33,488 research outputs found
Optimising Trade-offs Among Stakeholders in Ad Auctions
We examine trade-offs among stakeholders in ad auctions. Our metrics are the
revenue for the utility of the auctioneer, the number of clicks for the utility
of the users and the welfare for the utility of the advertisers. We show how to
optimize linear combinations of the stakeholder utilities, showing that these
can be tackled through a GSP auction with a per-click reserve price. We then
examine constrained optimization of stakeholder utilities.
We use simulations and analysis of real-world sponsored search auction data
to demonstrate the feasible trade-offs, examining the effect of changing the
allowed number of ads on the utilities of the stakeholders. We investigate both
short term effects, when the players do not have the time to modify their
behavior, and long term equilibrium conditions.
Finally, we examine a combinatorially richer constrained optimization
problem, where there are several possible allowed configurations (templates) of
ad formats. This model captures richer ad formats, which allow using the
available screen real estate in various ways. We show that two natural
generalizations of the GSP auction rules to this domain are poorly behaved,
resulting in not having a symmetric Nash equilibrium or having one with poor
welfare. We also provide positive results for restricted cases.Comment: 18 pages, 10 figures, ACM Conference on Economics and Computation
201
Adaptive Ranking Based Constraint Handling for Explicitly Constrained Black-Box Optimization
A novel explicit constraint handling technique for the covariance matrix
adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint
handling exhibits two invariance properties. One is the invariance to arbitrary
element-wise increasing transformation of the objective and constraint
functions. The other is the invariance to arbitrary affine transformation of
the search space. The proposed technique virtually transforms a constrained
optimization problem into an unconstrained optimization problem by considering
an adaptive weighted sum of the ranking of the objective function values and
the ranking of the constraint violations that are measured by the Mahalanobis
distance between each candidate solution to its projection onto the boundary of
the constraints. Simulation results are presented and show that the CMA-ES with
the proposed constraint handling exhibits the affine invariance and performs
similarly to the CMA-ES on unconstrained counterparts.Comment: 9 page
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