2,885 research outputs found
Using Online Auctions to Choose Optimal Product Configurations
In the current environment, product design involves choosing from a vast array of components and subcomponents. By developing modular platforms and communicating with multiple suppliers, each product becomes a bundle of features. The multitude of potential product configurations poses a challenge in identifying optimal configurations to offer customers. For most companies, the greatest challenge is measuring consumersâ marginal value for enhanced features. If this data were available, companies would offer those configurations with the highest margin. To date, consumersâ value for product features was estimated using decision analysis techniques, surveys, or conjoint analysis. This research proposes a different methodology for measuring consumersâ value. The emergence of active auction markets for a wide variety of products and services provides a venue for calibrating customer preferences. By offering different bundles of features, a company can measure the marginal increase in auction price obtained from enhanced features. Coupled with cost data, this information facilitates the evaluation of gross profit margin from offering different configurations. The methodology is assessed on used laptop computers sold via auction. Analyzing auction results indicates that consumers do value better features, and the incremental value of enhancements varies across features. Estimating cost differences from online posted prices in non-auction situations provide a foundation for estimating the efficient frontier of optimal bundles of features in a value-cost space. Data envelopment analysis is used in this context to define the efficient frontier. As online auction markets expand and evolve, this methodology could be implemented for many new products and services, which are offered as a bundle of features. Examples include many consumer electronics, travel packages, and communication services
Optimizing Your Online-Advertisement Asynchronously
We consider the problem of designing optimal online-ad investment strategies
for a single advertiser, who invests at multiple sponsored search sites
simultaneously, with the objective of maximizing his average revenue subject to
the advertising budget constraint. A greedy online investment scheme is
developed to achieve an average revenue that can be pushed to within
of the optimal, for any , with a tradeoff that the
temporal budget violation is . Different from many existing
algorithms, our scheme allows the advertiser to \emph{asynchronously} update
his investments on each search engine site, hence applies to systems where the
timescales of action update intervals are heterogeneous for different sites. We
also quantify the impact of inaccurate estimation of the system dynamics and
show that the algorithm is robust against imperfect system knowledge
Evolution of optimal L\'evy-flight strategies in human mental searches
Recent analysis of empirical data [F. Radicchi, A. Baronchelli & L.A.N.
Amaral. PloS ONE 7, e029910 (2012)] showed that humans adopt L\'evy flight
strategies when exploring the bid space in on-line auctions. A game theoretical
model proved that the observed L\'evy exponents are nearly optimal, being close
to the exponent value that guarantees the maximal economical return to players.
Here, we rationalize these findings by adopting an evolutionary perspective. We
show that a simple evolutionary process is able to account for the empirical
measurements with the only assumption that the reproductive fitness of a player
is proportional to her search ability. Contrarily to previous modeling, our
approach describes the emergence of the observed exponent without resorting to
any strong assumptions on the initial searching strategies. Our results
generalize earlier research, and open novel questions in cognitive, behavioral
and evolutionary sciences.Comment: 8 pages, 4 figure
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Opaque Selling
We study âopaqueâ selling in multiproduct environments â a marketing practice in which sellers strategically withhold product information by keeping important characteristics of their products hidden until after purchase. We show that a monopolist will always use opaque selling, but it is not first-best optimal to do so. However, opaque selling might be used at the constrained optimum (with the monopolistâs pricing behavior taken as given). For linear disutility costs, it is optimal for a monopolist to offer a single opaque product
Fast Iterative Combinatorial Auctions via Bayesian Learning
Iterative combinatorial auctions (CAs) are often used in multi-billion dollar
domains like spectrum auctions, and speed of convergence is one of the crucial
factors behind the choice of a specific design for practical applications. To
achieve fast convergence, current CAs require careful tuning of the price
update rule to balance convergence speed and allocative efficiency. Brero and
Lahaie (2018) recently introduced a Bayesian iterative auction design for
settings with single-minded bidders. The Bayesian approach allowed them to
incorporate prior knowledge into the price update algorithm, reducing the
number of rounds to convergence with minimal parameter tuning. In this paper,
we generalize their work to settings with no restrictions on bidder valuations.
We introduce a new Bayesian CA design for this general setting which uses Monte
Carlo Expectation Maximization to update prices at each round of the auction.
We evaluate our approach via simulations on CATS instances. Our results show
that our Bayesian CA outperforms even a highly optimized benchmark in terms of
clearing percentage and convergence speed.Comment: 9 pages, 2 figures, AAAI-1
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
Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model
Motivated by applications such as viral marketing, the problem of influence
maximization (IM) has been extensively studied in the literature. The goal is
to select a small number of users to adopt an item such that it results in a
large cascade of adoptions by others. Existing works have three key
limitations. (1) They do not account for economic considerations of a user in
buying/adopting items. (2) Most studies on multiple items focus on competition,
with complementary items receiving limited attention. (3) For the network
owner, maximizing social welfare is important to ensure customer loyalty, which
is not addressed in prior work in the IM literature. In this paper, we address
all three limitations and propose a novel model called UIC that combines
utility-driven item adoption with influence propagation over networks. Focusing
on the mutually complementary setting, we formulate the problem of social
welfare maximization in this novel setting. We show that while the objective
function is neither submodular nor supermodular, surprisingly a simple greedy
allocation algorithm achieves a factor of of the optimum
expected social welfare. We develop \textsf{bundleGRD}, a scalable version of
this approximation algorithm, and demonstrate, with comprehensive experiments
on real and synthetic datasets, that it significantly outperforms all
baselines.Comment: 33 page
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