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. 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. This doesn’t disentangle whether murders are more available to memory because they are more reported-on, or whether newspapers 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.”1
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.2 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.
1 Howard Kunreuther, Robin Hogarth, and Jacqueline Meszaros, “Insurer Ambiguity and Market Failure,” Journal of Risk and Uncertainty 7 (1 1993): 71–87.
2 Ian Burton, Robert W. Kates, and Gilbert F. White, The Environment as Hazard, 1st ed. (New York: Oxford University Press, 1978).
Lichtenstein et aliōrum research subjects were 1) college students and 2) members of a chapter of the League of Women Voters. Students thought that accidents are 1.62 times more likely than diseases, and league members thought they were 11.6 times more likely (geometric mean). Sadly, no standard deviation was given. The true value is 15.4. Note that only 57% and 79% of students and league members respectively got the direction right, which further biased the geometric average down.
There were some messed up answers. For example, students thought that tornadoes killed more people than asthma, when in fact asthma kills 20x more people than tornadoes. All accidents are about as likely as stomach cancer (well, 1.19x more likely), but they were judged to be 29 times more likely. Pairs like these represent a minority, and subjects were generally only bad at guessing which cause of death was more frequent when the ratio was less than 2:1. These are the graphs from the paper.
The following excerpt is from Judged Frequency Of Lethal Events by Lichtenstein, Slovic, Fischhoff, Layman and Combs.
There were more instructions about relative likelihoods and scales. And there was a glossary to help the people understand some categories.
Note that there was nothing about “old age” anywhere. There is no such thing as “death by old age,” but I’ll risk generalizing from my own example to say that some people think there is. And even those who know there isn’t might think, despite the instructions, “Oh, darnit, I forgot that old people count, too.”
I wish I’d tested myself BEFORE reading the correct answer. As near as I could tell, I would’ve been correct about homicide vs. suicide, but wrong about diseases vs. accidents (“Old people count, too!” facepalm). I wouldn’t even bother guessing the relative frequency. I didn’t have a clue.
When I need to know the number of square feet in an acre, or the world population it takes me seconds to get from the question to the answer. I dutifully spent ~20 minutes googling the CDC website, looking for this. It wasn’t even some heroic effort, but it’s not something I, or most other people, would casually expend on every question that starts with, “Huh, I wonder….” (we should, but we don’t).
As for what I found: I dare you, click on my link and see table 9. (http://www.cdc.gov/NCHS/data/nvsr/nvsr58/nvsr58_19.pdf). Did you? If you did, you would’ve seen that Zubon2 was right in this comment. Accidents win by quite a margin in the 15-44 demographic. I couldn’t find 1978 data, but I’d expect it to be similar (Lichtenstein’s et al tables are no help because they pool all age groups).
I spent the last two hours looking at these tables. Ask me anything! … I won’t be able to answer. Unless I have the CDC tables in front of me, I might not even do much better on Lichtenstein et aliōrum questionnaire than a typical subject (well, at least, I know tornadoes have frequency; measles doesn’t—I’ll get that question right). I suppose that people who haven’t looked at the CDC table are getting all of their information from fragmented reports like “Drive safely! Traffic accidents is the leading cause of death among teenagers who !” or “Buy our drug! is the leading cause of death in over 55!” or “5-star exhaust pipe crash safety rating!” Humans aren’t good at integrating these fragments.
Memory is a bad guide to probability estimates. But what’s the alternative? Should we carry tables around with us?
Personally, I hope that someday data that is already out there in the public domain will be made easily accessible. I hope that finding the relative frequencies of measles-related deaths and tornado-related deaths will be as quick as finding the number of square feet in an acre or the world population, and that political squabble will focus on whether or not certain data should be in the public domain (“You can’t force hospitals to put their data online! That violates the patients’ right to privacy!” “Well, but….”)
Note: repost from SEQ RERUN.