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Why Do Simple Heuristics Perform Well in Choices with Binary Attributes?


  • Konstantinos V. Katsikopoulos

    () (Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition (ABC), 14195 Berlin, Germany)


Simple heuristics, such as deterministic elimination by aspects (DEBA) and equal weighting of attributes with DEBA as a tiebreaker (EW/DEBA), have been found to perform curiously well in choosing one out of many alternatives based on a few binary attributes. DEBA and EW/DEBA sometimes achieve near-perfect performance and complement each other (if one is wrong or does not apply, the other is correct). Here, these findings are confirmed and extended; most importantly, a theory is presented that explains them. The theory allows calculating the performance of any model, for any number of binary attributes, for any preferences of the decision maker, for all sizes of the consideration set, and for sampling alternatives with as well as without replacement. Calculations based on the theory organize previous empirical findings and provide new surprising results. For example, the performance of both DEBA and EW/DEBA is a U-shaped function of the size of the consideration set and converges relatively quickly to perfection as the size of the consideration set increases (this result holds even when the preferences of the decision maker are worst-case scenarios for the performance of the heuristics). An explanation for why DEBA and EW/DEBA complement each other is also provided. Finally, the need for a unified theory of multiattribute choice and cue-based judgment is discussed.

Suggested Citation

  • Konstantinos V. Katsikopoulos, 2013. "Why Do Simple Heuristics Perform Well in Choices with Binary Attributes?," Decision Analysis, INFORMS, vol. 10(4), pages 327-340, December.
  • Handle: RePEc:inm:ordeca:v:10:y:2013:i:4:p:327-340
    DOI: 10.1287/deca.2013.0281

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    References listed on IDEAS

    1. Manel Baucells & Juan A. Carrasco & Robin M. Hogarth, 2008. "Cumulative Dominance and Heuristic Performance in Binary Multiattribute Choice," Operations Research, INFORMS, vol. 56(5), pages 1289-1304, October.
    2. Konstantinos V. Katsikopoulos, 2011. "Psychological Heuristics for Making Inferences: Definition, Performance, and the Emerging Theory and Practice," Decision Analysis, INFORMS, vol. 8(1), pages 10-29, March.
    3. Robin M. Hogarth & Natalia Karelaia, 2005. "Simple Models for Multiattribute Choice with Many Alternatives: When It Does and Does Not Pay to Face Trade-offs with Binary Attributes," Management Science, INFORMS, vol. 51(12), pages 1860-1872, December.
    4. Carrasco, Juan A. & Baucells, Manel, 2008. "Tight upper bounds for the expected loss of lexicographic heuristics in binary multi-attribute choice," Mathematical Social Sciences, Elsevier, vol. 55(2), pages 156-189, March.
    5. Laura Martignon & Ulrich Hoffrage, 2002. "Fast, frugal, and fit: Simple heuristics for paired comparison," Theory and Decision, Springer, vol. 52(1), pages 29-71, February.
    6. Todd, Peter M., 2007. "How much information do we need?," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1317-1332, March.
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    Cited by:

    1. Katsikopoulos, Konstantinos V. & Durbach, Ian N. & Stewart, Theodor J., 2018. "When should we use simple decision models? A synthesis of various research strands," Omega, Elsevier, vol. 81(C), pages 17-25.


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