Ecological Rationality

Which city has more inhabitants: Detroit or Milwaukee? If you don’t know the correct answer, you are not alone. In a study, we asked this question to German and US students and students from neither country knew that the answer was Detroit. However, German students intuitively gave the correct answer much more often than students in the US did. Why? German students were able to rely on a simple, intuitive rule of thumb, the so-called recognition heuristic: If you only recognize one of two objects, then infer that the recognized object has a higher value (in this case more inhabitants).This rule of thumb works well as long as we don’t know too much about a topic. Having heard about both cities many times before, the U.S. students could not apply the recognition heuristic and selected the right answer less often.

Recognition Heuristics
© Max-Planck-Institut für Bildungsforschung

Note: Impact of quality: High-quality objects are mentioned more frequently in the media than low-quality objects. Impact of publicity: Objects more frequently mentioned in the media are more likely to be recognized. Recognition validity: Objects that are recognized more often are thus more often of higher quality (cf. Goldstein & Gigerenzer, 2002).

Ecological rationality studies humans in real-world domains and explores which heuristics are promising in which environment. For example, the recognition heuristic works only if there is a correlation between recognition of an object (name of a city) and quality of the object (size of the city) – for instance, that bigger cities appear more often in the media than smaller ones – and if only one of the two objects is recognized (as did the German students but not the U.S. students).

Key References

  • Todd, P. M., Gigerenzer, G., & the ABC Research Group (2012). Ecological rationality: Intelligence in the world. New York: Oxford University Press.
  • Katsikopoulos, K. V. (2011). Psychological heuristics for making inferences: Definition, performance, and the emerging theory and practice, Decision Analysis, 8, 1.

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