Emergent properties of a market-based digital library with strategic agents

The University of Michigan Digital Library (UMDL) is 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 one could impose mechanisms to remove incentives for strategic reasoning, such mechanisms are not possible in the UMDL. Therefore, the authors´ approach has instead been to study whether encouraging the other extreme-making strategic reasoning ubiquitous-provides an answer. Toward this end, they have designed a strategy (called p-strategy) that uses a stochastic model of the market to find the best offer price. They have then examined the collective behavior of p-strategy agents in the UMDL auction. Their 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 simple strategy can be as effective when enough other agents use the p-strategy. Consequently, they expect the UMDL is likely to evolve to a point where some agents use simpler strategies and same use the p-strategy. Because of that, although the market efficiency (measured by total profit) decreases with an increasing number of p-strategy agents, the UMDL will not suffer inefficiency in the worst case