The paper goals are to define the bidders valuation, determine the optimal seller's reserve value, and see the effect of reserve value adjustment (changing the value of current reserve value to the calculated reserve value) to the corresponding revenue. It is presumed that by adjusting the existing reserve value to an optimized value, a higher revenue could be gained.
The one that is interesting, the authors did not only observed the winning bids record, but also the losing bids (in an interval up to 1 second). Similar previous papers usually only consider the winning bids in their model. They believe that this extra observation could improve their prediction on the distribution of private values of the bidders. The other thing that the writers did is that they use a markov chain monte-carlo and gibbs sampling in the projection of the private values of the bidder.
In general, the flow of the research can be presented as the scheme above. First they observe the recorded data (winning and losing bids). Second they try to mimic the historical data to a valuation distribution of bidders by using the a markov chain monte-carlo and gibbs sampling (they do this step in 4 scenarios). Third then they calculate the optimal reserve bid and also the corresponding delta of revenue. By their finding the increment of the reserve price will not bring big change to the increase of the revenue.
However the conclusion of this paper is not final yet, the team planned to have another discussion about this paper in some other time to really grasp the essence of the paper.
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