3 research outputs found
Exploration via design and the cost of uncertainty in keyword auctions
We present a deterministic exploration mechanism for sponsored search
auctions, which enables the auctioneer to learn the relevance scores of
advertisers, and allows advertisers to estimate the true value of clicks
generated at the auction site. This exploratory mechanism deviates only
minimally from the mechanism being currently used by Google and Yahoo! in the
sense that it retains the same pricing rule, similar ranking scheme, as well
as, similar mathematical structure of payoffs. In particular, the estimations
of the relevance scores and true-values are achieved by providing a chance to
lower ranked advertisers to obtain better slots. This allows the search engine
to potentially test a new pool of advertisers, and correspondingly, enables new
advertisers to estimate the value of clicks/leads generated via the auction.
Both these quantities are unknown a priori, and their knowledge is necessary
for the auction to operate efficiently. We show that such an exploration policy
can be incorporated without any significant loss in revenue for the auctioneer.
We compare the revenue of the new mechanism to that of the standard mechanism
at their corresponding symmetric Nash equilibria and compute the cost of
uncertainty, which is defined as the relative loss in expected revenue per
impression. We also bound the loss in efficiency, as well as, in user
experience due to exploration, under the same solution concept (i.e. SNE). Thus
the proposed exploration mechanism learns the relevance scores while
incorporating the incentive constraints from the advertisers who are selfish
and are trying to maximize their own profits, and therefore, the exploration is
essentially achieved via mechanism design. We also discuss variations of the
new mechanism such as truthful implementations.Comment: 19 pages, presentation improved, references added, title change
Capacity as a Fundamental Metric for Mechanism Design in the Information Economy
The auction theory literature has so far focused mostly on the design of
mechanisms that takes the revenue or the efficiency as a yardstick. However,
scenarios where the {\it capacity}, which we define as \textit{``the number of
bidders the auctioneer wants to have a positive probability of getting the
item''}, is a fundamental concern are ubiquitous in the information economy.
For instance, in sponsored search auctions (SSA's) or in online ad-exchanges,
the true value of an ad-slot for an advertiser is inherently derived from the
conversion-rate, which in turn depends on whether the advertiser actually
obtained the ad-slot or not; thus, unless the capacity of the underlying
auction is large, key parameters, such as true valuations and
advertiser-specific conversion rates, will remain unknown or uncertain leading
to inherent inefficiencies in the system. In general, the same holds true for
all information goods/digital goods. We initiate a study of mechanisms, which
take capacity as a yardstick, in addition to revenue/efficiency. We show that
in the case of a single indivisible item one simple way to incorporate capacity
constraints is via designing mechanisms to sell probability distributions, and
that under certain conditions, such optimal probability distributions could be
identified using a Linear programming approach. We define a quantity called
{\it price of capacity} to capture the tradeoff between capacity and
revenue/efficiency. We also study the case of sponsored search auctions.
Finally, we discuss how general such an approach via probability spikes can be
made, and potential directions for future investigations.Comment: 12 page
To Broad-Match or Not to Broad-Match : An Auctioneer's Dilemma ?
We initiate the study of an interesting aspect of sponsored search
advertising, namely the consequences of broad match-a feature where an ad of an
advertiser can be mapped to a broader range of relevant queries, and not
necessarily to the particular keyword(s) that ad is associated with. Starting
with a very natural setting for strategies available to the advertisers, and
via a careful look through the algorithmic lens, we first propose solution
concepts for the game originating from the strategic behavior of advertisers as
they try to optimize their budget allocation across various keywords. Next, we
consider two broad match scenarios based on factors such as information
asymmetry between advertisers and the auctioneer, and the extent of
auctioneer's control on the budget splitting. In the first scenario, the
advertisers have the full information about broad match and relevant
parameters, and can reapportion their own budgets to utilize the extra
information; in particular, the auctioneer has no direct control over budget
splitting. We show that, the same broad match may lead to different equilibria,
one leading to a revenue improvement, whereas another to a revenue loss. This
leaves the auctioneer in a dilemma - whether to broad-match or not. This
motivates us to consider another broad match scenario, where the advertisers
have information only about the current scenario, and the allocation of the
budgets unspent in the current scenario is in the control of the auctioneer. We
observe that the auctioneer can always improve his revenue by judiciously using
broad match. Thus, information seems to be a double-edged sword for the
auctioneer.Comment: 33 pages, 10 figures, new results added, substantially revise