2,966 research outputs found
Pricing Ad Slots with Consecutive Multi-unit Demand
We consider the optimal pricing problem for a model of the rich media
advertisement market, as well as other related applications. In this market,
there are multiple buyers (advertisers), and items (slots) that are arranged in
a line such as a banner on a website. Each buyer desires a particular number of
{\em consecutive} slots and has a per-unit-quality value (dependent on
the ad only) while each slot has a quality (dependent on the position
only such as click-through rate in position auctions). Hence, the valuation of
the buyer for item is . We want to decide the allocations and
the prices in order to maximize the total revenue of the market maker.
A key difference from the traditional position auction is the advertiser's
requirement of a fixed number of consecutive slots. Consecutive slots may be
needed for a large size rich media ad. We study three major pricing mechanisms,
the Bayesian pricing model, the maximum revenue market equilibrium model and an
envy-free solution model. Under the Bayesian model, we design a polynomial time
computable truthful mechanism which is optimum in revenue. For the market
equilibrium paradigm, we find a polynomial time algorithm to obtain the maximum
revenue market equilibrium solution. In envy-free settings, an optimal solution
is presented when the buyers have the same demand for the number of consecutive
slots. We conduct a simulation that compares the revenues from the above
schemes and gives convincing results.Comment: 27page
Stability of Service under Time-of-Use Pricing
We consider "time-of-use" pricing as a technique for matching supply and
demand of temporal resources with the goal of maximizing social welfare.
Relevant examples include energy, computing resources on a cloud computing
platform, and charging stations for electric vehicles, among many others. A
client/job in this setting has a window of time during which he needs service,
and a particular value for obtaining it. We assume a stochastic model for
demand, where each job materializes with some probability via an independent
Bernoulli trial. Given a per-time-unit pricing of resources, any realized job
will first try to get served by the cheapest available resource in its window
and, failing that, will try to find service at the next cheapest available
resource, and so on. Thus, the natural stochastic fluctuations in demand have
the potential to lead to cascading overload events. Our main result shows that
setting prices so as to optimally handle the {\em expected} demand works well:
with high probability, when the actual demand is instantiated, the system is
stable and the expected value of the jobs served is very close to that of the
optimal offline algorithm.Comment: To appear in STOC'1
On Revenue Maximization with Sharp Multi-Unit Demands
We consider markets consisting of a set of indivisible items, and buyers that
have {\em sharp} multi-unit demand. This means that each buyer wants a
specific number of items; a bundle of size less than has no value,
while a bundle of size greater than is worth no more than the most valued
items (valuations being additive). We consider the objective of setting
prices and allocations in order to maximize the total revenue of the market
maker. The pricing problem with sharp multi-unit demand buyers has a number of
properties that the unit-demand model does not possess, and is an important
question in algorithmic pricing. We consider the problem of computing a revenue
maximizing solution for two solution concepts: competitive equilibrium and
envy-free pricing.
For unrestricted valuations, these problems are NP-complete; we focus on a
realistic special case of "correlated values" where each buyer has a
valuation v_i\qual_j for item , where and \qual_j are positive
quantities associated with buyer and item respectively. We present a
polynomial time algorithm to solve the revenue-maximizing competitive
equilibrium problem. For envy-free pricing, if the demand of each buyer is
bounded by a constant, a revenue maximizing solution can be found efficiently;
the general demand case is shown to be NP-hard.Comment: page2
Coalition Formation and Combinatorial Auctions; Applications to Self-organization and Self-management in Utility Computing
In this paper we propose a two-stage protocol for resource management in a
hierarchically organized cloud. The first stage exploits spatial locality for
the formation of coalitions of supply agents; the second stage, a combinatorial
auction, is based on a modified proxy-based clock algorithm and has two phases,
a clock phase and a proxy phase. The clock phase supports price discovery; in
the second phase a proxy conducts multiple rounds of a combinatorial auction
for the package of services requested by each client. The protocol strikes a
balance between low-cost services for cloud clients and a decent profit for the
service providers. We also report the results of an empirical investigation of
the combinatorial auction stage of the protocol.Comment: 14 page
On the theory of truthful and fair pricing for banner advertisements
We consider revenue maximization problem in banner advertisements under two fundamental concepts: Envy-freeness and truthfulness. Envy-freeness captures fairness requirement among buyers while truthfulness gives buyers the incentive to announce truthful private bids. A extension of envy-freeness named competitive equilibrium, which requires both envy-freeness and market clearance conditions, is also investigated. For truthfulness also called incentive compatible, we adapt Bayesian settings, where each buyer's private value is drawn independently from publicly known distributions. Therefore, the truthfulness we adopt is Bayesian incentive compatible mechanisms. Most of our results are positive. We study various settings of revenue maximizing problem e.g. competitive equilibrium and envy-free solution in relaxed demand, sharp demand and consecutive demand case; Bayesian incentive compatible mechanism in relaxed demand, sharp demand, budget constraints and consecutive demand cases. Our approach allows us to argue that these simple mechanisms give optimal or approximate-optimal revenue guarantee in a very robust manner
Overview of Infrastructure Charging, part 4, IMPROVERAIL Project Deliverable 9, âImproved Data Background to Support Current and Future Infrastructure Charging Systemsâ
Improverail aims are to further support the establishment of railway infrastructure management in accordance with Directive 91/440, as well as the new railway infrastructure directives, by developing the necessary tools for modelling the management of railway infrastructure; by evaluating improved methods for capacity and resources management, which allow the improvement of the Life Cycle Costs (LCC) calculating methods, including elements related to vehicle - infrastructure interaction and external costs; and by improving data background in support of charging for use of railway infrastructure. To achieve these objectives, Improverail is organised along 8 workpackages, with specific objectives, responding to the requirements of the task 2.2.1/10 of the 2nd call made in the 5th RTD Framework Programme in December 1999.This part is the task 7.1 (Review of infrastructure charging systems) to the workpackage 7 (Analysis of the relation between infrastructure cost variation and diversity of infrastructure charging systems).Before explaining the economic characteristics of railway and his basic pricing principles, authors must specify the objectives of railways infrastructure charging.principle of pricing ; rail infrastructure charging ; public service obligation ; rail charging practice ; Europe ; Improverail
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