1-level QPAs take advantage of price fluctuations by keeping models of the QPAs and UIAs and use these to make better predictions as to what they should bid. The 1-level models, while computationally expensive, allow QPAs to track the individual agents more closely, thereby identifying when a UIA is willing to pay more than the going rate. Previous research has shown that the advantages of 1-level models can be correlated to the price volatility (see ).
However, this strategic thinking is only successful against 0-level UIAs. When we tested the 1-level QPAs against the 1-level UIAs we found the QPA's performance on par with other similar 0-level QPAs. The 1-level QPA's advantage was eliminated only at the cost of making the UIAs keep 1-level models. From the systems' perspective, these computations are ``wasted'' effort in that they do not contribute in any way to the task of servicing the user. Notice, however, that they would only be needed when the 1-level QPAs participating in the auction are offering significantly different services. In these cases, the buyers will want to determine which one of the QPAs offers the ``best'' (in its own opinion) service. Once they have determined this then, perhaps, a new auction could be started that would service only these QPAs so that the UIAs could then go back to being simpler 0-level agents and avoid the heavy computational costs. The incentive/financing mechanisms for starting new auctions are still under study.