Recombinant Estimation for Normal-Form Games, with Applications to Auctions and Bargaining

In empirical analysis of economic games, researchers frequently wish to estimate quantities describing group outcomes, such as the expected revenue in an auction or the mean allocative efficiency in a market experiment. For such applications, we propose an improved statistical estimation technique called "recombinant estimation." The technique takes observations of the complete strategy of each player and recombines them to compute all the possible group outcomes which could have resulted from different matches of players. We calculate the improvement in efficiency of the recombinant estimator relative to the standard estimator, and show how to estimate standard errors for the recombinant estimator for use in hypothesis testing. We present an application to a two-player sealed-bid auction and a two-player ultimatum bargaining game. In these applications, the improved efficiency of our estimator is equivalent to an increase of between 40% and 200% in the sample size. We discuss how to design game experiments in order to be able to take full advantage of recombinant estimation. Finally, we discuss practical computational issues, showing how one can avoid combinatorial explosions of computing time while still yielding significantly improved efficiency of estimation.