1,043 research outputs found
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
Modeling On-Line Art Auction Dynamics Using Functional Data Analysis
In this paper, we examine the price dynamics of on-line art auctions of
modern Indian art using functional data analysis. The purpose here is not just
to understand what determines the final prices of art objects, but also the
price movement during the entire auction. We identify several factors, such as
artist characteristics (established or emerging artist; prior sales history),
art characteristics (size; painting medium--canvas or paper), competition
characteristics (current number of bidders; current number of bids) and auction
design characteristics (opening bid; position of the lot in the auction), that
explain the dynamics of price movement in an on-line art auction. We find that
the effects on price vary over the duration of the auction, with some of these
effects being stronger at the beginning of the auction (such as the opening bid
and historical prices realized). In some cases, the rate of change in prices
(velocity) increases at the end of the auction (for canvas paintings and
paintings by established artists). Our analysis suggests that the opening bid
is positively related to on-line auction price levels of art at the beginning
of the auction, but its effect declines toward the end of the auction. The
order in which the lots appear in an art auction is negatively related to the
current price level, with this relationship decreasing toward the end of the
auction. This implies that lots that appear earlier have higher current prices
during the early part of the auction, but that effect diminishes by the end of
the auction. Established artists show a positive relationship with the price
level at the beginning of the auction. Reputation or popularity of the artists
and their investment potential as assessed by previous history of sales are
positively related to the price levels at the beginning of the auction. The
medium (canvas or paper) of the painting does not show any relationship with
art auction price levels, but the size of the painting is negatively related to
the current price during the early part of the auction. Important implications
for auction design are drawn from the analysis.Comment: Published at http://dx.doi.org/10.1214/088342306000000196 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data
Widespread e-commerce activity on the Internet has led to new opportunities
to collect vast amounts of micro-level market and nonmarket data. In this paper
we share our experiences in collecting, validating, storing and analyzing large
Internet-based data sets in the area of online auctions, music file sharing and
online retailer pricing. We demonstrate how such data can advance knowledge by
facilitating sharper and more extensive tests of existing theories and by
offering observational underpinnings for the development of new theories. Just
as experimental economics pushed the frontiers of economic thought by enabling
the testing of numerous theories of economic behavior in the environment of a
controlled laboratory, we believe that observing, often over extended periods
of time, real-world agents participating in market and nonmarket activity on
the Internet can lead us to develop and test a variety of new theories.
Internet data gathering is not controlled experimentation. We cannot randomly
assign participants to treatments or determine event orderings. Internet data
gathering does offer potentially large data sets with repeated observation of
individual choices and action. In addition, the automated data collection holds
promise for greatly reduced cost per observation. Our methods rely on
technological advances in automated data collection agents. Significant
challenges remain in developing appropriate sampling techniques integrating
data from heterogeneous sources in a variety of formats, constructing
generalizable processes and understanding legal constraints. Despite these
challenges, the early evidence from those who have harvested and analyzed large
amounts of e-commerce data points toward a significant leap in our ability to
understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The Dynamics of Seller Reputation: Theory and Evidence from eBay
We propose a basic theoretical model of eBay's reputation mechanism, derive a series of implications and empirically test their validity. Our theoretical model features both adverse selection and moral hazard. We show that when a seller receives a negative rating for the first time his reputation decreases and so does his effort level. This implies a decline in sales and price; and an increase in the rate of arrival of subsequent negative feedback. Our model also suggests that sellers with worse records are more likely to exit (and possibly re-enter under a new identity), whereas better sellers have more to gain from buying a reputation' by building up a record of favorable feedback through purchases rather than sales. Our empirical evidence, based on a panel data set of seller feedback histories and cross-sectional data on transaction prices collected from eBay is broadly consistent with all of these predictions. An important conclusion of our results is that eBay's reputation system gives way to strategic responses from both buyers and sellers.
The Economics of Internet Markets
The internet has facilitated the creation of new markets characterized by large scale, increased customization, rapid innovation and the collection and use of detailed consumer and market data. I describe these changes and some of the economic theory that has been useful for thinking about online advertising markets, retail and business-to-business e-commerce, internet job matching and financial exchanges, and other internet platforms. I also discuss the empirical evidence on competition and consumer behavior in internet markets and some directions for future research.internet, market, innovation, advertising, retail, e-commerce, financial exchanges
Buy-It-Now or Snipe on eBay?
In this paper, we study bidder behavior in an eBay auction with a buy-it-now option. The digital environment that eBay provides gives bidders and sellers a variety of options when they participate. These include using sniping software to submit bids at the last minute and hard close times set a priori by the seller (versus Amazon.com’s soft close which adds 10 minutes to the end of the auction if there is last minute acitivity in an auction). Due to the richness of behaviors which can be observed by the bidders participating in eBay, we realize that there are many equilibria for the bidders in eBay. We propose an equilibrium and prove that it is one of the existing equilibria which survives any kind of deviation by the bidders. We analyze this equilibria for the bidders on eBay and validate our model using the data we collected from the Internet
Opportunity costs calculation in agent-based vehicle routing and scheduling
In this paper we consider a real-time, dynamic pickup and delivery problem with timewindows where orders should be assigned to one of a set of competing transportation companies. Our approach decomposes the problem into a multi-agent structure where vehicle agents are responsible for the routing and scheduling decisions and the assignment of orders to vehicles is done by using a second-price auction. Therefore the system performance will be heavily dependent on the pricing strategy of the vehicle agents. We propose a pricing strategy for vehicle agents based on dynamic programming where not only the direct cost of a job insertion is taken into account, but also its impact on future opportunities. We also propose a waiting strategy based on the same opportunity valuation. Simulation is used to evaluate the benefit of pricing opportunities compared to simple pricing strategies in different market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization and delivery reliability
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