Effects of approximation on an iterative combinatorial auction

Abstract

Combinatorial auctions are a promising way of solving the complicated heterogeneous resource allocation problem. However combinatorial auctions present inherently hard computational problems for agents and auctioneers. In particular (1) the auction must solve the winner determination problem, which is NP-complete and (2) the agents must solve the value determination problem which can be intractable when computing a precise valuation for the bundles.The winner determination problem can be addressed by (1) developing algorithms which solve the problem of computing the optimal solution; (2) developing heuristics which approximate the solution quickly. The combinatorial auctions, proposed in the literature are dependent on the optimal solution of the winner determination problem, which is intractable for large number of agents and items.In this thesis we study the effects of using approximate winner determination in a particular iterative combinatorial auction : A1BA. We categorize the effects of approximation and develop several corrective methods which reduce the errors due to approximation in several categories.