On Evaluating Information Revelation Policies in Procurement Auctions: A Markov Decision Process Approach

Each market session in a reverse electronic marketplace features a procurer and many suppliers. An important attribute of a market session chosen by the procurer is its information revelation policy. The revelation policy determines the information (such as the number of competitors, the winning bids, etc.) that will be revealed to participating suppliers at the conclusion of each market session. Suppliers participating in multiple market sessions use strategic bidding and fake their own cost structure to obtain information revealed at the end of each market session. The information helps to reduce two types of uncertainties encountered in future market sessions, namely, their opponents' cost structure and an estimate of the number of their competitors. Whereas the first type of uncertainty is present in physical and e-marketplaces, the second type of uncertainty naturally arises in IT-enabled marketplaces. Through their effect on the uncertainty faced by suppliers, information revelation policies influence the bidding behavior of suppliers which, in turn, determines the expected price paid by the procurer. Therefore, the choice of information revelation policy has important consequences for the procurer. This paper develops a partially observable Markov decision process model of supplier bidding behavior and uses a multiagent e-marketplace simulation to analyze the effect that two commonly used information revelation policies—complete information policy and incomplete information policy—have on the expected price paid by the procurer. We find that the expected price under the complete information policy is lower than that under the incomplete information policy. The integration of ideas from the multiagents literature, the machine-learning literature, and the economics literature to develop a method to evaluate information revelation policies in e-marketplaces is a novel feature of this paper.