Decentralized computation procurement and computational robustness in a smart market

Abstract

Several `smart market' mechanisms have recently appeared in the literature. These mechanisms combine a computer network that collects bids from agents with a central computer that selects a schedule of bids to fill based upon maximization of revenue or trading surplus. Potential problems exist when this optimization involves combinatorial difficulty sufficient to overwhelm the central computer. This paper explores the use of a computation procuring clock auction to induce human agents to approximate the solutions to discrete constrained optimization problems. Economic and computational properties of the auction are studied through a series of laboratory experiments. The experiments are designed around a potential application of the auction as a secondary institution that approximates the solution to difficult computational problems that occur within the primary `smart market', and show that the auction is effective and robust in eliciting and processing suggestions for improved schedules.