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Multiple attribute search is a central feature of economic life: we consider much more than price when purchasing a home, and more than wage when choosing a job. Nevertheless, while single attribute search problems have been studied extensively, little is known about optimal search in multiple attribute environments. This paper provides the first partial characterization of optimal sequential search in a problem with multiple searchable attributes and alternatives, no order restrictions on search, and full recall,. When the partial rational benchmark is applied to a rich dataset subjects are found to systematically deviate from optimal search by (1) searching too deeply within alternatives and (2) switching too adjacently between alternatives. These patterns of behavior result in considerable economic losses, can be exploited by producers, and cannot be explained by existing behavioral models.
