Search before Trade-Offs are Known

Abstract

Search, broadly defined, is a critical managerial activity. Our contribution is a model of search for multiattribute alternatives. Most of the search literature considers univariate alternatives, and can be applied to a multiattribute setting provided the trade-offs to be used at the final selection stage were known at the search stage. However, uncertainty about tradeoffs is likely to occur, especially in settings (e.g., vendor selection, new product development, innovation tournaments) that involve parallel search. We show that incorporating uncertainty about trade-offs into a model changes its search strategy recommendations. Failing to account for such uncertainty, which is likely in practice, leads to suboptimal search and potentially large losses. For parallel search and a multivariate elliptical (e.g., normal) distribution of the alternatives, the solution is equivalent to univariate search with appropriately adjusted standard deviation. We prove that the optimal number of draws there increases if uncertainty about tradeoffs increases.