Are these cognitive biases, biases?

35 Kaj_Sotala 23 December 2009 05:27PM

Continuing my special report on people who don't think human reasoning is all that bad, I'll now briefly present some studies which claim that phenomena other researchers have considered signs of faulty reasoning aren't actually that. I found these from Gigerenzer (2004), which I in turn found when I went looking for further work done on the Take the Best algorithm.

Before we get to the list - what is Gigerenzer's exact claim when he lists these previous studies? Well, he's saying that minds aren't actually biased, but may make judgments that seem biased in certain environments.

Table 4.1 Twelve examples of phenomena that were first interpreted as "cognitive illusions" but later revalued as reasonable judgments given the environmental structure. [...]

The general argument is that an unbiased mind plus environmental structure (such as unsystematic error, unequal sample sizes, skewed distributions) is sufficient to produce the phenomenon. Note that other factors can also contribute to some of the phenomena. The moral is not that people would never err, but that in order to understand good and bad judgments, one needs to analyze the structure of the problem or of the natural environment.

On to the actual examples. Of the twelve examples referenced, I've included three for now.

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Fundamentally Flawed, or Fast and Frugal?

41 Kaj_Sotala 20 December 2009 03:10PM

Whenever biases are discussed around here, it tends to happen under the following framing: human cognition is a dirty, jury-rigged hack, only barely managing to approximate the laws of probability even in a rough manner. We have plenty of biases, many of them a result of adaptations that evolved to work well in the Pleistocene, but are hopelessly broken in a modern-day environment.

That's one interpretation. But there's also a different interpretation: that a perfect Bayesian reasoner is computationally intractable, and our mental algorithms make for an excellent, possibly close to an optimal, use of the limited computational resources we happen to have available. It's not that the programming would be bad, it's simply that you can't do much better without upgrading the hardware. In the interest of fairness, I will be presenting this view by summarizing a classic 1996 Psychological Review article, "Reasoning the Fast and Frugal Way: Models of Bounded Rationality" by Gerd Gigerenzer and Daniel G. Goldstein. It begins by discussing two contrasting views: the Enlightenment ideal of the human mind as the perfect reasoner, versus the heuristics and biases program that considers human cognition as a set of quick-and-dirty heuristics.

Many experiments have been conducted to test the validity of these two views, identifying a host of conditions under which the human mind appears more rational or irrational. But most of this work has dealt with simple situations, such as Bayesian inference with binary hypotheses, one single piece of binary data, and all the necessary information conveniently laid out for the participant (Gigerenzer & Hoffrage, 1995). In many real-world situations, however, there are multiple pieces of information, which are not independent, but redundant. Here, Bayes’ theorem and other “rational” algorithms quickly become mathematically complex and computationally intractable, at least for ordinary human minds. These situations make neither of the two views look promising. If one would apply the classical view to such complex real-world environments, this would suggest that the mind is a supercalculator like a Laplacean Demon (Wimsatt, 1976)— carrying around the collected works of Kolmogoroff, Fisher, or Neyman—and simply neds a memory jog, like the slave in Plato’s Meno. On the other hand, the heuristics-and-biases view of human irrationality would lead us to believe that humans are hopelessly lost in the face of real-world complexity, given their supposed inability to reason according to the canon of classical rationality, even in simple laboratory experiments.

There is a third way to look at inference, focusing on the psychological and ecological rather than on logic and probability theory. This view questions classical rationality as a universal norm and thereby questions the very definition of “good” reasoning on which both the Enlightenment and the heuristics-and-biases views were built. Herbert Simon, possibly the best-known proponent of this third view, proposed looking for models of bounded rationality instead of classical rationality. Simon (1956, 1982) argued that information-processing systems typically need to satisfice rather than optimize. Satisficing, a blend of sufficing and satisfying, is a word of Scottish origin, which Simon uses to characterize algorithms that successfully deal with conditions of limited time, knowledge, or computational capacities. His concept of satisficing postulates, for instance, that an organism would choose the first object (a mate, perhaps) that satisfies its aspiration level—instead of the intractable sequence of taking the time to survey all possible alternatives, estimating probabilities and utilities for the possible outcomes associated with each alternative, calculating expected utilities, and choosing the alternative that scores highest.

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