The work Gigerenzer will be talking about on Wednesday goes well beyond the prior research that demonstrated that people are good at solving Bayesian reasoning problems when asked to reason about event frequencies. He has found evidence that people make judgments using algorithms that are "fast and frugal". These algorithms differ from the kind of heuristics suggested by Tversky, Kahneman, and others, in a very important manner: rather than being "quick and dirty", these algorithms are "quick and clean". I.e., they produce very well-calibrated judgments - in many cases, judgments that are as good or better than those generated by more sophisticated computational methods that attempt to integrate more types of information.
This work has important implications for cognitive scientists, economists,
and also for biologists working on foraging or other problems involving
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L. J., Beatty, J., and Krueger, L. (1989). The empire of chance. How probability changed science and everyday life. Cambridge, UK: Cambridge University Press.
Gigerenzer, G. & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650-669.
Gigerenzer, G. & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102, 684-704.
Gigerenzer, G. (1991). From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review, 98, 254-267.