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

Functional Data Analysis in Electronic Commerce Research

This paper describes opportunities and challenges of using functional data analysis (FDA) for the exploration and analysis of data originating from electronic commerce (eCommerce). We discuss the special data structures that arise in the online environment and why FDA is a natural approach for representing and analyzing such data. The paper reviews several FDA methods and motivates their usefulness in eCommerce research by providing a glimpse into new domain insights that they allow. We argue that the wedding of eCommerce with FDA leads to innovations both in statistical methodology, due to the challenges and complications that arise in eCommerce data, and in online research, by being able to ask (and subsequently answer) new research questions that classical statistical methods are not able to address, and also by expanding on research questions beyond the ones traditionally asked in the offline environment. We describe several applications originating from online transactions which are new to the statistics literature, and point out statistical challenges accompanied by some solutions. We also discuss some promising future directions for joint research efforts between researchers in eCommerce and statistics.Comment: Published at http://dx.doi.org/10.1214/088342306000000132 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

Smoothing sparse and unevenly sampled curves using semiparametric mixed models: An application to online auctions

Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data which can be difficult if the data is irregularly distributed. In this paper we present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. While the model can handle sparse and unevenly distributed data, it also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's online auctions. Online auctions produce monotonic increasing price curves that are often correlated across two auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also estimates the underlying increasing trend from the data without imposing model-constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an online auction, our approach also results in more accurate price predictions compared to standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Competition Between Auctions

Even though auctions are capturing an increasing share of commerce, they are typically treated in the theoretical economics literature as isolated. That is, an auction is typically treated as a single seller facing multiple buyers or as a single buyer facing multiple sellers. In this paper, we review the state of the art of competition between auctions. We consider three different types of competition: competition between auctions, competition between formats, and competition between auctioneers vying for auction traffic. We highlight the newest experimental, statistical and analytical methods in the analysis of competition between auctions.auctions, bidding, competition, auction formats, auction houses

Regional and Sub-Global Climate Blocs.A Game-Theoretic Perspective on Bottom-up Climate Regimes

No international regime on climate change is going to be fully effective in controlling GHG emissions without the involvement of countries such as China, India, the United States, Australia, and possibly other developing countries. This highlights an unambiguous weakness of the Kyoto Protocol, where the aforementioned countries either have no binding emission targets or have decided not to comply with their targets. Therefore, when discussing possible post-Kyoto scenarios, it is crucial to prioritise participation incentives for all countries, especially those without explicit or with insufficient abatement targets. This paper offers a bottom-up game-theoretic perspective on participation incentives. Rather than focusing on issue linkage, transfers or burden sharing as tools to enhance the incentives to participate in a climate agreement, this paper aims at exploring whether a different policy approach could lead more countries to adopt effective climate control policies. This policy approach is explicitly bottom-up, namely it gives each country the freedom to sign agreements and deals, bilaterally or multilaterally, with other countries, without being constrained by any global protocol or convention. This study provides a game-theoretic assessment of this policy approach and then evaluates empirically the possible endogenous emergence of single or multiple climate coalitions. Welfare and technological consequences of different multiple bloc climate regimes will be assessed and their overall environmental effectiveness will be discussed.Agreements, Climate, Incentives, Negotiations, Policy

Trust and Experience in Online Auctions

This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust