Sequential Procurement with Contractual and Experimental Learning

We study the design of sequential procurement strategies that integrate stochastic and strategic information. We consider a buyer who repeatedly demands a certain good and is unable to commit to long-term contracts. In each time period, the buyer makes a price offer to a seller who has private, persistent information regarding his or her cost and quality of provision. If the offer is accepted, the seller provides the good with a stochastic quality that is not contractible. Therefore, the buyer can learn from the (strategic) acceptance decisions taken by the seller and from evaluations of the (stochastic) quality delivered whenever a purchase occurs. Hence, the buyer not only faces a tradeoff between exploration and exploitation but also needs to decide how to explore: by facilitating quality experimentation or by strategically separating seller types. We characterize the perfect Bayesian equilibria of this sequential interaction and show that the buyer’s equilibrium strategy consists of a dynamic sequence of thresholds on his or her belief on the seller’s type. When only one seller type is more efficient than the buyer’s outside option, the buyer uses one form of information: either strategic or stochastic. If both seller types are more efficient, then the buyer uses both forms of information; at the early stages of the interaction, the buyer offers high prices that incentivize trade and quality experimentation, and after gathering enough information, the buyer may advance to offering low prices that partially separate seller types. We identify the effect strategic sellers have on the buyer’s optimal strategy relative to more traditional learning dynamics and establish that, paradoxically, when sellers are strategic, the ability to observe delivered quality is not always beneficial for the buyer. This paper was accepted by Victor Martínez-de-Albéniz, operations management.