The Global Knowledge Game
To illustrate that global knowledge is a game, consider a story about Alexander Luria, who studied illiterate Russian peasants and their semi-literate children. Consider especially this version of the story, prepared in the 1970s to provide morale and context to reading teachers (John Guthrie, 1977). Essentially, Luria discovered that the illiterate, unschooled peasants were highly resistant to syllogisms and word games. The adult peasants would only answer questions based on their own knowledge, and stubbornly refused to make deductions from given premises. “All bears are white where it is snowy. It is snowy in Nova Zembla. What color are the bears in Nova Zembla?” “I don’t know, I have never been to Nova Zembla.” Children with only a year or two of education, however, were easily able to engage in such abstract reasoning. They quickly answered the syllogisms and drew inferences from hypothetical facts outside of their own observation.
In this story, I argue, Luria’s peasants are indexical geniuses, who refuse to engage in unproven syllogistic games. They are not interested in a global, universal game. Their children, however, are easily introduced to this game by the process of schooling and literacy.
Interestingly, a more recent group of researchers claim that illiterate people do fine at making inferences against experience, if the context is given as a distant planet (Dias et al., 2005). I am not offering this as true, but as a story about how expecting people to operate in the “global knowledge game” might portray them as stupider than they really are, if they simply choose not to play in that game. This is to segue into the next hermeneutic pass, in which we are told that the hype surrounding “cognitive bias” is really a sort of science magic trick, an illusion designed to portray indexical geniuses, like Luria’s peasants and ourselves, as global fools.
The paper is “The Bias Bias in Behavioral Economics,” by Gerd Gigerenzer (2018). If you, like me, have ever been fascinated by cognitive bias research, this is a brutal paper to come to terms with. Gigerenzer examines several purported biases in what I would call analytic reasoning or the global knowledge game, and finds explanations for these purported biases in the indexical reality of humans.
For instance, some apparent “biases” that people display about probability are not actually errors. For the small (and in most cases, merely finite) samples that reality has to offer, people’s “biased” intuitions are more accurate than a “globally correct” answer would be (that is, the correct answer if the sample were infinite). In tossing fair coins, people tend to intuit that irregular strings are more probable than more regular strings (e.g. that HHHT is more probable than HHHH in a sequence of coin flips). This simple intuition can’t be correct, though, because given infinite coin flips, each string is as likely as any other, and if the sequence is only four flips, after HHH, each outcome is equally likely. But for small, finite numbers of flips greater than the string length, Gigerenzer argues, it is the human intuition that is correct, not the naive global solution: HHHT does take less time to show up than HHHH in repeated simulations, and is more commonly encountered in small samples. To drive home his point, he offers a bet:
If you are still not convinced, try this bet (Hahn and Warren, 2010), which I will call the law-of-small-numbers bet:
You flip a fair coin 20 times. If this sequence contains at least one HHHH, I pay you $100. If it contains at least one HHHT, you pay me $100. If it contains neither, nobody wins.
More broadly, cognitive bias proponents find fault with their subjects for treating “logically equivalent” language statements as having different meanings, when context reveals that these “logically irrelevant” cues frequently do reveal rich meaning in practice. For instance, people react differently to the “same” information presented negatively vs. positively (10% likelihood of death vs. 90% likelihood of survival). Cognitive bias proponents frame this as an error, but Gigerenzer argues that when people make this “error,” they are making use of meaningful context that a “bias-free” robot would miss.