Two empirical reasons not to take the extreme scope neglect in studies like the 2,000 vs 200,000 birds one as directly reflecting people’s values.
First, the results of studies like this depend on how you ask the question. A simple variation which generally leads to more scope sensitivity is to present the two options side by side, so that the same people would be asked both about 2,000 birds and about the 200,000 birds (some call this “joint evaluation” in contrast to “separate evaluation”). Other variations also generally produce more scope sensitive results (this Wikipedia article seems uneven in quality but gives a flavor for some of those variations.) The fact that this variation exists means that just take people’s answers at face value does not work as a straightforward approach to understanding people’s values, and I think the studies which find more scope sensitivity often have a strong case for being better designed.
Second, there are variants of scope insensitivity which involve things other than people’s values. Christopher Hsee has done a number of studies in the context of consumer choice, where the quantity is something like the amount of ice cream that you get or the number of entries in a dictionary, which find scope insensitivity under separate evaluation (but not under joint evaluation), and there is good reason to think that people do prefer more ice cream and more comprehensive dictionaries. Daniel Kahneman has argued that several different kinds of extension neglect all reflect similar cognitive processes, including scope neglect in the bird study, base rate neglect in the Tom W problem, and duration neglect in studies of colonoscopies. And superforecasting researchers have found that ordinary forecasters neglect scope in questions like (in 2012) “How likely is it that the Assad regime will fall in the next three months?” vs. “How likely is it that the Assad regime will fall in the next six months?”; superforecasters’ forecasts are more sensitive to the 3 month vs. 6 month quantity (there’s a passage in Superforecasting about this which I’ll leave as a reply, and a paper by Mellers & colleagues with more examples). These results suggest that people’s answers to questions about values-at-scale has a lot to do with how people think about quantities, that “how people think about quantities” is a fairly messy empirical matter, and that it’s fairly common for people’s thinking about quantities to involve errors/biases/limitations which make their answers less sensitive to the size of the quantity.
This does not imply that the extreme scope sensitivity common in effective altruism matches people’s values; I think that claim requires more of a philosophical argument rather than an empirical one. Just that the extreme scope insensitivity found in some studies probably doesn’t match people’s values.
Flash back to early 2012. How likely is the Assad regime to fall? Arguments against a fall include (1) the regime has well-armed core supporters; (2) it has powerful regional allies. Arguments in favor of a fall include (1) the Syrian army is suffering massive defections; (2) the rebels have some momentum, with fighting reaching the capital. Suppose you weight the strength of these arguments, they feel roughly equal, and you settle on a probability of roughly 50%.
But notice what’s missing? The time frame. It obviously matters. To use an extreme illustration, the probability of the regime falling in the next twenty-four hours must be less—likely a lot less—than the probability that it will fall in the next twenty-four months. To put this in Kahneman’s terms, the time frame is the “scope” of the forecast.
So we asked one randomly selected group of superforecasters, “How likely is it that the Assad regime will fall in the next three months?” Another group was asked how likely it was in the next six months. We did the same experiment with regular forecasters.
Kahneman predicted widespread “scope insensitivity.” Unconsciously, they would do a bait and switch, ducking the hard question that requires calibrating the probability to the time frame and tackling the easier question about the relative weight of the arguments for and against the regime’s downfall. The time frame would make no difference to the final answers, just as it made no difference whether 2,000, 20,000, or 200,000 migratory birds died. Mellers ran several studies and found that, exactly as Kahneman expected, the vast majority of forecasters were scope insensitive. Regular forecasters said there was a 40% chance Assad’s regime would fall over three months and a 41% chance it would fall over six months.
But the superforecasters did much better: They put the probability of Assad’s fall at 15% over three months and 24% over six months. That’s not perfect scope sensitivity (a tricky thing to define), but it was good enough to surprise Kahneman. If we bear in mind that no one was asked both the three- and six-month version of the question, that’s quite an accomplishment. It suggests that the superforecasters not only paid attention to the time frame in the question but also thought about other possible time frames—and thereby shook off a hard-to-shake bias.
Note: in the other examples studied by Mellers & colleagues (2015), regular forecasters were less sensitive to scope than they should’ve been, but they were not completely insensitive to scope, so the Assad example here (40% vs. 41%) is unusually extreme.
Two empirical reasons not to take the extreme scope neglect in studies like the 2,000 vs 200,000 birds one as directly reflecting people’s values.
First, the results of studies like this depend on how you ask the question. A simple variation which generally leads to more scope sensitivity is to present the two options side by side, so that the same people would be asked both about 2,000 birds and about the 200,000 birds (some call this “joint evaluation” in contrast to “separate evaluation”). Other variations also generally produce more scope sensitive results (this Wikipedia article seems uneven in quality but gives a flavor for some of those variations.) The fact that this variation exists means that just take people’s answers at face value does not work as a straightforward approach to understanding people’s values, and I think the studies which find more scope sensitivity often have a strong case for being better designed.
Second, there are variants of scope insensitivity which involve things other than people’s values. Christopher Hsee has done a number of studies in the context of consumer choice, where the quantity is something like the amount of ice cream that you get or the number of entries in a dictionary, which find scope insensitivity under separate evaluation (but not under joint evaluation), and there is good reason to think that people do prefer more ice cream and more comprehensive dictionaries. Daniel Kahneman has argued that several different kinds of extension neglect all reflect similar cognitive processes, including scope neglect in the bird study, base rate neglect in the Tom W problem, and duration neglect in studies of colonoscopies. And superforecasting researchers have found that ordinary forecasters neglect scope in questions like (in 2012) “How likely is it that the Assad regime will fall in the next three months?” vs. “How likely is it that the Assad regime will fall in the next six months?”; superforecasters’ forecasts are more sensitive to the 3 month vs. 6 month quantity (there’s a passage in Superforecasting about this which I’ll leave as a reply, and a paper by Mellers & colleagues with more examples). These results suggest that people’s answers to questions about values-at-scale has a lot to do with how people think about quantities, that “how people think about quantities” is a fairly messy empirical matter, and that it’s fairly common for people’s thinking about quantities to involve errors/biases/limitations which make their answers less sensitive to the size of the quantity.
This does not imply that the extreme scope sensitivity common in effective altruism matches people’s values; I think that claim requires more of a philosophical argument rather than an empirical one. Just that the extreme scope insensitivity found in some studies probably doesn’t match people’s values.
A passage from Superforecasting:
Note: in the other examples studied by Mellers & colleagues (2015), regular forecasters were less sensitive to scope than they should’ve been, but they were not completely insensitive to scope, so the Assad example here (40% vs. 41%) is unusually extreme.