基于信号博弈的卖方欺诈行为分析

本文主要基于信号博弈的卖方欺诈行为进行研究，假设拍卖中可能存在欺诈型和诚实型两种卖家，其中欺诈行为有概率发生在第二价格拍卖中：欺诈型卖方冒充竞拍者递交仅次于最高价的报价从而获得额外收益。两种卖家根据各自效用选择拍卖形式：第一价格或者第二价格。而竞买者将卖者的选择作为信号，更新对卖方类型的判断，然后制定报价策略。这是一个买卖方信号交叉影响的过程。考虑到拍卖过程中买方价值相关性，本文在建立模型中参考了关联价值原理。针对该模型进行分析，得出了不同情况下的买卖方策略，并且研究了买方报价、买方判断、卖方收益三者之间的关系。文中利用贝叶斯公式对双方的策略选择问题进行预测，与单纯的概率分布方法相比，更具实践价值。     

A Study of Online Auction Sellers’ Intention to Switch Platform: The Case of Yahoo!Kimo Versus Ruten_eBay*

The study of consumers’ switching from one service provider to another has a long tradition in economics, information systems, and marketing. The emergence of electronic commerce presents new challenges in understanding consumers’ switching intentions in the context of e‐commerce in general and online auctions in particular. With the abundance of literature on online auctions, there is a surprising lack of research on auction sellers’ intentions to switch from one online auction platform to another. Using the competition between Yahoo!Kimo and Ruten_eBay, two leading auction platforms in Taiwan, as the backdrop, we developed a research model and collected empirical data based on this real case to study what factors influence auction sellers to switch to a competing service provider. We find that the higher the procedural switching costs, financial switching costs, relational switching costs, site design quality, or interaction quality, the lower the intention of an auction seller to switch to a competing auction service provider. A higher perceived transaction fee, however, leads to a higher switching intention.     

Strategic Supply Function Competition With Private Information

A finite number of sellers (n) compete in schedules to supply an elastic demand. The cost of each seller is random, with common and private value components, and the seller receives a private signal about it. A Bayesian supply function equilibrium is characterized: The equilibrium is privately revealing and the incentives to rely on private signals are preserved. Supply functions are steeper with higher correlation among the cost parameters. For high (positive) correlation, supply functions are downward sloping, price is above the Cournot level, and as we approach the common value case, price tends to the collusive level. As correlation becomes maximally negative, we approach the competitive outcome. With positive correlation, private information coupled with strategic behavior induces additional distortionary market power above full information levels. Efficiency can be restored with appropriate subsidy schemes or with a precise enough public signal about the common value component. As the market grows large with the number of sellers, the equilibrium becomes price‐taking, bid shading is on the order of 1/n, and the order of magnitude of welfare losses is 1/n². The results extend to inelastic demand, demand uncertainty, and demand schedule competition. A range of applications in product and financial markets is presented.     

Modeling Reputation as a Time‐Series: Evaluating the Risk of Purchase Decisions on eBay*

Reputation systems based on buyer feedback play an important role in today's online markets. In this article, we provide a rigorous methodology to establish a relationship between a seller's feedback history and risk of default. We validate this method against eBay's reputation system, using a dataset of terminated users (Not‐A‐Registered‐User or NARU) and the feedback left for them by buyers. By treating feedback rating data as a function of time, we characterize the tendency of change in seller feedback ratings in order to predict the behavior of a seller. We find that NARU sellers have significantly more negative feedback in their final weeks. Applying functional principal component analysis and classification tree methods, we find that when projecting the feedback data to an appropriate space, NARU and non‐NARU sellers can be distinguished at better than 92% accuracy. We use this to provide a quantitative mechanism for evaluating the risk of trading with a seller who has less than perfect feedback, and offer advice on how much a buyer should offer to pay, given an asking price on a commodity item and a seller's feedback history.     

Dynamic Matching,Two‐Sided Incomplete Information,and Participation Costs: Existence and Convergence to Perfect Competition

Consider a decentralized, dynamic market with an infinite horizon and participation costs in which both buyers and sellers have private information concerning their values for the indivisible traded good. Time is discrete, each period has length δ, and, each unit of time, continuums of new buyers and sellers consider entry. Traders whose expected utility is negative choose not to enter. Within a period each buyer is matched anonymously with a seller and each seller is matched with zero, one, or more buyers. Every seller runs a first price auction with a reservation price and, if trade occurs, both the seller and the winning buyer exit the market with their realized utility. Traders who fail to trade continue in the market to be rematched. We characterize the steady‐state equilibria that are perfect Bayesian. We show that, as δ converges to zero, equilibrium prices at which trades occur converge to the Walrasian price and the realized allocations converge to the competitive allocation. We also show the existence of equilibria for δ sufficiently small, provided the discount rate is small relative to the participation costs.     

The Dark Side of Information and Market Efficiency in E‐Markets*

Price dispersion reflects the differences in prices for identical products. While in physical markets such dispersion is prevalent due to high search costs, many researchers argue that search costs and price dispersion will be much lower in electronic markets (e‐markets). Empirical evidence does not support this contention, and researchers have studied search costs, market factors, and service‐quality factors to explain this dispersion. Previous research has largely assumed that more information is better. By ignoring the dark side of information, we argue that only a partial understanding of price dispersion is possible. In this article, information overload and equivocality are studied as two dark attributes of information that lead sellers to different pricing decisions in e‐markets. Hypotheses relating these attributes to price dispersion are supported through analysis of 161 product markets. This work opens up new avenues in the study of e‐markets and discusses the implications of these findings for research and practice on consumer and seller decisions.     

网络销售中退货担保与定价决策

本文采用退货担保期权综合完全退货策略与部分退货策略的优点,构建了网络销售中基于期权的退货与定价模型,以商品的价格、退货策略和退货担保期权价格为决策变量,讨论了存在退货期权和不存在退货期权两种情况下,最优决策存在的条件,求解出销售商最优价格、退货策略和退货担保期权价格,分析了各个市场参数变化对最优值的影响,通过对担保退货期权与退货策略同时存在和购买期权才能退货二种策略的比较,发现供应商最优策略是前者。算例验证了以上结论,做了敏感性分析,总结了相关管理启示与未来研究方向。所得结论对网络销售商开展网上销售有重要参考价值。     

Efficient Multi‐Attribute Multi‐Unit Auctions for B2B E‐Commerce Logistics

This study is the first proposing allocatively efficient multi‐attribute auctions for the procurement of multiple items. In the B2B e‐commerce logistics problem (ELP), the e‐commerce platform is the shipper generating a large number of online orders between product sellers and buyers, and third‐party logistics (3PL) providers are carriers that can deliver these online orders. This study focuses on the ELP with multiple attributes (ELP‐MA), which is generally the problem of matching the shipper's online orders and 3PL providers given that price and other attributes are jointly evaluated. We develop a one‐sided Vickrey–Clarke–Groves (O‐VCG) auction for the ELP‐MA. The O‐VCG auction leads to incentive compatibility (on the sell side), allocative efficiency, budget balance, and individual rationality. We next introduce the concept of universally unsatisfied set to construct a primal‐dual algorithm, also called the primal‐dual Vickrey (PDV) auction. We prove that the O‐VCG auction can be viewed as a single‐attribute multi‐unit forward Vickrey (SA‐MFV) auction. Both PDV and SA‐MFV auctions realize VCG payments and truthful bidding for general valuations. This result reveals the underlying link not only between single‐attribute and multi‐attribute auctions, but between static and dynamic auctions in a multi‐attribute setting.     

Repurposing Materials and Waste through Online Exchanges: Overcoming the Last Hurdle

Online material and waste exchanges (OMWEs) provide online channels to repurpose by‐products, unused materials and waste from industrial and commercial facilities. Unfortunately, OMWE's also have challenges. First, sellers may have access to other disposal options and, as a result, may not fully commit to the exchange. Second, buyers can face high uncertainty about the product exchanged and the transaction being undertaken. Overcoming these challenges is the “last hurdle” to making OMWEs successful. This study investigates the factors that reduce the buyers' uncertainty and increase the sellers' commitment to the OMWE. We analyze novel transaction‐level data from an online exchange (MNExchange.org) combined with other archival public records on county‐level repurposing and disposal statistics. First, we find that regional repurposing policies and alternatives have a complementary effect on sellers' commitment toward OMWEs, resulting in increased OMWE exchanges. However, regional disposal policies and alternatives have a substitution effect on sellers' commitment, resulting in reduced exchange success. Further, greater product and transaction information reduce the buyer's uncertainty and increase exchange success. Finally, the analysis shows that users' (buyers and sellers) heavily rely on their prior experience with OMWEs. Specifically, higher familiarity between the buyer–seller pair and familiarity with the OMWE system leads to higher likelihood of exchange success. This study lays the foundation for understanding OMWEs and has important implications for developing policies and operations to increase online transactions of by‐products, materials and wastes.     

在线拍卖中的“托”出价研究

本文分析了独立的私人估价环境下最优保留价格与竞买人数之间的关系,纠正了最优保留价格与竞买人数无关的传统观点.在此基础上,讨论了卖者可能递交"托"出价的条件以及拍卖中只剩下一个竞买人时卖者的最优"托"出价.     

不同线上销售模式的产品定价策略研究

现售和预售是线上销售的主要模式。考虑不同模式下消费者对产品估值的差异，分析现售、预售和混合销售（同时开展现售和预售）三种模式下消费者购买决策过程，构建Stackelberg博弈和Nash均衡模型，得到三种模式的产品均衡定价及需求，并探讨开展不同销售模式对应的消费者估值差异阈值以及不同主导结构对供应链企业绩效的影响。结果表明：任意主导结构下，销售模式只会影响现售产品价格，对预售产品定价没有影响；任意销售模式下，制造商主导结构的产品定价和批发价更高，但产品销售量较低；消费者延迟收货敏感度增强使预售模式的供应链成员企业的利润降低，但混合销售模式可以有效防止由此引起的损失，且制造商主导可以同时实现供应链成员企业的利益最大化。     

Reverse Auctions with Multiple Reinforcement Learning Agents*

Reverse auctions in business‐to‐business (B2B) exchanges provide numerous benefits to participants. Arguably the most notable benefit is that of lowered prices driven by increased competition in such auctions. The competition between sellers in reverse auctions has been analyzed using a game‐theoretic framework and equilibria have been established for several scenarios. One finding of note is that, in a setting in which sellers can meet total demand with the highest‐bidding seller being able to sell only a fraction of the total capacity, the sellers resort to a mixed‐strategy equilibrium. Although price randomization in industrial bidding is an accepted norm, one might argue that in reality managers do not utilize advanced game theory calculations in placing bids. More likely, managers adopt simple learning strategies. In this situation, it remains an open question as to whether the bid prices converge to the theoretical equilibrium over time. To address this question, we model reverse‐auction bidding behavior by artificial agents as both two‐player and n‐player games in a simulation environment. The agents begin the game with a minimal understanding of the environment but over time analyze wins and losses for use in determining future bids. To test for convergence, the agents explore the price space and exploit prices where profits are higher, given varying cost and capacity scenarios. In the two‐player case, the agents do indeed converge toward the theoretical equilibrium. The n‐player case provides results that reinforce our understanding of the theoretical equilibria. These results are promising enough to further consider the use of artificial learning mechanisms in reverse auctions and other electronic market transactions, especially as more sophisticated mechanisms are developed to tackle real‐life complexities. We also develop the analytical results when one agent does not behave strategically while the other agent does and show that our simulations for this environment also result in convergence toward the theoretical equilibrium. Because the nature of the best response in the new setting is very different (pure strategy as opposed to mixed), it indicates the robustness of the devised algorithm. The use of artificial agents can also overcome the limitations in rationality demonstrated by human managers. The results thus have interesting implications for designing artificial agents in automating bid responses for large numbers of bids where human intervention might not always be possible.     

基于好评返现和差评偏好的体验型产品两阶段定价决策研究

假定产品存在发布试销期与正式销售期,在销售商不采取好评返现与采取好评返现两种情形下分别建立考虑消费者差评偏好的两阶段定价决策模型,用KT方法得到相应的最优决策和利润,给出了销售商实施好评返现的条件。基于导函数分析方法与数值仿真技术,分析了好评返现与差评偏好对产品定价、销售商利润和消费者效用的影响,证实了好评返现下消费者重视差评信息的合理性,并针对消费者、平台和销售商提出了对策。研究表明:只有产品质量中等的销售商才会采取好评返现策略,其利润随产品质量的提升而先递增后递减;好评返现提高了销售商第二阶段的市场份额和售价,导致消费者效用损失;消费者越重视差评信息,实施好评返现的高(低)质量产品销售商越多(少),且销售商利润将下降;好评返现扭曲了评价信息的真实性,降低了消费者对在线评价系统的信任度。     

Managing the Revenue of Sales Platforms: Insights into Service Fees

Online sales platforms have grown substantially in recent years. These platforms assist sellers to conduct sales, and in return, collect service fees from sellers. We study the fee policies by considering a fee‐setting platform, on which a seller may conduct a sale with a reserve price to a group of potential buyers: the seller retains the object for sale if the final trading price is below the reserve price. The platform may charge two types of fees as in current practice: a reserve fee as a function of the seller's reserve price and a final value fee as a function of the sale's final trading price. We derive the optimality condition for fee policies, and show that the platform can use either just a final value fee or just a reserve fee to achieve optimality. In the former case, the optimal final value fee charged by the platform is independent of the number of buyers. In the latter case, the optimal reserve fee is often a decreasing, instead of increasing, function of the seller's reserve price. An increasing reserve fee may make the seller reluctant to use a positive reserve price and hurt the platform's revenue. In general, the optimal fees are nonlinear functions, but in reality, linear fees are commonly used because of their simplicity for implementation. We show that a linear fee policy is indeed optimal in the case that the seller's valuation follows a power distribution. In other cases, our numerical analysis suggests close‐to‐optimal performance of the linear policy.     

Do Auction Parameters Affect Buyer Surplus in E‐Auctions for Procurement?

Although the initial euphoria about Internet‐enabled reverse auctions has given way to a cautious but widespread use of reverse auctions in business‐to‐business (B2B) procurement, there is a limited understanding of the effect of auction design parameters on buyer surplus. In this paper, we study the effect of bidding competition, information asymmetry, reserve price, bid decrement, auction duration, and bidder type on buyer surplus. We collected field data on more than 700 online procurement auctions conducted by a leading auctioneer and involving procurement items worth millions of dollars. Consistent with the predictions of auction theory, the results indicate that bidding competition, reserve price, and information sharing affect buyer surplus. Unlike previous findings in the consumer‐to‐consumer context, we find that bid decrement and auction duration have no effect in B2B procurement auctions. Our results suggest that use of the rank‐bidding format increases buyer surplus when incumbent suppliers participate in the auction. We discuss the theoretical and managerial implications of these findings for future research and for optimal design of online procurement auctions.     

带佣金率和保留价的一级和二级价格拍卖

本文通过引入佣金率和保留价,分别讨论了一级和二级价格密封式拍卖,得到以下结论:(1)两种密封式拍卖的均衡报价随佣金率的提高而减少;(2)一级价格密封式拍卖的均衡报价随保留价的增加而增加,而二级价格密封式拍卖的均衡报价随保留价的增加而减少;(3)一级价格密封式拍卖的投标者预期收益与佣金率和保留价均呈反向变动,而二级价格密封式拍卖的投标者预期收益独立于佣金率和保留价;(4)收益等价定理仍然成立;(5)在两种密封式拍卖中,当佣金率增加时,卖方的预期收益减少,拍卖行的预期收益以及卖方和拍卖行的总预期收益可能增加也可能减少;(6)在两种密封式拍卖中,当保留价增加时,卖方的预期收益、拍卖行的预期收益以及卖方和拍卖行的总预期收益可能增加也可能减少;(7)给出了卖方的最优保留价的表达式;(8)把本文的结论与以前相应的研究工作做了比较。     

Real options valuation principle in the multi-period base-stock problem

This paper analyzes the multi-period base-stock problem where there is a financial risk associated with a stochastic demand. For the single-period problem, it is known that the optimal inventory policy can be obtained with the Black and Scholes option pricing formula. This paper pushes the analysis further by applying the options valuation framework to the multi-period problem and presenting an algorithm for finding the optimal inventory policy. A computational study indicates that the effect of systematic risk is typically negligible (as for the single-period problem). Therefore, it can be concluded that systematic risk in demand is of little importance for optimal inventory control.     

COMPETING AUCTIONS

This paper shows that larger auctions are more efficient than smaller ones, but that despite this scale effect, two competing and otherwise identical markets or auction sites of different sizes can coexist in equilibrium. We find that the range of equilibrium market sizes depends on the aggregate buyer‐seller ratio, and also whether the markets are especially thin. (JEL: D44, L11)     

An Experimental Investigation of Procurement Auctions with Asymmetric Sellers

Electronic reverse auctions are a commonly used procurement mechanism. Research to date has focused on suppliers who are ex ante symmetric in that their costs are drawn from a common distribution. However, in many cases, a seller's range of potential costs depends on their own operations, location, or economies of scale and scope. Thus, understanding how different bidder types impact auction outcomes is key when designing an auction. This study reports the results of the first controlled laboratory experiment designed to compare prices between first‐price and second‐price procurement auctions for homogeneous goods when seller cost types are asymmetric and the number of bidders varies. The results indicate that first‐price auctions generate lower prices regardless of market composition. The results also reveal that first‐price auctions are at least weakly more efficient than second‐price auctions despite the theoretical prediction that the reverse should hold in asymmetric auctions. Post hoc analysis of individual bidders' behavior in first‐price auctions revealed evidence that bidders systematically underbid when their cost realizations were close to the lower bound. Furthermore, bidders adjust their behavior based on the type of the other bidders in the market in a manner inconsistent with theory. Consequently, adding a third bidder to a two‐bidder market is not advantageous to the buyer unless that third bidder is a low‐cost type.     

Feedback Mechanisms,Judgment Bias,and Trust Formation in Online Auctions*

Online markets, like eBay, Amazon, and others rely on electronic reputation or feedback systems to curtail adverse selection and moral hazard risks and promote trust among participants in the marketplace. These systems are based on the idea that providing information about a trader's past behavior (performance on previous market transactions) allows market participants to form judgments regarding the trustworthiness of potential interlocutors in the marketplace. It is often assumed, however, that traders correctly process the data presented by these systems when updating their initial beliefs. In this article, we demonstrate that this assumption does not hold. Using a controlled laboratory experiment simulating an online auction site with 127 participants acting as buyers, we find that participants interpret seller feedback information in a biased (non‐Bayesian) fashion, overemphasizing the compositional strength (i.e., the proportion of positive ratings) of the reputational information and underemphasizing the weight (predictive validity) of the evidence as represented by the total number of transactions rated. Significantly, we also find that the degree to which buyers misweigh seller feedback information is moderated by the presentation format of the feedback system as well as attitudinal and psychological attributes of the buyer. Specifically, we find that buyers process feedback data presented in an Amazon‐like format—a format that more prominently emphasizes the strength dimension of feedback information—in a more biased (less‐Bayesian) manner than identical ratings data presented using an eBay‐like format. We further find that participants with greater institution‐based trust (i.e., structural assurance) and prior online shopping experience interpreted feedback data in a more biased (less‐Bayesian) manner. The implications of these findings for both research and practice are discussed.