Strategic Bidding in Knapsack Auctions

Résumé 0

In the Knapsack Problem a set of indivisible objects, each with different values and sizes, must be packed into a fixed-size knapsack to maximize the total value. The knapsack problem is known to be an NP-hard problem even when there is full information regarding values and sizes. In many real-world situations, however, the values of objects are private information, which adds another dimension of complexity. In this paper we examine the knapsack problem with private information by investigating three practical auctions as possible candidates for payment rules in a setup where the knapsack owner sells the space to object owners via an auction. The three auctions are the discriminatory price, the generalized second-price and the uniform-price auctions. Using a Greedy algorithm for allocating objects, we analyze bidding behavior, revenue and efficiency of these three auctions using theory, lab experiments, and AI-enriched simulations. Our results suggest that the uniform-price auction has the highest level of truthful bidding and efficiency while the discriminatory price and the generalized second-price auctions are superior in terms of revenue generation.