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The Surplus Identification Task and Limits to Multi-Attribute Consumer Choice

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  • Lunn, Pete
  • Bohacek, Marek
  • McGowan, Feidhlim

Abstract

We present a novel experimental method for investigating consumer choice. The Surplus Identification (S-ID) task is inspired by studies of detection in perceptual psychophysics. It employs a forced-choice procedure, in which participants must decide whether a novel product is worth more or less than the price at which it is being offered, that is, whether there is a positive or negative surplus. The SI-D task reveals how precision, bias and learning vary across attribute and price structures. We illustrate its use by testing for cognitive capacity constraints in multi-attribute choice in three separate experiments, with implications for models of bounded rationality and rational inattention. As the number of product attributes rises from one to four in the S-ID task (Experiment 1), participants cannot integrate additional information efficiently and they display systematic, persistent biases, despite incentivised opportunities to learn. Experiment 2 demonstrates how the S-ID task can be used to track learning and serves as a robustness check for the findings of Experiment 1. Experiment 3 adapts the S-ID task to test accuracy of surplus identification when multiple attributes are perfectly correlated. The S-ID task also has the potential to test multiple aspects of consumer choice models and to test specific hypotheses about the cognitive mechanisms behind surplus identification.

Suggested Citation

  • Lunn, Pete & Bohacek, Marek & McGowan, Feidhlim, 2016. "The Surplus Identification Task and Limits to Multi-Attribute Consumer Choice," Papers WP536, Economic and Social Research Institute (ESRI).
  • Handle: RePEc:esr:wpaper:wp536
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    References listed on IDEAS

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    Cited by:

    1. Peter D. Lunn & Marek Bohacek, 2017. "Price transparency in residential electricity: Experiments for regulatory policy," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 1(2), pages 31-37, September.

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