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How do people learn to allocate resources? Comparing two learning theories.

Jörg Rieskamp1, Jerome R Busemeyer, Tei Laine

  • 1Department of Psychology, Indiana University Bloomington, Bloomington, IN, USA. neskamp@ mpib-berlin.mpg.de

Journal of Experimental Psychology. Learning, Memory, and Cognition
|November 19, 2003
PubMed
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People learn resource allocation through comparing current decisions to past successes, not by analyzing all historical data. The local adaptation model better explains this learning process than global search models.

Area of Science:

  • Cognitive Psychology
  • Behavioral Economics
  • Decision Science

Background:

  • Understanding how individuals learn to allocate resources is crucial in various fields, including economics and psychology.
  • Existing models of learning offer different mechanisms for how past experiences influence future decisions.

Purpose of the Study:

  • To compare two major learning models: a global search model and a local adaptation model.
  • To determine which model better explains human learning in resource allocation tasks.

Main Methods:

  • Two studies were conducted where participants repeatedly allocated a capital resource among three financial assets.
  • The global search model assumes probabilistic allocation based on the entire history of past decisions.
  • The local adaptation model assumes allocation based on comparing the current decision to the single most successful past decision.

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Main Results:

  • Participants demonstrated significant learning effects in resource allocation over time.
  • However, the optimal allocation strategy was frequently not achieved.
  • Model calibration from Study 1 allowed for a priori predictions tested in Study 2.

Conclusions:

  • The local adaptation model provides a superior explanation for learning in resource allocation compared to the global search model.
  • This suggests that individuals tend to simplify their learning process by focusing on recent, successful outcomes rather than a comprehensive analysis of all past data.