## Sunday, November 20, 2016

### "How to Make a Bad Decision"

From Freakonomics:
Our latest Freakonomics Radio episode is called “How to Make a Bad Decision.” (You can subscribe to the podcast at iTunes or elsewhere, get the RSS feed, or listen via the media player above.)

Some of our most important decisions are shaped by something as random as the order in which we make them. The gambler’s fallacy, as it’s known, affects loan officers, federal judges — and probably you too. How to avoid it? The first step is to admit just how fallible we all are.
Below is a transcript of the episode, modified for your reading pleasure. For more information on the people and ideas in the episode, see the links at the bottom of this post. And you’ll find credits for the music in the episode noted within the transcript.
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Let’s say I flip a coin and it comes up … heads. Now I flip it again … hmm heads again. One more time … and … wow, that’s three heads in a row. Okay, if I were to flip the coin one more time, what are you predicting? Here’s what a lot of people would predict: “Let’s see, heads-heads-heads … it’s gotta come up tails this time.” Even though you know a coin toss is a random event, and that each flip is independent — and therefore the odds for any one coin toss are … 50-50. But that doesn’t sit well with people.
Toby MOSKOWITZ: That doesn’t sit well with people.
Toby Moskowitz is an economist at Yale.
MOSKOWITZ: We like to tell stories and find patterns that aren’t really there. And if you flip a coin, say, ten times, most people think — and they’re correct — that on average you should get five heads, five tails. The problem is they think that should happen in any ten coin flips. And of course it’s very probable that you might get eight heads and two tails and it’s even possible to get ten heads in a row. But people have this notion that randomness is alternating. And that’s not true.
This notion has come to be known as “the gambler’s fallacy.”
MOSKOWITZ: This is a common misconception in Vegas. You go to the slot machine, it hasn’t paid out in a long time and people think, “Well, it’s due to be paid out.” That is just simply not true, if it is a truly independent event, which it is, the way it’s programmed.
DUBNER: So Toby, you have co-authored a new working paper called “Decision-Making Under the Gambler’s Fallacy,” and if I understand correctly, the big question you’re trying to answer is how the sequencing of decision-making affects the decisions we make. Is that about right?

MOSKOWITZ: That’s correct. In fact, the genesis of the paper was really to take this idea of the gambler’s fallacy, which has been repeated many times in psychological experiments, which is typically a bunch of undergrads playing for a free pizza, and apply it to real-world stakes, where the stakes are big, there is a great deal of uncertainty, and these decisions matter a lot.
Some of these decisions matter so much they can mean the difference between life and death. So these probably aren’t the kind of decisions we should be making based on a coin toss.
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So, Toby Moskowitz and his co-authors, Daniel Chen and Kelly Shue, have written this interesting research paper. It’s called “Decision-Making Under the Gambler’s Fallacy.” It’s the kind of paper that academics publish by the thousand. They publish in order to get their research out there, maybe to get tenure, etc. So it matters for them. Does it matter for you? Why should you care about something like the gambler’s fallacy?

Well, we often talk on this program about the growing science of decision-making. But it’s funny.

Most of the conversations focus on the outcome for the decision-maker. What about the people the decision is affecting? What if you are a political refugee, hoping to gain asylum in the United States? There’s a judge making that decision. What if you’re trying to get your family out of poverty in India by starting a business and you need a bank loan? There’s a loan officer making that decision. Or what if you’re a baseball player, waiting on a 3-2 pitch that’s going to come at you 98 miles an hour from just 60 feet, 6 inches away? That’s where the umpire comes in.
MOSKOWITZ: We’ll start with Major League Baseball – that was a simple one.
Moskowitz and his co-authors analyzed decision-making within three different professions – baseball umpires, loan officers, and asylum judges – to see whether they fall prey to the gambler’s fallacy. Because …
MOSKOWITZ: … there’s all kinds of possible areas where the sequence of events shouldn’t matter, but our brains think they should and it causes us to make poor decisions.
Decisions that are the result of …
MOSKOWITZ: What I would call decision heuristics.
A “heuristic” being, essentially, a cognitive shortcut. Now, why choose baseball umpires?
MOSKOWITZ: Because baseball has this tremendous data set called PITCHf/x, which records every pitch from every ballgame and what it records is if you look at the home-plate umpire — where the pitch landed, where it was located within or outside the strike zone, and also what the call was from the umpire.
Moskowitz and his colleagues looked at data from over 12,000 baseball games, which included roughly 1.5 million called pitches – that is, the pitches where the batter doesn’t swing, leaving the umpire to decide whether the pitch is a ball or strike. As they write in the paper: “We test whether baseball umpires are more likely to call the current pitch a ball after calling the previous pitch a strike and vice versa.” There were 127 different umpires in the data. The researchers did not focus on pitches that were obvious balls or strikes.
MOSKOWITZ: If you take a pitch dead center of the strike zone, umpires get that right 99 percent of the time.
Instead, they focused on the real judgment calls.
MOSKOWITZ: So the thought experiment was as follows — take two pitches that land in exactly the same spot. The umpire should be consistent and call that pitch the same way every time. Because the rules state that each pitch is independent in terms of calling it correctly — it’s either in the strike zone or it’s not.
The first thing the PITCHf/x data shows is that umpires are, generally, quite fallible.
MOSKOWITZ: On pitches that are just outside of the strike zone – they’re definitely balls, but they’re close – on those pitches, umpires only get those right about 64 percent of the time. So that’s a 36 percent error rate, it’s big.
DUBNER: Slightly better than flipping a coin, but not much.
MOSKOWITZ: Not much. Better than you and I could do though, I would say.
And how does the previous pitch influence the current pitch?
MOSKOWITZ: Just as a simple example, if the previous pitch was a strike, the umpire was already about half a percent less likely to call the next pitch a strike.
Half a percent doesn’t seem like that big an error. But keep in mind that’s for the entire universe of next pitches — whether it’s right down the middle, or high and outside, or in the dirt. What happens when the next pitch is a borderline call?...MUCH MORE (not on baseball, that's just the hook)