Space Complexity in Choice

I introduce a measure of choice complexity based on the working memory load that a decision maker experiences as she sequentially processes information over multiple alternatives and attributes, in any order. The measure is analogous to the space complexity of an algorithm in computational theory. I characterize the minimum complexity orders, formalizing the intuitions that «considering one alternative at a time» and considering attributes in a «systematic» way minimizes complexity. I then build a model of choice (error) as a function of complexity, and test it on the data of an existing choice experiment. The simplest one-parameter version of the model successfully tracks a complicated pattern of choice errors across six treatments. Finally, I use the model and data to estimate the choice error-complexity curve, and find that choice error is roughly linearly increasing in complexity.

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