Evaluating Case-based Decision Theory: Predicting Empirical Patterns of Human Classification Learning (Extensions)
Andreas Pape and
MPRA Paper from University Library of Munich, Germany
We introduce a computer program which calculates an agent’s optimal behavior according to Case-based Decision Theory (Gilboa and Schmeidler, 1995) and use it to test CBDT against a benchmark set of problems from the psychological literature on human classification learning (Shepard et al., 1961). This allows us to evaluate the efficacy of CBDT as an account of human decision-making on this set of problems. We find: (1) The choice behavior of this program (and therefore Case-based Decision Theory) correctly predicts the empirically observed relative difficulty of problems and speed of learning in human data. (2) ‘Similarity’ (how CBDT decision makers extrapolate from memory) is decreasing in vector distance, consistent with evidence in psychology (Shepard, 1987). (3) The best-fitting parameters suggest humans aspire to an 80 − 85% success rate, and humans may increase their aspiration level during the experiment. (4) Average similarity is rejected in favor of additive similarity.
Keywords: Case-based Decision Theory; Human Cognition; Learning; Agent-based Computational Economics; Psychology; Cognitive Science (search for similar items in EconPapers)
JEL-codes: C63 C88 D83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cbe, nep-cmp and nep-exp
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