And yet if we’re going to improve our skills of rationality, go beyond the standards of performance set by hunter-gatherers, we’ll need deliberate beliefs about how to think with propriety. When we write new mental programs for ourselves, they start out in System 2, the deliberate system, and are only slowly—if ever—trained into the neural circuitry that underlies System 1. So if there are certain kinds of thinking that we find we want to avoid—like, say, biases—it will end up represented, within System 2, as an injunction not to think that way; a professed duty of avoidance.
If we want the truth, we can most effectively obtain it by thinking in certain ways, rather than others; these are the techniques of rationality. And some of the techniques of rationality involve overcoming a certain class of obstacles, the biases . . .
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4
. . . What’s a Bias, Again?
A bias is a certain kind of obstacle to our goal of obtaining truth. (Its character as an “obstacle” stems from this goal of truth.) However, there are many obstacles that are not “biases.”
If we start right out by asking “What is bias?,” it comes at the question in the wrong order. As the proverb goes, “There are forty kinds of lunacy but only one kind of common sense.” The truth is a narrow target, a small region of configuration space to hit. “She loves me, she loves me not” may be a binary question, but E = mc2 is a tiny dot in the space of all equations, like a winning lottery ticket in the space of all lottery tickets. Error is not an exceptional condition; it is success that is a priori so improbable that it requires an explanation.
We don’t start out with a moral duty to “reduce bias,” because biases are bad and evil and Just Not Done. This is the sort of thinking someone might end up with if they acquired a deontological duty of “rationality” by social osmosis, which leads to people trying to execute techniques without appreciating the reason for them. (Which is bad and evil and Just Not Done, according to Surely You’re Joking, Mr. Feynman, which I read as a kid.)
Rather, we want to get to the truth, for whatever reason, and we find various obstacles getting in the way of our goal. These obstacles are not wholly dissimilar to each other—for example, there are obstacles that have to do with not having enough computing power available, or information being expensive. It so happens that a large group of obstacles seem to have a certain character in common—to cluster in a region of obstacle-to-truth space—and this cluster has been labeled “biases.”
What is a bias? Can we look at the empirical cluster and find a compact test for membership? Perhaps we will find that we can’t really give any explanation better than pointing to a few extensional examples, and hoping the listener understands. If you are a scientist just beginning to investigate fire, it might be a lot wiser to point to a campfire and say “Fire is that orangey-bright hot stuff over there,” rather than saying “I define fire as an alchemical transmutation of substances which releases phlogiston.” You should not ignore something just because you can’t define it. I can’t quote the equations of General Relativity from memory, but nonetheless if I walk off a cliff, I’ll fall. And we can say the same of biases—they won’t hit any less hard if it turns out we can’t define compactly what a “bias” is. So we might point to conjunction fallacies, to overconfidence, to the availability and representativeness heuristics, to base rate neglect, and say: “Stuff like that.”
With all that said, we seem to label as “biases” those obstacles to truth which are produced, not by the cost of information, nor by limited computing power, but by the shape of our own mental machinery. Perhaps the machinery is evolutionarily optimized to purposes that actively oppose epistemic accuracy; for example, the machinery to win arguments in adaptive political contexts. Or the selection pressure ran skew to epistemic accuracy; for example, believing what others believe, to get along socially. Or, in the classic heuristic-and-bias, the machinery operates by an identifiable algorithm that does some useful work but also produces systematic errors: the availability heuristic is not itself a bias, but it gives rise to identifiable, compactly describable biases. Our brains are doing something wrong, and after a lot of experimentation and/or heavy thinking, someone identifies the problem in a fashion that System 2 can comprehend; then we call it a “bias.” Even if we can do no better for knowing, it is still a failure that arises, in an identifiable fashion, from a particular kind of cognitive machinery—not from having too little machinery, but from the machinery’s shape.
“Biases” are distinguished from errors that arise from cognitive content, such as adopted beliefs, or adopted moral duties. These we call “mistakes,” rather than “biases,” and they are much easier to correct, once we’ve noticed them for ourselves. (Though the source of the mistake, or the source of the source of the mistake, may ultimately be some bias.)
“Biases” are distinguished from errors that arise from damage to an individual human brain, or from absorbed cultural mores; biases arise from machinery that is humanly universal.
Plato wasn’t “biased” because he was ignorant of General Relativity—he had no way to gather that information, his ignorance did not arise from the shape of his mental machinery. But if Plato believed that philosophers would make better kings because he himself was a philosopher—and this belief, in turn, arose because of a universal adaptive political instinct for self-promotion, and not because Plato’s daddy told him that everyone has a moral duty to promote their own profession to governorship, or because Plato sniffed too much glue as a kid—then that was a bias, whether Plato was ever warned of it or not.
Biases may not be cheap to correct. They may not even be correctable. But where we look upon our own mental machinery and see a causal account of an identifiable class of errors; and when the problem seems to come from the evolved shape of the machinery, rather from there being too little machinery, or bad specific content; then we call that a bias.
Personally, I see our quest in terms of acquiring personal skills of rationality, in improving truthfinding technique. The challenge is to attain the positive goal of truth, not to avoid the negative goal of failure. Failurespace is wide, infinite errors in infinite variety. It is difficult to describe so huge a space: “What is true of one apple may not be true of another apple; thus more can be said about a single apple than about all the apples in the world.” Success-space is narrower, and therefore more can be said about it.
While I am not averse (as you can see) to discussing definitions, we should remember that is not our primary goal. We are here to pursue the great human quest for truth: for we have desperate need of the knowledge, and besides, we’re curious. To this end let us strive to overcome whatever obstacles lie in our way, whether we call them “biases” or not.
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5
Availability
The availability heuristic is judging the frequency or probability of an event by the ease with which examples of the event come to mind.
A famous 1978 study by Lichtenstein, Slovic, Fischhoff, Layman, and Combs, “Judged Frequency of Lethal Events,” studied errors in quantifying the severity of risks, or judging which of two dangers occurred more frequently.1 Subjects thought that accidents caused about as many deaths as disease; thought that homicide was a more frequent cause of death than suicide. Actually, diseases cause about sixteen times as many deaths as accidents, and suicide is twice as frequent as homicide.
An obvious hypothesis to account for these skewed beliefs is that murders are more likely to be talked about than suicides—thus, someone is more likely to recall hearing about a murder than hearing about a suicide. Accidents are more dramatic than diseases—perhaps this makes people more likely to remember, or more likely to recall, an accident. In 1979, a followup study by Combs and Slovic showed that the skewed probability judgments correlated strongly (0.85 and 0.89) with skewed reporting frequencies in two newspapers.2 This doesn’t disentangle whether murders are more available to memory because they are more reported-on, or whether ne
wspapers report more on murders because murders are more vivid (hence also more remembered). But either way, an availability bias is at work. Selective reporting is one major source of availability biases. In the ancestral environment, much of what you knew, you experienced yourself; or you heard it directly from a fellow tribe-member who had seen it. There was usually at most one layer of selective reporting between you, and the event itself. With today’s Internet, you may see reports that have passed through the hands of six bloggers on the way to you—six successive filters. Compared to our ancestors, we live in a larger world, in which far more happens, and far less of it reaches us—a much stronger selection effect, which can create much larger availability biases.
In real life, you’re unlikely to ever meet Bill Gates. But thanks to selective reporting by the media, you may be tempted to compare your life success to his—and suffer hedonic penalties accordingly. The objective frequency of Bill Gates is 0.00000000015, but you hear about him much more often. Conversely, 19% of the planet lives on less than $1/day, and I doubt that one fifth of the blog posts you read are written by them.
Using availability seems to give rise to an absurdity bias; events that have never happened are not recalled, and hence deemed to have probability zero. When no flooding has recently occurred (and yet the probabilities are still fairly calculable), people refuse to buy flood insurance even when it is heavily subsidized and priced far below an actuarially fair value. Kunreuther et al. suggest underreaction to threats of flooding may arise from “the inability of individuals to conceptualize floods that have never occurred . . . Men on flood plains appear to be very much prisoners of their experience . . . Recently experienced floods appear to set an upward bound to the size of loss with which managers believe they ought to be concerned.”3
Burton et al. report that when dams and levees are built, they reduce the frequency of floods, and thus apparently create a false sense of security, leading to reduced precautions.4 While building dams decreases the frequency of floods, damage per flood is afterward so much greater that average yearly damage increases. The wise would extrapolate from a memory of small hazards to the possibility of large hazards. Instead, past experience of small hazards seems to set a perceived upper bound on risk. A society well-protected against minor hazards takes no action against major risks, building on flood plains once the regular minor floods are eliminated. A society subject to regular minor hazards treats those minor hazards as an upper bound on the size of the risks, guarding against regular minor floods but not occasional major floods.
Memory is not always a good guide to probabilities in the past, let alone in the future.
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1. Sarah Lichtenstein et al., “Judged Frequency of Lethal Events,” Journal of Experimental Psychology: Human Learning and Memory 4, no. 6 (1978): 551–578, doi:10.1037/0278-7393.4.6.551.
2. Barbara Combs and Paul Slovic, “Newspaper Coverage of Causes of Death,” Journalism & Mass Communication Quarterly 56, no. 4 (1979): 837–849, doi:10.1177/107769907905600420.
3. Howard Kunreuther, Robin Hogarth, and Jacqueline Meszaros, “Insurer Ambiguity and Market Failure,” Journal of Risk and Uncertainty 7 (1 1993): 71–87, doi:10.1007/BF01065315.
4. Ian Burton, Robert W. Kates, and Gilbert F. White, The Environment as Hazard, 1st ed. (New York: Oxford University Press, 1978).
6
Burdensome Details
Merely corroborative detail, intended to give artistic verisimilitude to an otherwise bald and unconvincing narrative . . .
—Pooh-Bah, in Gilbert and Sullivan’s The Mikado1
The conjunction fallacy is when humans rate the probability P(A,B) higher than the probability P(B), even though it is a theorem that P(A,B) ≤ P(B). For example, in one experiment in 1981, 68% of the subjects ranked it more likely that “Reagan will provide federal support for unwed mothers and cut federal support to local governments” than that “Reagan will provide federal support for unwed mothers.”
A long series of cleverly designed experiments, which weeded out alternative hypotheses and nailed down the standard interpretation, confirmed that conjunction fallacy occurs because we “substitute judgment of representativeness for judgment of probability.” By adding extra details, you can make an outcome seem more characteristic of the process that generates it. You can make it sound more plausible that Reagan will support unwed mothers, by adding the claim that Reagan will also cut support to local governments. The implausibility of one claim is compensated by the plausibility of the other; they “average out.”
Which is to say: Adding detail can make a scenario SOUND MORE PLAUSIBLE, even though the event necessarily BECOMES LESS PROBABLE.
If so, then, hypothetically speaking, we might find futurists spinning unconscionably plausible and detailed future histories, or find people swallowing huge packages of unsupported claims bundled with a few strong-sounding assertions at the center. If you are presented with the conjunction fallacy in a naked, direct comparison, then you may succeed on that particular problem by consciously correcting yourself. But this is only slapping a band-aid on the problem, not fixing it in general.
In the 1982 experiment where professional forecasters assigned systematically higher probabilities to “Russia invades Poland, followed by suspension of diplomatic relations between the USA and the USSR” than to “Suspension of diplomatic relations between the USA and the USSR,” each experimental group was only presented with one proposition.2 What strategy could these forecasters have followed, as a group, that would have eliminated the conjunction fallacy, when no individual knew directly about the comparison? When no individual even knew that the experiment was about the conjunction fallacy? How could they have done better on their probability judgments?
Patching one gotcha as a special case doesn’t fix the general problem. The gotcha is the symptom, not the disease.
What could the forecasters have done to avoid the conjunction fallacy, without seeing the direct comparison, or even knowing that anyone was going to test them on the conjunction fallacy? It seems to me, that they would need to notice the word “and.” They would need to be wary of it—not just wary, but leap back from it. Even without knowing that researchers were afterward going to test them on the conjunction fallacy particularly. They would need to notice the conjunction of two entire details, and be shocked by the audacity of anyone asking them to endorse such an insanely complicated prediction. And they would need to penalize the probability substantially—a factor of four, at least, according to the experimental details.
It might also have helped the forecasters to think about possible reasons why the US and Soviet Union would suspend diplomatic relations. The scenario is not “The US and Soviet Union suddenly suspend diplomatic relations for no reason,” but “The US and Soviet Union suspend diplomatic relations for any reason.”
And the subjects who rated “Reagan will provide federal support for unwed mothers and cut federal support to local governments”? Again, they would need to be shocked by the word “and.” Moreover, they would need to add absurdities—where the absurdity is the log probability, so you can add it—rather than averaging them. They would need to think, “Reagan might or might not cut support to local governments (1 bit), but it seems very unlikely that he will support unwed mothers (4 bits). Total absurdity: 5 bits.” Or maybe, “Reagan won’t support unwed mothers. One strike and it’s out. The other proposition just makes it even worse.”
Similarly, consider the six-sided die with four green faces and two red faces. The subjects had to bet on the sequence (1) RGRRR, (2) GRGRRR, or (3) GRRRRR appearing anywhere in twenty rolls of the dice.3 Sixty-five percent of the subjects chose GRGRRR, which is strictly dominated by RGRRR, since any sequence containing GRGRRR also pays off for RGRRR. How could the subjects have done better? By noticing the inclusion? Perhaps; but that is only a band-aid, it does not fix the fundamental problem. By explicitly calculating the probabilities? That would certainly fix the fundamental problem, b
ut you can’t always calculate an exact probability.
The subjects lost heuristically by thinking: “Aha! Sequence 2 has the highest proportion of green to red! I should bet on Sequence 2!” To win heuristically, the subjects would need to think: “Aha! Sequence 1 is short! I should go with Sequence 1!”
They would need to feel a stronger emotional impact from Occam’s Razor—feel every added detail as a burden, even a single extra roll of the dice.
Once upon a time, I was speaking to someone who had been mesmerized by an incautious futurist (one who adds on lots of details that sound neat). I was trying to explain why I was not likewise mesmerized by these amazing, incredible theories. So I explained about the conjunction fallacy, specifically the “suspending relations ± invading Poland” experiment. And he said, “Okay, but what does this have to do with—” And I said, “It is more probable that universes replicate for any reason, than that they replicate via black holes because advanced civilizations manufacture black holes because universes evolve to make them do it.” And he said, “Oh.”
Rationality- From AI to Zombies Page 5