Pricing commodities

How should a seller price her goods in a market where each buyer prefers a single good among his desired goods, and will buy the cheapest such good, as long as it is within his budget? We provide efficient algorithms that compute near-optimal prices for this problem, focusing on a commodity market, where the range of buyer budgets is small. We also show that our LP rounding based technique easily extends to a different scenario, in which the buyers want to buy all the desired goods, as long as they are within budget.     

The role of online seller reviews and product price on buyers' willingness-to-pay: a risk perspective

Third-party interpersonal communications such as online seller reviews play an important role in buyers’ purchase decisions in online markets. Although it is empirically clear that seller reviews (volume and valence) and product price contribute to buyers’ willingness-to-pay (WTP) differently across various studies, it is theoretically less understood why such effects qualitatively differ (e.g., positive vs negative), rendering unclear managerial implications for online marketers. In this paper, we study the role of online seller reviews and product price in buyers’ WTP. We offer a conceptual framework from a risk perspective in which we argue that the different effects of seller reviews and product price on buyers’ WTP may emerge simultaneously in an online market. We highlight two important drivers for such qualitatively different effects: a difference in buyers’ risk attitudes (averse, neutral, or seeking) and a difference in WTP measures (absolute or relative). We test our hypotheses and find good support for them both internally (an experimental study) and externally (an empirical study). Our research enhances the understanding of the relationship between online user reviews and online price dispersions while shedding light on better management of online user reviews for market makers.     

Intelligent agents in electronic markets for information goods: customization, preference revelation and pricing

Electronic commerce has enabled the use of intelligent agent technologies that can evaluate buyers, customize products, and price in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of agent-based technologies that price dynamically based on product preference information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off has a higher impact on buyers on the higher end of the market and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, seller revenues decrease since the gains from better buyer information are dominated by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.     

多智能体协同选择的智能导购模型

基于多智能体协同选择提出了一种导购选择模型,该模型可识别其他可信买方智能体("值得信赖的朋友"),并将它们关于卖方的信息结合自身关于卖方的信息综合起来协同选择质高价低的卖方,从而实现高质量的导购性能。构建了一个存在多种类型的买方和卖方的购物模拟环境,并进行了多组实验。实验结果表明,该模型可以准确地识别可信买方智能体,并可在复杂的购物环境中高效地选择出优质卖方。此外,实验结果还表明,有了该模型,单个买方智能体选择优质卖方的能力要明显高于无多智能体协同选择情况下单个买方智能体的选择能力。     

Using “last-minute” sales for vertical differentiation on the Internet

In Internet-based commerce, sellers often use multiple distribution channels for the sale of standard consumer goods. We study a model of second-degree price discrimination in which a monopolist sells to risk-averse buyers. The seller uses two channels that differ in their risk attributes. In one channel prices and qualities are fixed and availability is assured. In the second channel, the seller offers a joint distribution of prices and qualities and may not guarantee availability. We characterize optimal two-channel selling policies. We show that it can be optimal to offer multiple identical items in a random sale event. However, the seller cannot benefit by offering two distinct quality levels in a sale event that is held with probability smaller than one. We use the model to offer explanations for the observed behavior of online sellers and discuss implementation issues in recent e-commerce environments.     

An agent-based system for bilateral contracts of energy

In this paper, an agent-based system for bilateral contracts of energy is proposed. The generating companies submit their offers to the demand companies. The demand companies also submit their bids to the generators. Each load or generator’s agent wants to match with an opponent, which offers the most valuable proposal. However, the problem of simultaneous decision-making causes decision conflicts among the agents. To overcome this conflict, we assume loads as the leaders and generators as the followers. We use Stackelberg game to match the seller and buyer agents. The negotiation process between a buyer and its potential seller will determine the power price between them. This process is carried out through a proposed combined time-behavioral protocol (TBP). With negligible changes in around the agreed price, this protocol can reduce the negotiation time considerably. After successful negotiation, the seller and buyer agents could sign a bilateral contract of energy if the market conditions allow it. The applicability of the proposed method is illustrated through a case study.     

Auction-based distributed efficient economic operations of microgrid systems

This paper studies the economic operations of the microgrid in a distributed way such that the operational schedule of each of the units, like generators, load units, storage units, etc., in a microgrid system, is implemented by autonomous agents. We apply and generalise the progressive second price (PSP) auction mechanism which was proposed by Lazar and Semret to efficiently allocate the divisible network resources. Considering the economic operation for the microgrid systems, the generators play as sellers to supply energy and the load units play as the buyers to consume energy, while a storage unit, like battery, super capacitor, etc., may transit between buyer and seller, such that it is a buyer when it charges and becomes a seller when it discharges. Furthermore in a connected mode, each individual unit competes against not only the other individual units in the microgrid but also the exogenous main grid possessing fixed electricity price and infinite trade capacity; that is to say, the auctioneer assigns the electricity among all individual units and the main grid with respect to the submitted bid strategies of all individual units in the microgrid in an economic way. Due to these distinct characteristics, the underlying auction games are distinct from those studied in the literature. We show that under mild conditions, the efficient economic operation strategy is a Nash equilibrium (NE) for the PSP auction games, and propose a distributed algorithm under which the system can converge to an NE. We also show that the performance of worst NE can be bounded with respect to the system parameters, say the energy trading price with the main grid, and based upon that, the implemented NE is unique and efficient under some conditions.     

Vulnerability Bazaar

A pure vulnerability market is one in which each discrete vulnerability is a unit of trade with a price assigned to it by the buyer, seller, and demand. In such a market, exclusivity of knowledge is a key factor in overall value, thus when a vulnerability becomes public knowledge, it loses its value. Other factors also come into play, such as the affected product's popularity, the vulnerability's security impact, and the exploit's ease and efficacy. Vulnerabilities in this market retain their peak value when very few people know about them; value decreases through events such as vendor notification, information leaks, independent rediscovery, or accidental discovery of the vulnerability due to attack activity in the wild. Because it's difficult to certify and appraise information exclusivity, many buyers contractually obligate vulnerability reporters to exclusivity agreements to ensure that their information is exclusive to the best of their knowledge. Very few buyers are interested in nonexclusive information.     

Who wins when price information is more ubiquitous? An experiment to assess how infomediaries influence price

Internet retailers often make use of price search services (infomediaries) that have the effect of reducing consumer search and expanding seller market presence. Research on price and advertising suggests that this may not be a profitable strategy. We develop an experimental posted-offer market to estimate the impact of infomediaries on price of homogenous goods. We analyze panel data from a range of market conditions that address how infomediaries services affect both seller-level offered price as well as market-level transacted price. We find that, while sellers attempt to extract higher prices through a higher initial offer price when using an infomediary, they fail to successfully collect it; transacted prices remain largely unchanged. As a consequence the benefits of infomediaries fall primarily to the buyer who faces largely unchanged transacted prices, and substantially reduced search costs. Search costs are, in essence shifted to the seller. Sellers who participate in this sort of price comparison should be aware that they are following a high-volume/low profit per unit strategy.     

Implications of a Reserve Price in an Agent-Based Common-Value Auction

Auction sellers can use a reserve price to require a minimum bid before items are sold. Theoretical and experimental research has tested the influence of a reserve price in an independent private values auction, but little focus has been given to the influence of a reserve price in a first-price common-value auction. We establish an agent-based first-price common-value auction to determine the impact of the reserve price with two buyers and with three buyers. An agent-based approach to this problem is both a unique contribution to the literature and appropriate since finding analytical solutions with common-value auctions is difficult. The agent-based model approach also allows us to consider buyers that have non-symmetric bid functions. Furthermore, we introduce a combination of numerical integration techniques with a new particle swarm learning algorithm. The buyers in the model choose their expected-net-revenue-maximizing bid price, and sellers choose their expected-revenue-maximizing reserve price. In the two-buyer and three-buyer auction, a reserve price increases the equilibrium winning bid price, decreases the probability that the item is sold, and increases the seller’s expected revenue. A reserve price in a two-buyer auction increases the winning bid price more than including an additional buyer in the auction with no reserve price. However, due to only receiving a salvage value when the item does not sell in the auction, the seller’s expected revenue is higher in the three-buyer first-price common-value auction without a reserve price than in the two-buyer auction with a reserve price.     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

Nash equilibrium solution in a vendor–buyer supply chain model with permissible delay in payments

In practice, vendors (or sellers) often offer their buyers a fixed credit period to settle the account. The benefits of trade credit are not only to attract new buyers but also to avoid lasting price competition. On the other hand, the policy of granting a permissible delay adds not only an additional cost but also an additional dimension of default risk to vendors. In this paper, we will incorporate the fact that granting a permissible delay has a positive impact on demand but negative impacts on both costs and default risks to establish vendor–buyer supply chain models. Then we will derive the necessary and sufficient conditions to obtain the optimal solution for both the vendor and the buyer under non-cooperative Nash equilibrium. Finally, we will use two numerical examples to show that (1) granting a permissible delay may significantly improve profits for both the vendor and the buyer, and (2) the sensitivity analysis on the optimal solution with respect to each parameter.     

The impact of available information on negotiation results

Negotiation is the most famous tool for reaching an agreement between parties. Usually, the different parties can be modeled as a buyer and a seller, who negotiate about the price of a given item. In most cases, the parties have incomplete information about one another, but they can invest money and efforts in order to acquire information about each other. This leads to the question of how much each party will be willing to invest on information about its opponent, prior to the negotiation process. In this paper, we consider the profitability of automated negotiators acquiring information on their opponents. In our model, a buyer and a seller negotiate on the price of a given item. Time is costly, and incomplete information exists about the reservation price of both parties. The reservation price of the buyer is the maximum price it is willing to pay for an item or service, and the reservation price of the seller is the minimum price it is willing to receive in order to sell the item or service. Our research is based on Cramton’s symmetrical protocol of negotiation that provides the agents with stable and symmetric strategies, and involves a delay in proposing an offer for signaling. The parties in Cramton’s model delay their offers in order to signal their strength, and then an agreement is reached after one or two offers. We determine the Nash equilibrium for agents that prefer to purchase information. Then, in addition to the theoretical background, we used simulations to check which type of equilibrium will actually be obtained. We found that in most of the cases, each agent will prefer to purchase information only if its opponent does. The reason for these results lies in the fact that an agent that prefers to purchase information according to a one-side method, signals its weakness and thereby reduces its position in the negotiation. Our results demonstrate the efficiency of joint information acquisition by both agents, but they also show that one-sided information purchasing may be inefficient, if the acquisition activity is revealed by the opponent, which causes it to infer that the informed agent is relatively weak.     

Shopbot Economics

Shopbots are Internet agents that automatically search for information pertaining to the price and quality of goods and services. As the prevalence and usage of shopbots continues to increase, one might expect the resultant reduction in search costs to alter market behavior significantly. We explore the potential impact of shopbots upon market dynamics by proposing, analyzing, and simulating a model that is similar in form to some that have been studied by economists investigating the phenomenon of price dispersion. However, the underlying assumptions and methodology of our approach are different, since our ultimate goal is not to explain human economic behavior, but rather to design economic software agents and study their behavior. We study markets consisting of shopbots and other agents representing buyers and sellers in which (i) search costs are nonlinear, (ii) some portion of the buyer population makes no use of search mechanisms, and (iii) shopbots are economically motivated, strategically pricing their information services so as to maximize their own profits. Under these conditions, we find that the market can exhibit a variety of hitherto unobserved dynamical behaviors, including complex limit cycles and the co-existence of several buyer search strategies. We also demonstrate that a shopbot that charges buyers for price information can manipulate markets to its own advantage, sometimes inadvertently benefitting buyers and sellers.     

Nash and integrated solutions in a just-in-time seller–buyer supply chain with buyer's ordering cost reductions

The seller frequently offers the buyer trade credit to settle the purchase amount. From the seller's prospective, granting trade credit increases not only the opportunity cost (i.e., the interest loss on the buyer's purchase amount during the credit period) but also the default risk (i.e., the rate that the buyer will be unable to pay off his/her debt obligations). On the other hand, granting trade credit increases sales volume and revenue. Consequently, trade credit is an important strategy to increase seller's profitability. In this paper, we assume that the seller uses trade credit and number of shipments in a production run as decision variables to maximise his/her profit, while the buyer determines his/her replenishment cycle time and capital investment as decision variables to reduce his/her ordering cost and achieve his/her maximum profit. We then derive non-cooperative Nash solution and cooperative integrated solution in a just-in-time inventory system, in which granting trade credit increases not only the demand but also the opportunity cost and default risk, and the relationship between the capital investment and the ordering cost reduction is logarithmic. Then, we use a software to solve and compare these two distinct solutions. Finally, we use sensitivity analysis to obtain some managerial insights.     

An options-based solution to the sequential auction problem

The sequential auction problem is commonplace in open, electronic marketplaces such as eBay. This is the problem where a buyer has no dominant strategy in bidding across multiple auctions when the buyer would have a simple, truth-revealing strategy if there was but a single auction event. Our model allows for multiple, distinct goods and market dynamics with buyers and sellers that arrive over time. Sellers each bring a single unit of a good to the market while buyers can have values on bundles of goods. We model each individual auction as a second-price (Vickrey) auction and propose an options-based, proxied solution to provide price and winner-determination coordination across auctions. While still allowing for temporally uncoordinated market participation, this options-based approach solves the sequential auction problem and provides truthful bidding as a weakly dominant strategy for buyers. An empirical study suggests that this coordination can enable a significant efficiency and revenue improvement over the current eBay market design, and highlights the effect on performance of complex buyer valuations (buyers with substitutes and complements valuations) and varying the market liquidity.     

一口价的信息与信誉机制——基于网上拍卖数据的实证研究

经典拍卖理论无法理解网络拍卖中的一口价拍卖,已有文献从风险态度和交易成本的角度给出了解释,但却难以解释"一口价"设定比例的横截面差异。利用网站大样本数据,本文对已有理论进行了检验,证实了风险态度和交易成本的作用。然而控制这些因素之后,卖家信用水平仍然对一口价的设定和执行具有显著的影响,一口价对于买家估价和出价的参考作用同样显著,而且效果与卖家的信用水平正相关,从而验证了一口价的信息与信誉机制。     

Multi‐Unit Bayesian Auction with Demand or Budget Constraints

We consider the problem of revenue maximization on multi‐unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount b_i to compete for ad slots on a web page. The value of each ad slot corresponds to its click‐through‐rate, and each buyer has her own per‐click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting. We develop the optimal (with maximum revenue) truthful auction for a relaxed demand model (where each buyer i wants at most d_i items) and a sharp demand model (where buyer i wants exactly d_i items). We also find an auction that always guarantees at least half of the revenue of the optimal auction when the buyers are budget constrained. Moreover, all of the auctions we design can be computed efficiently, that is, in polynomial time.     

Dynamic Consumer Profiling and Tiered Pricing Using Software Agents

Shopbots or software agents that enable comparison shopping of items from different online sellers have become popular for quick and easy shopping among online buyers. Rapid searches and price comparison by shopbots have motivated sellers to use software agents called pricebots to adjust their prices dynamically so that they can maintain a competitive edge in the market. Existing pricebots charge the same price for an item from all of their customers. Online consumers differ in their purchasing preferences and, therefore, a seller's profit can be increased by charging two different prices for the same good from price-insensitive and price-sensitive consumers. In this paper, we present an algorithm that partitions the buyer population into different segments depending on the buyers' purchase criteria and then charges a different price for each segment. Simulation results of our tiered pricing algorithm indicate that sellers' profits are improved by charging different prices to buyers with different purchase criteria. Price wars between sellers that cause regular price fluctuations in the market, are also prevented when all the sellers in the market use a tiered pricing strategy.