When we can’t fit a square peg into a round hole, we’ll usually blame the peg – when.
Not only does political coverage often lose the signal – it frequently accentuates the noise. If there are a number of polls in a state that show the Republican ahead, it won’t make news when another one says the same thing. But if a new poll comes out showing the Democrat with the lead, it will grab headlines – even though the poll is probably an outlier and won’t predict the outcome accurately.
Partisans who expect every idea to fit on a bumper sticker will proceed through the various stages of grief before accepting that they have oversimplified reality.
The answer as to why bubbles form,” Blodget told me, “is that it’s in everybody’s interest to keep markets going up.
Most of it is just noise, and the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine – but a relatively constant amount of objective truth.
Political experts had difficulty anticipating the USSR’s collapse, Tetlock found, because a prediction that not only forecast the regime’s demise but also understood the reasons for it required different strands of argument to be woven together. There was nothing inherently contradictory about these ideas, but they tended to emanate from people on different sides of the political spectrum,11 and scholars firmly entrenched in one ideological camp were unlikely to have embraced them both.
Some of you may be uncomfortable with a premise that I have been hinting at and will now state explicitly: we can never make perfectly objective predictions. They will always be tainted by our subjective point of view.
It is the alternative – failing to change our forecast because we risk embarrassment by doing so – that reveals a lack of courage.
Ordinary Americans were also concerned. Google searches on the term “housing bubble” increased roughly tenfold from January 2004 through summer 2005.
The market can stay irrational longer than you can stay solvent.
Bayes’s theorem requires us to state – explicitly – how likely we believe an event is to occur before we begin to weigh the evidence. It calls this estimate a prior belief.
What isn’t acceptable under Bayes’s theorem is to pretend that you don’t have any prior beliefs. You should work to reduce your biases, but to say you have none is a sign that you have many. To state your beliefs up front – to say “Here’s where I’m coming from”12 – is a way to operate in good faith and to recognize that you perceive reality through a subjective filter.
The word objective is sometimes taken to be synonymous with quantitative, but it isn’t. Instead it means seeing beyond our personal biases and prejudices and toward the truth of a problem.
Wherever there is human judgment there is the potential for bias. The way to become more objective is to recognize the influence that our assumptions play in our forecasts and to question ourselves about them.
When they play against each other, the game usually comes down to who can find his opponent’s blind spots first.
You will need to learn how to express-and quantify-the uncertainty in your predictions. You will need to update your forecast as facts and circumstances change. You will need to recognize the wisdom in seeing the world from a different viewpoint. The more you are willing to do these things, the more capable you will be of evaluating a wide variety of information without abusing it.
Successful gamblers – and successful forecasters of any kind – do not think of the future in terms of no-lose bets, unimpeachable theories, and infinitely precise measurements. These are the illusions of the sucker, the sirens of his overconfidence.
The fashionable term now is “Big Data.” IBM estimates that we are generating 2.5 quintillion bytes of data each day, more than 90 percent of which was created in the last two years.36.
There was “nothing new under the sun,” as the beautiful Bible verses in Ecclesiastes put it – not so much because everything had been discovered but because everything would be forgotten.
If you hold there is a 100 percent probability that God exists, or a 0 percent probability, then under Bayes’s theorem, no amount of evidence could persuade you otherwise.