Complexity and Choice
We develop a model of satisficing with evaluation errors that incorporates complexity at the level of individual alternatives. We test the model predictions in a novel data set with information on hundreds of millions of chess moves by experienced players. Consistent with the theory, complex optimal moves are chosen less frequently than simpler ones. Choice frequencies of suboptimal moves follow the opposite pattern. The former finding distinguishes satisficing from a large class of maximization-based models. We further document that skill and time moderate the adverse effect of complexity, and that they complement each other in doing so. Finally, we provide evidence that suboptimal behavior also hinges on the composition of the choice set but not its size. Our findings help to shed some of the first light on the importance of complexity outside of the laboratory.
We are grateful to Sandeep Baliga, Tim Feddersen, Peter Klibanoff, Daniel Martin, Pablo Montagnes, and David Smerdon, as well as audiences at Boston College, Monash University, Northwestern, University of Alicante, UC Santa Barbara, and SITE for helpful suggestions. This research was supported in part through the computational resources provided by the Quest high-performance computing facility at Northwestern University. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
- Many decisions involve choosing among complex options, and it may be difficult to assess the value of each alternative. In Complexity...