I Would Gladly Pay You Tuesday for a Hamburger Today (Part 9 of Cognitive Biases)

A cognitive bias is a pattern of deviation in judgment that occurs in particular situations, and boy howdy, are there a lot of them!

This week, meet the Hawthorne effect (being watched makes you work harder), the herd instinct (tendency to follow the crowd), hindsight bias (I knew it all along), and hyperbolic discounting (I would gladly pay you Tuesday for a hamburger today.)

Part 7 has links to all the previous installments. Part 8 is here.

Hawthorne Effect

The Hawthorne effect is often portrayed as aa sort of Heisenberg uncertainty principle for the social sciences: observer interacts with observed through the process of observation. In practice, almost any sort of internal improvement effort will have a short-term positive effect on performance, a placebo effect that benefits all of us in the management consulting world.

The original experiments on which the Hawthorne effect is based took place from 1924 to 1932 at the Hawthorne Works, a Western Electric plant outside of Chicago. A group of six women worked in a special room assembling telephone relays and dropping them down a chute. The most famous and oft-cited of those experiments involves a study of how illumination levels affected the rate with which these women dropped finished relays down the chute. Over a five year period the researchers also changed pay rules, varied break frequency and duration, and shortened and lengthened the workday, all to the tune of the drip-drip-drip of falling relays.

There was, interestingly, no double blind in the experiments. The women were fully aware they were being studied, and even suggested some of the experiments themselves. The lack of control over the numerous variables has led to a wide range of interpretation about what — if, indeed, anything — the studies really mean.

Herd Instinct

Herd behavior was well-known to exist in animals, but Friedrich Nietzche was the first to use the concept of “herd instinct” as one more reason to have contempt for the human species. There’s nothing inherently wrong, however, with acting as part of a group. In many circumstances, the natural tendency of a group to move in the same direction can increase safety. Of course, sometimes herds head over the edge of the cliff.

As noted earlier, calling something a cognitive bias isn’t the same as calling a biased decision wrong or stupid. If a crowd is fleeing in a particular direction, it may be a false alarm, but then again, they may know something you don’t. If danger doesn’t appear imminent, taking a few minutes to look around is a better way to balance your risks.

Hindsight Bias

Once you know how it turned out, a certain sense of inevitability creeps in. The signs were always there, and the people in charge should have known the truth all along.

The frequently repeated libel that FDR, for example, knew in advance about the impending Pearl Harbor attack and remained silent for political reasons is a case in point. (I won’t rehash the argument in detail, but I’m always appreciative of the Straight Dope’s accuracy and balance on almost any topic.) The argument relies on the idea that in the mass of raw data, decision-makers could have recognized in advance exactly which bits of information were salient. This is nonsense. Reading the future forward is orders of magnitude more difficult than reading it backward.

This particular bias is aided by our own tendency to believe that when we turn out to have been right that we “knew it all along.” Before-and-after measures of certainty tend to vary a lot.

Hyperbolic Discounting

“I would gladly pay you Tuesday for a hamburger today.” Wimpy, the hamburglar pal of Popeye the Sailor Man, liked his rewards up front and his penalties delayed. People in general tend to prefer the bird in the hand to a flock in the bush. That’s a fairly well-known cognitive bias.

What’s not so well known is the amount of the discount — how much will you give up in the future to receive the benefit today? Behavioral economists believe the relationship is hyperbolic. We’ll take a dollar today in preference to three dollars tomorrow.

But given a choice between a dollar 365 days from now and three dollars 366 days from now, we’ll gladly wait the same extra day for three times the payoff. Our choices are inconsistent over time: we’ll commit our future self to a course of action (waiting a day) that we aren’t willing to follow today.

This is often irrational, but not always. Depending on the uncertainty of the reward, a definite dollar today may be preferable to the possibility of three dollars tomorrow.

This particular cognitive bias shows up in studies of how people save for retirement, borrow on their credit cards, procrastinate on important tasks, and deal with the consequences of addiction. Especially where hamburgers are concerned.

“Looking for the Pony” — Cognitive Biases, Part 2

Welcome back to part two of our discussion of cognitive and decision-making biases. The series begins here.

Everyone's subject to cognitive biases of one sort or another. None of us is capable of pure objectivity; we cannot see reality without distortion. But we can try.

There are around 100 different identified cognitive and decision biases, and some of them have subsets, as we'll see shortly. Today, we'll cover three more: the base rate fallacy, congruence bias, and everyone's traditional favorite, experimenter's bias.

Base rate fallacy. There are 100 terrorists trying to sneak through airline security for every one million non-terrorists. TSA has set up an automated face recognition system that has 99% accuracy. The alarm goes off, and trained Homeland Security agents swoop down. What is the probability their captive is really a terrorist?

Well, if the failure rate is 1%, that means there’s a 99% chance the person is a terrorist, and a 1% chance that he or she is not, right? That justifies a significant assumption of guilt.

But this actually gets it backward. The chance the person isn't a terrorist is far greater — in fact, it's 99.02% likely that the new prisoner is completely innocent!

The mistake that leads to the first conclusion is called the base rate fallacy. It occurs when you don't notice that the failure rate (1 in 100) is the not the same as the false alarm rate. The false alarm rate is completely different, because there are, after all, far more non-terrorists than terrorists. Let's imagine that we walk everyone — 100 terrorist and 1 million non-terrorists, for a total of 1,000,100 people — in front of the face recognition tool. A 1% failure rate means it's going to ring incorrectly one time for each 100 passengers, 10,099 times in total. It will catch 99 terrorists and miss one, but it's also going to catch 10,000 non-terrorists. The ratio is actually 99:10,099, or a miniscule 0.98%, that the person caught is actually a terrorist.

This does not argue against the value of screening. Screening might be perfectly reasonable. Overreaction, however, is not. If you’re 99% sure you’ve caught a terrorist, you will behave differently than if you’re only 1% sure.

To avoid the base rate fallacy, look at the “prior probability.” If there were no terrorists, what would the face recognition system produce? With a 1% failure rate, it would never pick a real terrorist (there would be none), but it would trigger 10,000 false positives. Now you’ve found the missing fact.

(Footnote: Notice that the base rate fallacy only produces incorrect analysis when the scale is unbalanced, as is our case with 100 terrorists in city with a population of 1 million. As the populations approach 50/50, the failure rate and false alarm rate would converge. Mind you, we'd have different problems then.)

Congruence bias. In congruence bias, you only test your hypothesis directly, potentially missing alternative explanations. In the famous Hawthorne experiment, Frederick W. Taylor, father of Scientific Management, wanted to test whether improved lighting in factories would increase worker productivity. He performed a direct test: he measured productivity, installed better lighting, and measured productivity again. Productivity went up. If you are falling into congruence bias, you’re done. Experiment confirmed; case closed.

But Taylor avoided the trap. He tested his hypothesis indirectly. If improved lighting increased productivity, he reasoned that worse lighting should lower it. So he tested that proposition as well. He took out a lot of lights and measured again: and to everyone’s surprise, productivity went up! A deeper analysis revealed what is now known as the Hawthorne Effect: when people feel others are paying attention to them, their productivity tends to go up, at least temporarily. (It’s a huge benefit of management consultants; just by showing up, we’re likely to make things better.)

To avoid congruence bias, don’t be satisfied with direct reasoning alone. Direct confirmation asks, “If I behaved in accordance with my hypothesis, what would I expect to occur?” Indirect confirmation asks, “If I acted in conflict with my hypothesis, what would I expect to occur?” If Taylor had stopped with the first question, we’d all be fiddling with the lights. Only the second question allowed him to discover the deeper truth.

Experimenter’s bias. This bias is well known to anyone in scientific fields. It’s the tendency for experimenters to believe and trust data that agrees with their hypothesis, and to disbelieve and distrust data that doesn’t. It’s a natural enough feeling; there’s a price to pay if we’re wrong, even if it’s only a hit to our egos. It’s impossible for any human being to be completely objective. Our perceptions and intelligence are constrained, and we are looking from the inside, not the outside.

Experimenter’s bias can’t be avoided; it has to be managed instead. Last week, we discussed the “bias blind spot,” the recursive bias of failing to recognize that you have biases. Self-awareness helps. Another good technique is the “buddy system.” I frequently work with co-authors so I have someone to challenge my thinking. That reduces the problem, though it doesn’t eliminate it — wherever my co-author and I see it the same way, the risk remains.

The best technique is to understand the components of the bias. A 1979 study of sampling and measurement biases listed 56 different experimenter’s biases: the “all’s well” literature bias, the referral filter bias, the volunteer bias, the insensitive measure bias, the end-digit preference bias, and my favorite, the data dredging bias, also known as “looking for the pony.”

More next week…