Consistantly Inconsistant

In The Righteous Mind, the conscious mind is sometimes depicted as a kind of “Press secretary” for the unconscious. His or her job is not to make decisions, but rather to rationalize and put a positive spin on choices already made.

Often, the spokesman is confronted with contradiction between new decisions and previously expressed rationalizations. Usually, these can be waved away without engendering too much cognitive dissonance. For example, setting up someone or something as an “authority” is fine, as long as you agree with them; but when you disagree, their authority suddenly seems less compelling. For example, Sean Hannity is a well known Catholic who is happy to opine that homosexuality is immoral , but somehow parts ways with the Church on issues of war, capital punishment, and with the current pope, income inequality.

On the other side of the political spectrum, many liberals have no hesitation to paint their opponents as backward and “anti-science” for opposing the scientific consensus on a wide range of issues, including:

  • Evolution
  • The age of the Earth
  • Climate Change
  • Vaccination

But no one has a monopoly on pseudoscience. When it comes to Genetically Modified Organisms (GMOs), the actual research is rather strong in favor of safety: “After 14 years of cultivation and a cumulative total of 2 billion acres planted, no adverse health or environmental effects have resulted from commercialization of genetically engineered crops.”

The question: “Are GMO Opponents Are the Climate Skeptics of the Left?” is apt. It is easy for well-fed and healthy Westerners – like patrons of Chipotle –  to indulge in some psdueoscience. But when people are starving or going blind from vitamin A deficiency, the cost-benefit balance is greatly shifted.

In a New York Times piece, a former GMO opponent talks about his change of heart:

“I, too, was once in that activist camp. A lifelong environmentalist, I opposed genetically modified foods in the past. Fifteen years ago, I even participated in vandalizing field trials in Britain. Then I changed my mind. After writing two books on the science of climate change, I decided I could no longer continue taking a pro-science position on global warming and an anti-science position on G.M.O.s.”

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You’d Be Suprised

It often takes time for new ideas to percolate out of academia into the “real world,” but denizens of the Ivory Tower can remain oblivious to this lag. A stark example is the rapid fall from grace of the Normal Distribution. A huge swatch of statistics and finance has historically been based on assumptions of normal distributions for three main reasons: (1) The math is tractable, (2) The solutions are unique, (3) the central limit theory predicts, that, under certain assumptions, the normal distribution is the one to expect. However, anyone who is familiar with the work of Nicolas Taleb (who called the Normal Distribution an “Intellectual Fraud“) and Emanuel Derman will know that financial models that have assumptions of normality built in can fail spectacularly – as they did during the financial crisis – because they dramatically underestimate the probability of “tail events.” Using a normal distribution, such extreme events are incredibly (exponentially) unlikely, but, of course, they do happen in real life. Often this is because of contagions that spread throughout the system or some other previously unknown phenomenon that decided to pop up. Lately, even Jamie Dimon,  the head of JP Morgan Chase took a backhanded swipe at the models by saying that recent fluctuations in the US Treasury market were unprecedented and only  “an event that is supposed to happen only once in every 3 billion years or so” . One host on the Slate Money Podcast (Jump to 36 minutes) chided him for making such a hackneyed point. Of course everyone knows that normal distributions don’t work here! Except, as another host was quick to point out, the entire edifice of standard “Value at Risk” models, which are still the industry standard to deciding how much risk is too much, is still built on assumptions or normality. So don’t be so fast to assume that the news has spread.