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.     

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。     

How well do shopbots represent online markets? A study of shopbots’ vendor coverage strategy

Consumers often use shopbots to search for information when making purchase decisions in Internet markets. Although they have varying sensitivity to shopbot bias, consumers generally prefer accurate market representation. However, in choosing the accuracy of market representation, shopbots must balance the desires of consumers with the costs of providing their services and with the desires of the vendors, who are often the largest source of their revenue. In this paper, we study how accurately shopbots represent a market and analyze the strategies shopbots adopt to achieve market representativeness. We theoretically identify two important drivers in shopbot vendor coverage strategy – how many vendors it covers (shopbot size) and which vendors it covers (shopbot affiliation) – and analytically show how the drivers affect shopbot market representativeness. We report the results of a large-scale study in which we collected 2.2 million vendor price listings from eight shopbots and develop metrics for measuring shopbot size, shopbot affiliation, and shopbot market representativeness. We found that (1) shopbots do not represent markets equally well; (2) size drives a shopbot's market representativeness positively whereas affiliation drives a shopbot's market representativeness negatively; (3) shopbots follow differnet vendor representative strategies to pursue market representativeness.     

Design of a Multi–Unit Double Auction E–Market

We envision a future economy where e–markets will play an essential role as exchange hubs for commodities and services. Future e–markets should be designed to be robust to manipulation, flexible, and sufficiently efficient in facilitating exchanges. One of the most important aspects of designing an e–market is market mechanism design. A market mechanism defines the organization, information exchange process, trading procedure, and clearance rules of a market. If we view an e–market as a multi–agent system, the market mechanism also defines the structure and rules of the environment in which agents (buyers and sellers) play the market game. We design an e–market mechanism that is strategy–proof with respect to reservation price, weakly budget–balanced , and individually rational . Our mechanism also makes sellers unlikely to underreport the supply volume to drive up the market price. In addition, by bounding our market's efficiency loss, we provide fairly unrestrictive sufficient conditions for the efficiency of our mechanism to converge in a strong sense when (1) the number of agents who successfully trade is large, or (2) the number of agents, trading and not , is large. We implement our design using the RETSINA infrastructure, a multi–agent system development toolkit. This enables us to validate our analytically derived bounds by numerically testing our e–market.     

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.     

The Value of Temptation

There is an implicit assumption in electronic commerce that induces the buyers to believe that their deals will be handled appropriately. However, after a seller has already committed to a buyer, he may be tempted by several requests though he will not be able to supply them all. We analyze markets in which a finite set of automated buyers interacts repeatedly with a finite set of automated sellers. These sellers can satisfy one buyer at a time, and they can be tempted to break a commitment they already have. We have found the perfect equilibria that exist in markets with a finite horizon, and with an unrestricted horizon. A significant result stemming from our study reveals that sellers are almost always tempted to breach their commitments. However, we also show that if markets' designers implement an external mechanism that restricts the automated buyers actions, then sellers will keep their commitments.     

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.     

MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.     

E-Commerce in the Indian Insurance Industry: Prospects and Future

With the advent of the Internet, online processes are replacing conventional models in our society. The greatest impact in online technology has been achieved by e-commerce. E-commerce is attractive both to buyers and sellers as it reduces search costs for buyers and inventory costs for sellers. In this paper we investigate the impact of e-commerce on the insurance industry in India. The recent growth of Internet infrastructure and introduction of economic reforms in the insurance sector have opened up the monopolistic Indian insurance market to competition from foreign alliances. We study the evolving scenario in the insurance industry in India and identify the features of online insurance that improve the conventional insurance model and thus, makes it more attractive for the Indian insurance industry to go online.     

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.     

Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach

In this paper, we present a model for evaluating the trustworthiness of advice about seller agents in electronic marketplaces. In particular, we propose a novel personalized approach for effectively handling unfair ratings of sellers provided to buyer agents from other buyers (called advisors). Our approach offers flexibility for buyers to weight their value for private and public knowledge about advisors. A personalized approach is proposed as well for buyers to model the trustworthiness of sellers, based on the advice provided. Experimental results demonstrate that our approach can effectively model trustworthiness for both advisors and sellers, even when there are large numbers of unfair ratings.     

Information markets over trust networks

Information markets are inefficient. Information products have large upfront development costs, yet their duplication costs are negligibly small; and they are experience goods with high costs of marketing and promotion. As a result, either winner-take-all markets are created through large and expensive promotional campaigns, or artificial monopoly power is conferred by the government through copyright protection, or both, to prevent the collapse of these markets from intense price competition and piracy. Such inefficiency creates opportunities to design more efficient markets by utilizing new technologies. Trust networks provide such an opportunity where the network infrastructure acts not only as a distribution system for information products, but also as an advertising and promotion medium, a payment and pricing mechanism, a guarantee and insurance service, and a copyright enforcement and dispute resolution tool. Such a network-centric market place is proposed to remedy many of the shortcomings of mass markets by relying on peer-to-peer distribution, peer-to-peer payments, and peer-to-peer enforcement of trust and integrity. Analytical models are presented to show that such a market place for information goods can scale up to satisfy large markets without expensive promotions and advertising campaigns, create customized products with dynamic pricing, reduce entry costs by eliminating the distinction between buyers and sellers, and eliminate the need for copyright protection.     

Flexible versus committed and specific versus uniform: wholesale price contracting in a supply chain with downstream process innovation

If buyers are asymmetric in terms of their operating costs, researchers and managers broadly agree that the supplier can optimize her/his own profit by offering the more efficient buyer a higher price. In this paper, we develop a game theoretical model to investigate the interaction between one supplier and two asymmetric buyers within a supply chain. We formulate buyers' operating costs as a function of their process innovation levels, which implies that they can reduce the unit operating cost via investments in process innovation in the long run. Our research demonstrates that the uniform wholesale price (UWP) is always preferred over the buyer-specific wholesale price by the supplier because of the effect of innovation stimulation. The optimal timing of pricing is contingent on the level of market demand variance. If two buyers have the same ability to reduce their operating costs via process innovation, the UWP strategy forms a win-win situation to the supplier and two buyers. Our results provide the supplier with suggestions regarding when to adopt the UWP strategy and how to enhance downstream innovation performance within the supply chain.     

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.     

Internet Advertising, Game Theory and Consumer Welfare

The aim of this paper is to consider the implications of Internet advertising, for prospective consumer welfare gains/losses. The implications of imperfect information among buyers are discussed to highlight the role of intermediaries and search agents in enhancing consumer welfare in a world of complex products and uncertainty. The argument demonstrates that despite possessing welfare enhancing potential the retailing of complex products on the Internet increases the scope for price discrimination among sellers. Hence, buyers may pay different prices for the same product/services. The paper shows how agents may restrict this type of market failure if they possess the ability to search all databases. That the Internet intrinsically exhibits welfare enhancing and reducing characteristic a game model is develop to show how entry of new sellers may proceed or be checked by incumbents.     

The role of intermediaries in electronic marketplaces: developing a contingency model

Abstract. Early research in electronic markets seemed to suggest that e‐ commerce transactions would result in decreased costs for buyers and sellers alike, and would therefore ultimately lead to the elimination of intermediaries from electronic value chains. However, a careful analysis of the structure and functions of electronic marketplaces reveals a different picture. Intermediaries provide many value‐adding functions that cannot be easily substituted or ‘internalized’ through direct supplier–buyer dealings, and hence mediating parties may continue to play a significant role in the e‐commerce world. In this paper we provide an analysis of the potential roles of intermediaries in electronic markets and we articulate a number of hypotheses for the future of intermediation in such markets. Three main scenarios are discussed: the disintermediation scenario, in which market dynamics will favour direct buyer–seller transactions; the reintermediation scenario, in which traditional intermediaries will be forced to differentiate themselves and re‐emerge in the electronic marketplace; and the cybermediation scenario, in which wholly new markets for intermediaries will be created. The analysis suggests that the likelihood of each scenario dominating a given market is primarily dependent on the exact functions that intermediaries play in each case. A detailed discussion of such functions is presented in the paper, together with an analysis of likely outcomes in the form of a contingency model for intermediation in electronic markets.     

The agent-based negotiation process for B2C e-commerce

B2C e-commerce is becoming more widespread as more people come to recognize its convenience and its ability to rapidly respond to requests and as more products and services become available. However, many electronic marketplaces, especially in the business-to-consumer, are in essence some kind of search engine where buyers look for the best product in a database of products offered by sellers. Usually, such e-marketplaces do not use agent technology at all although agents could significantly improve the services provided both for the buyers and the sellers. Further, negotiation capabilities are essential for B2C e-commerce systems. In an automated negotiation, intelligent agents engage in broadly similar processes to achieve the same end. In more detail, the agents prepare bids for and evaluate offers on behalf of the parties they represent with the aim of obtaining the maximum benefit for their users. Nevertheless, in the current situation, price is the only criterion by which agents are created. This factor is easy to measure and automate. However, the criteria for advanced transactions need to be elaborated, for example, details of giveback and dividend. In this paper, we present a multiple-attributes negotiation model for B2C e-commerce, which deploys intelligent agents to facilitate autonomous and automatic on-line buying and selling by intelligent agents while quickly responding to consumers. These include a 4-phase model, information collection, search, negotiation, and evaluation. We also apply fuzzy theory and analytical hierarchy process to develop the system interface to facilitate the user inputs. Finally, an example of the notebook purchasing process is illustrated.     

On the Natural Selection of Market Choice

An evolutionary approach to the problem of economic mechanism choice is presented. It demonstrates the power that a single participant has on the choice of a preferred market mechanism. A population of sellers, each with one of two possible economic mechanisms, facing potential buyers, is presented as a test model. It is shown that if two auctions, such as first- and second-price auctions, are to attract an identical number of buyers, although under the model assumptions their expected revenues are identical, sellers using a first-price auction mechanism will be selected for. However, if a second-price auction attracts one additional buyer, then it will be selected for by the evolutionary process. These results are extended to the choice between an arbitrary k- and l-price auctions.