51,568 research outputs found
Conjugate information disclosure in an auction with learning
We consider a single-item, independent private value auction environment with two bidders: a leader, who knows his valuation, and a follower, who privately chooses how much to learn about his valuation. We show that, under some conditions, an ex-post efficient revenue-maximizing auction—which solicits bids sequentially—partially discloses the leader's bid to the follower, to influence his learning. The disclosure rule that emerges is novel; it may reveal to the follower only a pair of bids to which the leader's actual bid belongs. The identified disclosure rule, relative to the first-best, induces the follower to learn less when the leader's valuation is low and more when the leader's valuation is high
Learning Valuation Distributions from Partial Observation
Auction theory traditionally assumes that bidders' valuation distributions
are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson
auction. However, this theory does not describe how the auctioneer comes to
possess this information. Recently, Cole and Roughgarden [2014] showed that an
approximation based on a finite sample of independent draws from each bidder's
distribution is sufficient to produce a near-optimal auction. In this work, we
consider the problem of learning bidders' valuation distributions from much
weaker forms of observations. Specifically, we consider a setting where there
is a repeated, sealed-bid auction with bidders, but all we observe for each
round is who won, but not how much they bid or paid. We can also participate
(i.e., submit a bid) ourselves, and observe when we win. From this information,
our goal is to (approximately) recover the inherently recoverable part of the
underlying bid distributions. We also consider extensions where different
subsets of bidders participate in each round, and where bidders' valuations
have a common-value component added to their independent private values
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search
Sponsored search is an important monetization channel for search engines, in
which an auction mechanism is used to select the ads shown to users and
determine the prices charged from advertisers. There have been several pieces
of work in the literature that investigate how to design an auction mechanism
in order to optimize the revenue of the search engine. However, due to some
unrealistic assumptions used, the practical values of these studies are not
very clear. In this paper, we propose a novel \emph{game-theoretic machine
learning} approach, which naturally combines machine learning and game theory,
and learns the auction mechanism using a bilevel optimization framework. In
particular, we first learn a Markov model from historical data to describe how
advertisers change their bids in response to an auction mechanism, and then for
any given auction mechanism, we use the learnt model to predict its
corresponding future bid sequences. Next we learn the auction mechanism through
empirical revenue maximization on the predicted bid sequences. We show that the
empirical revenue will converge when the prediction period approaches infinity,
and a Genetic Programming algorithm can effectively optimize this empirical
revenue. Our experiments indicate that the proposed approach is able to produce
a much more effective auction mechanism than several baselines.Comment: Twenty-third International Conference on Artificial Intelligence
(IJCAI 2013
Real-Time Bidding by Reinforcement Learning in Display Advertising
The majority of online display ads are served through real-time bidding (RTB)
--- each ad display impression is auctioned off in real-time when it is just
being generated from a user visit. To place an ad automatically and optimally,
it is critical for advertisers to devise a learning algorithm to cleverly bid
an ad impression in real-time. Most previous works consider the bid decision as
a static optimization problem of either treating the value of each impression
independently or setting a bid price to each segment of ad volume. However, the
bidding for a given ad campaign would repeatedly happen during its life span
before the budget runs out. As such, each bid is strategically correlated by
the constrained budget and the overall effectiveness of the campaign (e.g., the
rewards from generated clicks), which is only observed after the campaign has
completed. Thus, it is of great interest to devise an optimal bidding strategy
sequentially so that the campaign budget can be dynamically allocated across
all the available impressions on the basis of both the immediate and future
rewards. In this paper, we formulate the bid decision process as a
reinforcement learning problem, where the state space is represented by the
auction information and the campaign's real-time parameters, while an action is
the bid price to set. By modeling the state transition via auction competition,
we build a Markov Decision Process framework for learning the optimal bidding
policy to optimize the advertising performance in the dynamic real-time bidding
environment. Furthermore, the scalability problem from the large real-world
auction volume and campaign budget is well handled by state value approximation
using neural networks.Comment: WSDM 201
An Experimental Study of Information Revelation Policies in Sequential Auctions
Theoretical models of information asymmetry have identied a tradeoff between the desire to learn and the desire to prevent an opponent from learning private information. This paper reports a laboratory experiment that investigates if actual bidders account for this tradeoff, using a sequential procurement auction with private cost information and varying information revelation policies. Specically, the Complete Information Policy, where all submitted bids are revealed between auctions, is compared against the Incomplete Information Policy, where only the winning bid is revealed. The experimental results are largely consistent with the theoretical predictions. For example, bidders pool with other types to prevent an opponent from learning signicantly more often under a Complete Information Policy. Also as predicted, the procurer pays less when employing an Incomplete Information Policy only when the market is highly competitive. Bids are usually more aggressive than the risk neutral quantitative prediction, which is usually consistent with risk aversion.Complete and Incomplete Information Revelation Policies, Laboratory Study, Procurement Auction, Multistage Game
Multi-Unit Auctions to Allocate Water Scarcity Simulating Bidding Behaviour with Agent Based Models
Multi-unit auctions are promising mechanisms for the reallocation of water. The main advantage of such auctions is to avoid the lumpy bid issue. However, there is great uncertainty about the best auction formats when multi-unit auctions are used. The theory can only supply the structural properties of equilibrium strategies and the multiplicity of equilibria makes comparisons across auction formats difficult. Empirical studies and experiments have improved our knowledge of multi- unit auctions but they remain scarce and most experiments are restricted to two bidders and two units. Moreover, they demonstrate that bidders have limited rationality and learn through experience. This paper constructs an agent-based model of bidders to compare the performance of alternative auction formats under circumstances where bidders submit continuous bid supply functions and learn over time to adjust their bids to improve their net incomes. We demonstrate that under the generalized Vickrey, simulated bids converge towards truthful bids as predicted by the theory and that bid shading is the rule for the uniform and discriminatory auctions. Our study allows us to assess the potential gains from agent-based modelling approaches in the assessment of the dynamic performance of multi-unit procurement auctions. Some recommendations on the desirable format of water auctions are provided.Multi-unit auctions, Learning, Multi-agent models, Water allocation
Optimal No-regret Learning in Repeated First-price Auctions
We study online learning in repeated first-price auctions with censored
feedback, where a bidder, only observing the winning bid at the end of each
auction, learns to adaptively bid in order to maximize her cumulative payoff.
To achieve this goal, the bidder faces a challenging dilemma: if she wins the
bid--the only way to achieve positive payoffs--then she is not able to observe
the highest bid of the other bidders, which we assume is iid drawn from an
unknown distribution. This dilemma, despite being reminiscent of the
exploration-exploitation trade-off in contextual bandits, cannot directly be
addressed by the existing UCB or Thompson sampling algorithms in that
literature, mainly because contrary to the standard bandits setting, when a
positive reward is obtained here, nothing about the environment can be learned.
In this paper, by exploiting the structural properties of first-price
auctions, we develop the first learning algorithm that achieves
regret bound when the bidder's private values are
stochastically generated. We do so by providing an algorithm on a general class
of problems, which we call monotone group contextual bandits, where the same
regret bound is established under stochastically generated contexts. Further,
by a novel lower bound argument, we characterize an lower
bound for the case where the contexts are adversarially generated, thus
highlighting the impact of the contexts generation mechanism on the fundamental
learning limit. Despite this, we further exploit the structure of first-price
auctions and develop a learning algorithm that operates sample-efficiently (and
computationally efficiently) in the presence of adversarially generated private
values. We establish an regret bound for this algorithm,
hence providing a complete characterization of optimal learning guarantees for
this problem
Tiger Daily: June 15, 2016
ANNOUNCEMENTS • Tiger Fitness Classes – Summer 2016 • FHSU Surplus Property – Sealed Bid Auction • Congratulations to Seth Kastle for Selection as 2016 Tillman Scholar • 2016-2017 Promotion and Tenure Timelines • 2016 Back to School Picnic Registration is Open • Free Folders and Hanging Files EVENTS THIS WEEK • Being Intentional About Faculty Engagement When Creating Credit for Prior Learning (CPL) Programs and Administering – TODAY 1:00 pm FUTURE EVENTS • Intell July 20, 8:30 a
Truthful Learning Mechanisms for Multi-Slot Sponsored Search Auctions with Externalities
Sponsored search auctions constitute one of the most successful applications
of microeconomic mechanisms. In mechanism design, auctions are usually designed
to incentivize advertisers to bid their truthful valuations and to assure both
the advertisers and the auctioneer a non-negative utility. Nonetheless, in
sponsored search auctions, the click-through-rates (CTRs) of the advertisers
are often unknown to the auctioneer and thus standard truthful mechanisms
cannot be directly applied and must be paired with an effective learning
algorithm for the estimation of the CTRs. This introduces the critical problem
of designing a learning mechanism able to estimate the CTRs at the same time as
implementing a truthful mechanism with a revenue loss as small as possible
compared to an optimal mechanism designed with the true CTRs. Previous work
showed that, when dominant-strategy truthfulness is adopted, in single-slot
auctions the problem can be solved using suitable exploration-exploitation
mechanisms able to achieve a per-step regret (over the auctioneer's revenue) of
order (where T is the number of times the auction is repeated).
It is also known that, when truthfulness in expectation is adopted, a per-step
regret (over the social welfare) of order can be obtained. In
this paper we extend the results known in the literature to the case of
multi-slot auctions. In this case, a model of the user is needed to
characterize how the advertisers' valuations change over the slots. We adopt
the cascade model that is the most famous model in the literature for sponsored
search auctions. We prove a number of novel upper bounds and lower bounds both
on the auctioneer's revenue loss and social welfare w.r.t. to the VCG auction
and we report numerical simulations investigating the accuracy of the bounds in
predicting the dependency of the regret on the auction parameters
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