesity among American adults rose form 15% to about 30% of the population. Well there it is! A wonderful correlation and therefore a cause.

Not so fast.

From 2000 to 2013, sugar consumption dropped from 110 grams per day to about 93. Obesity went from about 30% to about 38%. Now, what do you know?

In truth, not a lot more, except the need to dig further. There is obviously more to the puzzle.

As you go further in the article you will find more information. We learn sugar consumption from 1820 to 1920 increased ten-fold, but obesity stayed about the same. How odd is that? From 1920 to 1980 sugar consumption varied a little but was pretty much steady. Obesity didn’t change much. Then the obesity problem took off.

By now you should be seeing sugar as a red herring. It’s there and easy to measure, but probably it’s just there and easy to measure.

As Mark Twain has postulated, “What gets us into trouble is not what we don’t know. It’s what we know for sure that just ain’t so.” There is quite a lot of that going around.


The article is quite short but there are many comments that add value. The very best kind of article. You should read them.

Some that I found useful.

  • You know what else didn’t decline between 1820-1920? The amount of physical labour.
  • The 70s and 80s is when women started entering the (sedentary) US work-force in large numbers.
  • Hard to believe that our FDA (and CDC) could be so full of crap on the obesity issue.
  • A monocausal theory of obesity would be pretty dumb. I’m sure there are some people out there pushing such an oversimplified narrative, but a serious scientific approach to the question would not support this.
  • Don’t ignore survivorship bias issues. We are a lot better at keeping obese people alive than we were in 1980.
  • A monocausal of *anything at all* is pretty dumb.
  • We’re just eating more, and engaging in less activity.

Some others that might be causative.

The world we live in is higher stress than it once was. Stressed people tend to eat more. Maybe for comfort, maybe for some ancient genetic mandate.

People don’t realise how little extra it takes to add weight over the long run. A little for a long time is a lot. Suppose you need 2,000 calories per day and you get exactly that from a well-balanced diet. As you become more stressed or bored or whatever, you eat one Tim Hortons Chocolate Chunk Cookie five days a week. They are 240 calories each and so every week you add 1,200 calories to you body it does not need but is willing to store for the future. A pound of fat is about 3,500 calories. Three weeks to gain a pound. That’s 17 pounds in a year. Oh my!

The message

  • Most people let things happen. They don’t have to do so. Discipline?
  • Any small change applied over a long time has an objectively large effect.
  • There are people who benefit from a simple cause and effect equation. They may not know the truth, just a simple connection.
  • Once people hold a simple belief, they don’t give it up easily.

The thought to carry away.

“The secret of what anything means to us depends on how we’ve connected it to all the other things we know. That’s why it’s almost always wrong to seek the “real meaning” of anything. A thing with just one meaning has scarcely any meaning at all.” Marvin Minsky

Marvin Minsky was a computer scientist and a pioneer in artificial intelligence. There is no field of study that demonstrates complexity more easily than AI.

The limits:

  1. Simple problems may have simple answers.
  2. There are no simple problems.
  3. Therefore, simple answers are for some purpose other than solving a problem.

Disbelieve every simple explanation until you have eliminated others, or failed to find a more nuanced one. It is good to be a skeptic.

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