Using bid data for the management of sequential,multi-unit,online auctions with uniformly distributed bidder valuations

Internet auctions for consumers’ goods are an increasingly popular selling venue. We have observed that many sellers, instead of offering their entire inventory in a single auction, split it into sequential auctions of smaller lots, thereby reducing the negative market impact of larger lots. Information technology also makes it possible to collect and analyze detailed bid data from online auctions. In this paper, we develop and test a new model of sequential online auctions to explore the potential benefits of using real bid data from earlier auctions to improve the management of future auctions. Assuming a typical truth-revealing auction model, we quantify the effect of the lot size on the closing price and derive a closed-form solution for the problem of allocating inventory across multiple auctions when bidder valuation distributions are known. We also develop a decision methodology for allocating inventory across multiple auctions that dynamically incorporates the results of previous auctions as feedback into the management of subsequent auctions, and updating the lot size and number of auctions. We demonstrate how information signals from previous auctions can be used to update the auctioneer’s beliefs about the customers’ valuation distribution, and then to significantly increase the seller’s profit potential. We use several examples to reveal the benefits of using detailed transaction data for the management of sequential, multi-unit, online auctions and we demonstrate how these benefits are influenced by the inventory holding costs, the number of bidders, and the dispersion of consumers’ valuations.     

建筑业中投标合同的统计和模糊分析

许多货物和劳务合同是依据密标的拍卖签订的。在建筑业中，有报价选择特权的承包商决定工程的标价常常是一个艰难的过程。一般公认的标价办法是实际成本加毛利。本文运用主因素分析法和回归分析法来研究香港的竞争性承包商的投标成效，同时也用模糊线性回归来研究投标过程的结构。最后，根据香港建筑业投标数据库，比较了统计和模糊回归分析这两种方法的结果。     

All-pay auctions with pre- and post-bidding options

Motivated by the emergence of online penny or pay-to-bid auctions, in this study, we analyze the operational consequences of all-pay auctions competing with fixed list price stores. In all-pay auctions, bidders place bids, and highest bidder wins. Depending on the auction format, the winner pays either the amount of their bid or that of the second-highest bid. All losing bidders forfeit their bids, regardless of the auction format. Bidders may visit the store, both before and after bidding, and buy the item at the fixed list price. In a modified version, we consider a setting where bidders can use their sunk bid as a credit towards buying the item from the auctioneer at a fixed price (different from the list price). We characterize a symmetric equilibrium in the bidding/buying strategy and derive optimal list prices for both the seller and auctioneer to maximize expected revenue. We consider two situations: (1) one firm operating both channels (i.e. fixed list price store and all-pay auction), and (2) two competing firms, each operating one of the two channels.     

统计信息下在线反向拍卖的竞争分析

对于投标具有统计特征的在线反向拍卖问题,利用在线算法与平均情形竞争分析相结合的方法,讨论了单一定价策略的平均情形最优单一定价及其竞争性能,提出了无限可分商品在线反向拍卖的平均情形竞争分析策略,基于此策略建立了具有均匀分布特征的在线反向拍卖模型,通过对模型求解得到了采购商的竞争需要曲线。与不考虑投标的统计信息、只是利用常规的最坏情形竞争分析得到的在线反向拍卖的竞争策略进行对比分析,发现统计信息的利用提高了在线反向拍卖策略的竞争性能。     

Investigating buyer and seller strategies in online auctions

The annual turnover of online auctions is already in tens of billions of dollars and this amount is predicted to grow substantially over the next few years. Hence, it is important to know how buyers and sellers can influence their chances of success. Therefore, data were collected from eBay auctions for three different categories of collectible items, namely those with a published guide price, those with a rough guide price and those having no easily obtainable guide price. The options available to buyers and sellers of items were then analysed. It was found that it was hard for the seller to influence an item's achieved price significantly, apart from items with no guide price where the starting price could have an effect. Most bidders bid close to the current value and so there were insufficient data to determine the consequences of timing on the placing of high bids. For low bids, delaying a bid was found to improve significantly the chances of winning for one of the data sets.     

A kinetic thermodynamics approach to the psychology of fluctuations in financial markets

We derive a set of equations which are a simple model for investor behavior in a theoretical financial market. The model incorporates the emotional aspect of investor sentiment with memory of price history which decays exponentially in time. Within this model, the emotional reaction of the body of investors is to buy when the recent price has been increasing and sell when it has been decreasing. The rational motivations are based on capitalizing on the difference between the price and intrinsic value, with the possibility of some inertia in taking action. These two competing effects provide the basis for fluctuations and instability.     

Choice of item pricing feedback schemes for multiple unit reverse combinatorial auctions

Recently, interest in combinatorial auctions has extended to include trade in multiple units of heterogeneous items. Combinatorial bidding is complex and iterative auctions are used to allow bidders to sequentially express their preferences with the aid of auction market information provided in the form of price feedbacks. There are different competing designs for the provision of item price feedbacks; however, most of these have not been thoroughly studied for multiple unit combinatorial auctions. This paper focuses on addressing this gap by evaluating several feedback schemes or algorithms in the context of multiple unit auctions. We numerically evaluate these algorithms under different scenarios that vary in bidder package selection strategies and in the degree of competition. We observe that auction outcomes are best when bidders use a naïve bidding strategy and competition is strong. Performance deteriorates significantly when bidders strategically select packages to maximize their profit. Finally, the performances of some algorithms are more sensitive to strategic bidding than others.     

Estimating advertisers' values for paid search clickthroughs

Paid search is an important form of online advertisement. Clickthroughs from slots are bid for by advertisers. The process of formulating bids is a complex one involving bidders in competing against other advertisers in multiple auctions. It would be helpful in managing the bidding process if it were possible to determine the values placed on a clickthrough by different advertisers. The theory of two models for estimating advertiser values and associated parameters is presented. The models are applied to a set of data for searches on the term Personal Loans. The results of the model that fits the data better are evaluated. The utility of the model to practitioners is discussed. Some issues raised by the results about the role of bidding agents and the discriminatory power of Customer Relationship Management systems are considered. Ways to develop the preferred model are outlined. It is suggested that the model has implications for evaluating forecasting methods for use in paid search auctions.     

Dynamically integrated regression model for online auction data

Science China Mathematics - We propose a dynamically integrated regression model to predict the price of online auctions, including the final price. Different from existing models, the proposed...     

基于高斯分布的在线采购机制设计及竞争分析

依附于互联网电子商务的在线采购拍卖交易, 对传统的贝叶斯离线拍卖理论提出新的挑战, 因为面对不同时间点的投标, 采购电商必须即可决策出是否中标以及购买价格。鉴于此, 对于诸如石油、煤、粮食等无限可分商品的电子采购, 本文基于投标具有高斯分布特征设计了一种激励相容的在线采购策略, 演绎出在线采购的数学模型, 利用Runge-Kutta数值算法, 通过Matlab编程求解出采购电商在线定价策略的需求曲线及其对应的竞争比, 最后, 利用数值模拟, 将在线采购机制策略与纯竞争分析得到的在线采购策略比较, 结果显示利用了高斯分布信息的在线采购策略的竞争性能由于利用了投标的统计信息而得到了提高。     

购买价格递减的在线租赁问题策略设计

运用在线问题与竞争分析的方法研究了购买价格递减的在线租赁问题。通过揭示相关费用函数的性质,先后给出了最优离线策略以及在线策略。通过竞争比分析,证明了我们给出的在线策略是该问题唯一最优策略,而且该策略的竞争比随购买价格的优惠率的增加呈严格递减趋势。竞争分析结果表明考虑购买价格递减因素能够改进在线策略的竞争比从而提高决策效率。     

变动供给下的多物品拍卖

变动供给下的多物品拍卖是在线拍卖的形式之一，但对这一问题的相关研究未能真正地解决销售商如何确定待拍商品的数量和价格问题。本就变动供给下的多物品拍卖问题，建立了一个新的决策分析模型，与已有的模型相比，它不仅能同时确定待售商品的数量与价格，而且能有效地避免替代性商品间的外部性损失，为销售商的决策提供参考。     

Configurable offers and winner determination in multi-attribute auctions

The theory of procurement auctions traditionally assumes that the offered quantity and quality is fixed prior to source selection. Multi-attribute reverse auctions allow negotiation over price and qualitative attributes such as color, weight, or delivery time. They promise higher market efficiency through a more effective information exchange of buyer’s preferences and supplier’s offerings. This paper focuses on a number of winner determination problems in multi-attribute auctions. Previous work assumes that multi-attribute bids are described as attribute value pairs and that the entire demand is purchased from a single supplier. Our contribution is twofold: First, we will analyze the winner determination problem in case of multiple sourcing. Second, we will extend the concept of multi-attribute auctions to allow for configurable offers. Configurable offers enable suppliers to specify multiple values and price markups for each attribute. In addition, suppliers can define configuration and discount rules in form of propositional logic statements. These extensions provide suppliers with more flexibility in the specification of their bids and allow for an efficient information exchange among market participants. We will present MIP formulations for the resulting allocation problems and an implementation.     

A new end-of-auction model for curbing sniping

The online auction market has been growing at a spectacular rate. Most auctionsare open-bid auctions where all the participants know the current highest bid.This knowledge has led to a phenomenon known as sniping, whereby somebidders may wait until the last possible moment before bidding, therebydepriving other bidders of the opportunity to respond and also preventingsellers from obtaining the highest price for an item. This is especially true inthe case of the commonly used second-price, fixed-deadline auction. We considera procedure involving a randomly determined stopping time and show that thisapproach eliminates the potential benefits to a sniper. The scheme enables allbidders to compete more fairly and promotes an early bidding strategy, which islikely to increase the price received by the seller while providing adequatebidding opportunities for would-be buyers.     

Newsvendor with multiple options of expediting

We consider a manufacturer facing single period inventory planning problem with uncertain demand and multiple options of expediting. The demand comes at a certain time in the future. The manufacturer may order the product in advance with a relatively low cost. She can order additional amount by expediting after the demand is realized. There are a number of expediting options, each of which corresponds to a certain delivery lead time and a unit procurement price. The unit procurement price is decreasing over delivery lead time. The selling price is also decreasing over time. In this paper, we assume that the manufacturer must deliver all products to the customer in a single shipment. The problem can be formulated as a profit maximization problem. We develop structural properties and show how the optimal solution can be identified efficiently. In addition, we compare our model with the classical newsvendor model and obtain a number of managerial insights.     

基于数据包络分析的混合多属性第二分值拍卖机制

多属性拍卖在传统价格拍卖的基础上纳入了价格和质量多个属性,已广泛应用于许多领域。本文针对输入几乎没有先验结构且投标人数量足够大的情况,在以往有关多属性拍卖研究的基础上将单个投入扩展为多个投入,将物品的多个属性(价格、完成时间、劳动力数量、质量等因素)合理划分为投入或产出,设计了运行有效的基于数据包络分析的多属性第二分值拍卖机制。与其他方法相比,数据包络分析方法可以有效解决多投入多产出问题,结合多目标规划方法,可以帮助采购方在最大化自身利益的同时,找到整体表现更好的供应商。该机制满足个人理性和激励相容,与传统第二分值拍卖机制相比,在吸引投标人的前提下能够最大化拍卖人的利益。     

A multi-period inventory model with multi-dimensional procurement bidding

We consider a multi-period inventory model with raw material procurements carried out via a reverse auction. Bids are multi-dimensional, and they consist of supplier information of price, shortage quantity and lead time. This work is an extension of our earlier work that has focused on multi-dimensional procurement auctions in single-period inventory models, to multi-period settings. The new model is based on a hybrid approach combining stochastic dynamic programming and simulation.     

Visualizing Online Auctions

Online auctions have been the subject of many empirical research efforts in the fields of economics and information systems. These research efforts are often based on analyzing data from Web sites such as eBay.com which provide public information about sequences of bids in closed auctions, typically in the form of tables on HTML pages. The existing literature on online auctions focuses on tools like summary statistics and more formal statistical methods such as regression models. However, there is a clear void in this growing body of literature in developing appropriate visualization tools. This is quite surprising, given that the sheer amount of data that can be found on sites such as eBay.com is overwhelming and can often not be displayed informatively using standard statistical graphics. In this article we introduce graphical methods for visualizing online auction data in ways that are informative and relevant to the types of research questions that are of interest. We start by using profile plots that reveal aspects of an auction such as bid values, bidding intensity, and bidder strategies. We then introduce the concept of statistical zooming (STAT-zoom) which can scale up to be used for visualizing large amounts of auctions. STAT-zoom adds the capability of looking at data summaries at various time scales interactively. Finally, we develop auction calendars and auction scene visualizations for viewing a set of many concurrent auctions. The different visualization methods are demonstrated using data on multiple auctions collected from eBay.com.     

Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand

While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present a dynamic programming model to study how an online publisher should optimally allocate and price page views between guaranteed and spot markets. The problem is challenging because the allocation and pricing of guaranteed contracts affect how advertisers split their purchases between the two markets, and the terminal value of the model is endogenously determined by the updated dual force of supply and demand in auctions. We take the advertisers’ purchasing behaviour into consideration, i.e., risk aversion and stochastic demand arrivals, and present a scalable and efficient algorithm for the optimal solution. The model is also empirically validated with a commercial dataset. The experimental results show that selling page views via both channels can increase the publisher’s expected total revenue, and the optimal pricing and allocation strategies are robust to different market and advertiser types.     

Dual mechanism for an online retailer

While posted price and auction have typically been seen as alternatives to each other, we observe Web stores selling a product at a posted price and simultaneously running auctions for the identical product, a phenomenon that has not been studied fully. In this article, we study a dual mechanism, where an online retailer combines the two conventional mechanisms (posted price and auction) for multiple units of a product. We demonstrate that the dual mechanism can be used to achieve market segmentation when customers discount the expected utility of auctions. We characterize the customer’s decision rule and formulate a retailer’s profit function under the dual mechanism. Finally, we compare the performance of three selling mechanisms (posted price, auction, and dual) through computational experiments.