
Fast and Frugal Heuristics
Gerd Gigerenzer 
Max Planck Institute for Human Development, Germany 
The expected utility framework has shaped our ideal of rational decisionmaking: making optimal tradeoffs by weighting and summing. Yet many interesting problems, including welldefined ones such as chess, are computationally intractable. That is, the optimal solution cannot be found, neither by mind nor by machine. What do people do when optimization is out of reach? My hypothesis is that we often rely on fast and frugal heuristics. Today, I will discuss one class of heuristics that employ sequential search and onereason decision making. This class includes take the best, fast and frugal trees, and the priority heuristic. I show that simple heuristics can make predictions as good as or better than multiple regression and other complex strategies do, yet faster and at less cost. The priority heuristic predicts (i) Allais’ paradox, (ii) risk aversion for gains if probabilities are high, (iii) risk seeking for gains if probabilities are low (lottery tickets), (iv) risk aversion for losses if probabilities are low (buying insurance), (v) risk seeking for losses if probabilities are high, and other violations of expected utility theory. Across four studies, it outdoes cumulative prospect theory in predicting people’s majority choices. The systematic study of adaptive heuristics can provide a psychologically informed alternative to the expected utility framework.
Readings:
Gigerenzer, G. (2004). Fast and frugal heuristics: The tools of bounded rationality. In D. Koehler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making (pp. 62–88). Oxford, UK: Blackwell.
Gigerenzer, G., & Selten, R. (Eds.). (2001). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press.