  We study interactions between strategic players and markets whose behavior is
guided by an algorithm. Algorithms use data from prior interactions and a
limited set of decision rules to prescribe actions. While as-if rational play
need not emerge if the algorithm is constrained, it is possible to guide
behavior across a rich set of possible environments using limited details.
Provided a condition known as weak learnability holds, Adaptive Boosting
algorithms can be specified to induce behavior that is (approximately) as-if
rational. Our analysis provides a statistical perspective on the study of
endogenous model misspecification.
