Most of the economic reports forecast that almost half of the worldwide market value unlocked by AI over the next decade (up to 6 trillion USD per year) will be in marketing&sales. In particular, AI will enable the optimization of more and more intricate economic settings, in which multiple different activities need to be jointly automated. This is the case of, e.g., Google Hotel Ads and Tripadvisor, where auctions are used to display ads of similar products or services together with their prices. As in classical ad auctions, the ads are ranked depending on the advertisers' bids, whereas, differently from classical settings, ads are displayed together with their prices, so as to provide a direct comparison among them. This dramatically affects users' behavior, as well as the properties of ad auctions. We show that, in such settings, social welfare maximization can be achieved by means of a direct-revelation mechanism that jointly optimizes, in polynomial time, the ads allocation and the advertisers' prices to be displayed with them. However, in practice it is unlikely that advertisers allow the mechanism to choose prices on their behalf. Indeed, in commonly-adopted mechanisms, ads allocation and price optimization are decoupled, so that the advertisers optimize prices and bids, while the mechanism does so for the allocation, once prices and bids are given. We investigate how this decoupling affects the efficiency of mechanisms. In particular, we study the Price of Anarchy (PoA) and the Price of Stability (PoS) of indirect-revelation mechanisms with both VCG and GSP payments, showing that the PoS for the revenue may be unbounded even with two slots, and the PoA for the social welfare may be as large as the number of slots. Nevertheless, we show that, under some assumptions, simple modifications to the indirect-revelation mechanism with VCG payments achieve a PoS of 1 for the revenue.
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