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This research offers a joint consideration to two distinct problems in second-price auctions with common values: seller's optimal supply of information precision and bidders' incentive to acquire information. Normality assumption is adopted to allow for the utilization of different notions of stochastic ordering of signals simultaneously in a highly tractable learning environment. The main contribution is aimed to be enabling cooperative behavior among bidders through introduction of an information pool. Resulting environment can potentially lead to novel strategic considerations between the seller and the bidders who aim to capture larger shares of the total surplus.