In this paper, we present Information Market based Fusion (IMF), a novel, multi-classifier combiner method for decision fusion that is based on information markets. IMF does not require training or a static ensemble composition, adjusts to changes in base-classifier accuracy, provides incentives for the base-classifiers to present truthful information, and integrates with existing multi-agent system (MAS) coordination mechanisms. We compare the effectiveness of two different IMF implementations to Majority (MAJ), Average (AVG), and Weighted Average (WAVG) schemes, using computational experiments involving 16 datasets from the UCI Machine Learning Repository and 20 different base-classifiers from Weka.