19 research outputs found
Dominant bidding strategy in Mobile App advertising auction
The widespread use of intelligent mobile phone has promoted prosperity of mobile App advertising in recent years. Based on existing bidding status, this paper presents the dominant bidding strategy for mobile advertising auction. Firstly, our study characterizes multiple Nash Equilibria resulting from different bidding strategies in wGSP (weighted Generalized Second-Price) auction. Further more, we prove that advertiser’s rank and utility will not decrease by using the dominant bidding strategy. We also consider the situation where the reserve price is set by the mobile advertising platform. It turns out that that advertiser’s payment will be no less than reserve price. Finally, a practical implementation for a virtual market simulates the dynamic bidding process in real world environments.published_or_final_versio
Agent Behavior Prediction and Its Generalization Analysis
Machine learning algorithms have been applied to predict agent behaviors in
real-world dynamic systems, such as advertiser behaviors in sponsored search
and worker behaviors in crowdsourcing. The behavior data in these systems are
generated by live agents: once the systems change due to the adoption of the
prediction models learnt from the behavior data, agents will observe and
respond to these changes by changing their own behaviors accordingly. As a
result, the behavior data will evolve and will not be identically and
independently distributed, posing great challenges to the theoretical analysis
on the machine learning algorithms for behavior prediction. To tackle this
challenge, in this paper, we propose to use Markov Chain in Random Environments
(MCRE) to describe the behavior data, and perform generalization analysis of
the machine learning algorithms on its basis. Since the one-step transition
probability matrix of MCRE depends on both previous states and the random
environment, conventional techniques for generalization analysis cannot be
directly applied. To address this issue, we propose a novel technique that
transforms the original MCRE into a higher-dimensional time-homogeneous Markov
chain. The new Markov chain involves more variables but is more regular, and
thus easier to deal with. We prove the convergence of the new Markov chain when
time approaches infinity. Then we prove a generalization bound for the machine
learning algorithms on the behavior data generated by the new Markov chain,
which depends on both the Markovian parameters and the covering number of the
function class compounded by the loss function for behavior prediction and the
behavior prediction model. To the best of our knowledge, this is the first work
that performs the generalization analysis on data generated by complex
processes in real-world dynamic systems
Discrete Strategies in Keyword Auctions and Their Inefficiency for Locally Aware Bidders
We study formally discrete bidding strategies for the game induced by the Generalized Second Price keyword auction mechanism. Such strategies have seen experimental evaluation in the recent literature as parts of iterative best response procedures, which have been shown not to converge. We give a detailed definition of iterative best response under these strategies and, under appropriate discretization of the players' strategy spaces we find that the discretized configurations space {\em contains} socially optimal pure Nash equilibria. We cast the strategies under a new light, by studying their
performance for bidders that act based on local information; we prove bounds for the worst-case ratio of the social welfare of locally stable configurations, relative to the socially optimum welfare
Strategyproof auctions for balancing social welfare and fairness in secondary spectrum markets
Secondary spectrum access is emerging as a promising approach for mitigating the spectrum scarcity in wireless networks. Coordinated spectrum access for secondary users can be achieved using periodic spectrum auctions. Recent studies on such auction design mostly neglect the repeating nature of such auctions, and focus on greedily maximizing social welfare. Such auctions can cause subsets of users to experience starvation in the long run, reducing their incentive to continue participating in the auction. It is desirable to increase the diversity of users allocated spectrum in each auction round, so that a trade-off between social welfare and fairness is maintained. We study truthful mechanisms towards this objective, for both local and global fairness criteria. For local fairness, we introduce randomization into the auction design, such that each user is guaranteed a minimum probability of being assigned spectrum. Computing an optimal, interference-free spectrum allocation is NP-Hard; we present an approximate solution, and tailor a payment scheme to guarantee truthful bidding is a dominant strategy for all secondary users. For global fairness, we adopt the classic maxmin fairness criterion. We tailor another auction by applying linear programming techniques for striking the balance between social welfare and max-min fairness, and for finding feasible channel allocations. In particular, a pair of primal and dual linear programs are utilized to guide the probabilistic selection of feasible allocations towards a desired tradeoff in expectation. © 2011 IEEE.published_or_final_versionThe IEEE INFOCOM 2011, Shanghai, China, 10-15 April 2011. In Conference Proceedings, 2011, p. 3020-302
Nonbossy Mechanisms: Mechanism Design Robust to Secondary Goals
We study mechanism design when agents may have hidden secondary goals which
will manifest as non-trivial preferences among outcomes for which their primary
utility is the same. We show that in such cases, a mechanism is robust against
strategic manipulation if and only if it is not only incentive-compatible, but
also nonbossy -- a well-studied property in the context of matching and
allocation mechanisms. We give complete characterizations of
incentive-compatible and nonbossy mechanisms in various settings, including
auctions with single-parameter agents and public decision settings where all
agents share a common outcome. In particular, we show that in the single-item
setting, a mechanism is incentive-compatible, individually rational, and
nonbossy if and only if it is a sequential posted-price mechanism. In contrast,
we show that in more general single-parameter environments, there exist
mechanisms satisfying our characterization that significantly outperform
sequential posted-price mechanisms in terms of revenue or efficiency (sometimes
by an exponential factor)