Designing Pricing Mechanisms for Autonomous Agents Based on Bid-Forecasting

Autonomous agents that participate in the electronic market environment introduce an advanced paradigm for realizing automated deliberations over offered prices of auctioned goods. These agents represent humans and their assets, therefore it is critical for them not only to act rationally but also efficiently. By enabling agents to deploy bidding strategies and to compete with each other in a marketplace, a valuable amount of historical data is produced. An optimal dynamic forecasting of the maximum offered bid would enable more gainful behaviours by agents. In this respect, this paper presents a methodology that takes advantage of price offers generated in eauctions, in order to provide an adequate short-term forecasting schema based on time-series analysis. The forecast is incorporated into the reasoning mechanism of a group of autonomous e-auction agents to improve their bidding behaviour. In order to test the improvement introduced by the proposed method, we set up a test-bed, on which a slightly variant version of the firstprice ascending auction is simulated with many buyers and one seller, trading with each other over one item. The results of the proposed methodology are discussed and many possible extensions and improvements are advocated to ensure wide acceptance of the bid-forecasting reasoning mechanism.