The University of Michigan Digital Library (UMDL) is designed as an open system that allows third parties to build and integrate their own profit-seeking agents into the marketplace of information goods and services. The profit-seeking behavior of agents, however, risks inefficient allocation of goods and services, as agents take strategic stances that might backfire. While it would be good if we could impose mechanisms to remove incentives for strategic reasoning, this is not possible in the UMDL. Therefore, our approach has instead been to study whether encouraging the other extreme - making strategic reasoning ubiquitous - provides an answer. Toward this end, we have designed a strategy (called the p-strategy) that uses a stochastic model of the market to find the best offer price. We have then examined the collective behavior of p-strategy agents in the UMDL auction. Our experiments show that strategic thinking is not always beneficial and that the advantage of being strategic decreases with the arrival of equally strategic agents. Furthermore, a simpler strategy can be as effective when enough other agents use the p-strategy. Consequently, we expect the UMDL is likely to evolve to a point where some agents use simpler strategies and some use the p-strategy.
Bibliographical noteFunding Information:
This research has been funded in part by the joint NSFrDARPArNASA Digital Libraries Initiative under CERA IRI-9411287 and by NSF grant 9872057. The first author has been partially supported by the Horace H. Rackham Barbour scholarship, and the ITECC Center at Rutgers University.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence