Constrained Bliss Point

Nowadays, virtually all reasonable people will admit that global warming is real. It’s true that the world is not always warmer — for that reason a number of cool kids are calling it “climate change” instead of “global warming” — but one thing is certain: the times they are a-changin’.

I don’t much care whose fault it is, whether natural or anthropogenic. I do think we should do something about it, not least of all because the long-term survival of our species and civilization is at stake. “But what can I do to stop such a monstrous force as manbearpig global warming?” you might ask.

There is actually one very simple thing you can do that would go further than any other single step to fight climate change. Stop eating cows. Seriously. The UN put out a nice report to this end. Cattle ranching produces more greenhouse gas than cars, or even all other forms of transportation put together. It also drives deforestation and consumes massive amounts of water.

Do your part. Try not to eat meat. Tell your friends and family of the folly of cows. And when you have to eat meat, please, for the love of God, eat chickens.

Biases should be avoided if one is trying to do objective science. Some biases are relatively easy to see, like race or sex discrimination. Others are harder to combat, or even to identify. On a related note, Wikipedia has a pretty comprehensive list of cognitive biases if anyone is interested.

I’ve found one that’s very, very sneaky. I call it “measurability bias”, which I’m pretty sure I read somewhere but now I forget the exact reference. It comprises a few similar phenomena. For one, it is a tendency for researchers — notably those who pride themselves as scientists — to spend more time and resources focusing on problems where data are readily available, rather than what they identify as the most interesting or important problems.

A more general conception of measurability bias is when a decision maker weights more heavily the set of things that are quantitatively or accurately measurable when making decisions. For example, when Oreos increase in price by 20%, that’s very easy to see, but I’m not as good at noticing if the increase in quality leads to a greater-than-20% increase in the satisfaction I derive from said Oreos.

Cost-benefit analyses suffer from this bias a great deal. For example, in the case of climate change, the costs of carbon reduction programs are known relatively accurately, whereas things like the mitigated risk from rising sea levels, loss in biodiversity, chronic water scarcities in developing countries, etc. are much harder to measure. In this case, measurability bias is used as an excuse to do nothing — since the costs are large and the benefits are uncertain, we should defer climate stabilization policies.

The point to take away is that just because something is difficult to quantify does not mean it is not incredibly important.