Computational analysis of perfect-information position auctions

After experimentation with other designs, major search engines converged on weighted, generalized second-price auctions (wGSPs) for selling keyword advertisements. Theoretical analysis is still not able to settle the question of why they found this design preferable to other alternatives. We approach this question in a new way, adopting an analytical paradigm we dub “computational mechanism analysis.” Specifically, we sample position auction games from a given distribution, encode them in a computationally efficient representation language, compute their Nash equilibria, and calculate economic quantities of interest. We considered seven widely studied valuation models from the literature and three position auction variants. We found that wGSP consistently showed the best ads of any position auction, measured both by social welfare and expected number of clicks. In contrast, we found that revenue was extremely variable across auction mechanisms and was highly sensitive to equilibrium selection, the preference model, and the valuation distribution.