How Rational are your Choice Data?

We present an algorithm that finds the size of the largest subset of a choice data set that is consistent with acyclicality. Our algorithm is orders of magnitude more efficient than existing methods. It can be used to calculate measures of rationality, such as the Houtman-Maks index and the minimum multiple rationales, that have been previously impractical. We also develop a new measure of how close a data set is to rationality. Further, we demonstrate the efficiency of our algorithm on data from laboratory experiments [Choi et al. 2007] and household consumption data [Blundell, Browning and Crawford 2003, 2008]. In doing so, we provide previously unavailable rationality measures for these data sets.

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