1,041 research outputs found
A Probabilistic Approach to Automated Bidding in Alternative Auctions
This paper presents an approach to develop bidding agents that participate in multiple alternative auctions, with the goal of obtaining an item at the lowest price. The approach consists of a prediction method and a planning algorithm. The prediction method exploits the history of past auctions in order to build probability functions capturing the belief that a bid of a given price may win a given auction. The planning algorithm computes the lowest price, such that by sequentially bidding in a subset of the relevant auctions, the agent can obtain the item at that price with an acceptable probability. The approach addresses the case where the auctions are for substitutable items with different values. Experimental results are reported, showing that the approach increases the payoff of their users and the welfare of the market
Proving soundness of combinatorial Vickrey auctions and generating verified executable code
Using mechanised reasoning we prove that combinatorial Vickrey auctions are
soundly specified in that they associate a unique outcome (allocation and
transfers) to any valid input (bids). Having done so, we auto-generate verified
executable code from the formally defined auction. This removes a source of
error in implementing the auction design. We intend to use formal methods to
verify new auction designs. Here, our contribution is to introduce and
demonstrate the use of formal methods for auction verification in the familiar
setting of a well-known auction
Computer-aided verification in mechanism design
In mechanism design, the gold standard solution concepts are dominant
strategy incentive compatibility and Bayesian incentive compatibility. These
solution concepts relieve the (possibly unsophisticated) bidders from the need
to engage in complicated strategizing. While incentive properties are simple to
state, their proofs are specific to the mechanism and can be quite complex.
This raises two concerns. From a practical perspective, checking a complex
proof can be a tedious process, often requiring experts knowledgeable in
mechanism design. Furthermore, from a modeling perspective, if unsophisticated
agents are unconvinced of incentive properties, they may strategize in
unpredictable ways.
To address both concerns, we explore techniques from computer-aided
verification to construct formal proofs of incentive properties. Because formal
proofs can be automatically checked, agents do not need to manually check the
properties, or even understand the proof. To demonstrate, we present the
verification of a sophisticated mechanism: the generic reduction from Bayesian
incentive compatible mechanism design to algorithm design given by Hartline,
Kleinberg, and Malekian. This mechanism presents new challenges for formal
verification, including essential use of randomness from both the execution of
the mechanism and from the prior type distributions. As an immediate
consequence, our work also formalizes Bayesian incentive compatibility for the
entire family of mechanisms derived via this reduction. Finally, as an
intermediate step in our formalization, we provide the first formal
verification of incentive compatibility for the celebrated
Vickrey-Clarke-Groves mechanism
The BARISTA: A model for bid arrivals in online auctions
The arrival process of bidders and bids in online auctions is important for
studying and modeling supply and demand in the online marketplace. A popular
assumption in the online auction literature is that a Poisson bidder arrival
process is a reasonable approximation. This approximation underlies theoretical
derivations, statistical models and simulations used in field studies. However,
when it comes to the bid arrivals, empirical research has shown that the
process is far from Poisson, with early bidding and last-moment bids taking
place. An additional feature that has been reported by various authors is an
apparent self-similarity in the bid arrival process. Despite the wide evidence
for the changing bidding intensities and the self-similarity, there has been no
rigorous attempt at developing a model that adequately approximates bid
arrivals and accounts for these features. The goal of this paper is to
introduce a family of distributions that well-approximate the bid time
distribution in hard-close auctions. We call this the BARISTA process (Bid
ARrivals In STAges) because of its ability to generate different intensities at
different stages. We describe the properties of this model, show how to
simulate bid arrivals from it, and how to use it for estimation and inference.
We illustrate its power and usefulness by fitting simulated and real data from
eBay.com. Finally, we show how a Poisson bidder arrival process relates to a
BARISTA bid arrival process.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS117 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Optimal pricing using online auction experiments: A P\'olya tree approach
We show how a retailer can estimate the optimal price of a new product using
observed transaction prices from online second-price auction experiments. For
this purpose we propose a Bayesian P\'olya tree approach which, given the
limited nature of the data, requires a specially tailored implementation.
Avoiding the need for a priori parametric assumptions, the P\'olya tree
approach allows for flexible inference of the valuation distribution, leading
to more robust estimation of optimal price than competing parametric
approaches. In collaboration with an online jewelry retailer, we illustrate how
our methodology can be combined with managerial prior knowledge to estimate the
profit maximizing price of a new jewelry product.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS503 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Bidding for B2B or B2G tenders: toward the adoption of pricing models in practice
Data availability The authors confirm that all data generated or analyzed are included in this published
article. The data source for science mapping was Elsevier’s Scopus database. The search term for extracting
the publication data is presented in Sect. 3 in Scopus syntax for replicability by other researchers. The
same section also shows the parameterization of the SciMAT tool for transparency.This study investigates the lack of adoption of pricing models for tenders in business-
to-business (B2B) and business-to-government (B2G) markets. We aim to
identify the gaps between research and practice and propose a future research agenda
to bridge these gaps. Our study contributes in three ways: First, we outline how our
research agenda can influence the adoption of pricing models across specific practitioner
roles in tendering. Second, we introduce systematic science mapping (SSM)
as a novel methodology for literature reviews. SSM combines a systematic review
and science mapping in a multi-stage, mixed-methods research design. We chart the
evolution of 1042 research publications from 1956 to 2022 into three thematic areas.
Our review of 163 gray literature publications reveals seven schools of thought on
tender price modeling and the causes of theory-to-practice gaps. Finally, we introduce
a new metric, the mapping factor (MAPF), as a robustness indicator for systematic
literature reviews
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