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Reputation based buyer strategies for seller selection in electronic markets.

机译：电子市场中基于信誉的买方策略，用于卖方选择。

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摘要

Reputation based adaptive buying agents that reason about sellers for purchase decisions have been designed for B2C ecommerce markets. Previous research in the area of buyer agent strategies for choosing seller agents in ecommerce markets has focused on frequent purchases. In this thesis, we present reputation based strategies for buyer agents to choose seller agents in a decentralized multi agent based ecommerce markets for frequent as well as infrequent purchases.;We consider a marketplace where the behavior of seller agents and buyer agents can vary, they can enter and leave the market any time, they may be dishonest, and quality of the product can be gauged after actually receiving the product. Buyer agents exchange seller agents' information, which is based on their own experiences, with other buyer agents in the market. However, there is no guarantee that when other buyer agents provide information, they are truthful or share similar opinions.;First we present a method for buyer agent to model a seller agent's reputation. The buyer agent computes a seller agent's reputation based on its ability to meet its expectations of product quality and price as compared to its competitors. We show that a buying agent acting alone, utilizing our model of maintaining seller agents' reputation and buying strategy does better than buying agents acting alone employing strategies proposed previously by other researchers for frequent as well as for infrequent purchases.;Next we present two methods for buyer agents to identify other trustworthy buyer agent friends who are honest and have similar opinions regarding seller agents, based on sharing of seller agents' information with each other. In the first method, buyer agent utilizes other buyer agents' opinions and ratings of seller agents to identify trustworthy buyer agent friends. Reputation of seller agents provided by trustworthy buyer agent friends is adjusted to account for the differences in the rating systems and combined with its own information on seller agents to choose high quality, low priced seller agent. In the second method, buyer agent only utilizes other buyer agents' opinions of seller agents to identify trustworthy buyer agent friends. Ratings are assigned to seller agents by the buyer agent based on trustworthy friend buyer agents' opinions and combined with its own rating on seller agents to choose a high quality, low priced seller agent to purchase from.;We conducted experiments to show that both methods are successful in distinguishing between trustworthy buyer agent friends, whose opinions should be utilized in decision making, and untrustworthy buyer agent friends who are either dishonest, or have different opinions. We also show that buyer agents using our models of identifying trustworthy buyer agent friends have higher performance than a buyer agent acting alone for infrequent purchases and for increasing numbers of sellers in the market.;Finally we analyze the performances of buyer agents with risk taking and conservative attitudes. A buyer agent with risk taking attitude considers a new seller agent as reputable initially and tends to purchase from a new seller agent if they are offering the lowest price among reputable seller agents. A buyer agent with conservative attitude is cautious in its approach and explores new seller agents at a rate proportional to the ratio of unexplored seller agents to the all the seller agents who have sent bids. Our results show that, when buyer agents are making decisions based on their own information, a buyer agent with conservative attitude has the best performance. When buyer agents are utilizing information provided by their trusted friends, a buyer agent with risk taking attitude and using only trusted friend buyer agents' opinions of seller agents has the best performance.;In summary, the main contributions of this dissertation are: (1) A new reputation based way to model seller agents by buyer agents based on direct interactions. (2) A protocol to exchange reputation information about seller agents with other buyer agent friends based on the friends' direct interaction with seller agents. (3) Two methods of identifying trustworthy buyer agent friends who are honest and share similar opinions, and utilizing the information provided by them to maximize a buyer agent's chances of choosing a high quality, low priced seller agent to purchase from.
机译：基于信誉的适应性购买代理（针对卖家做出购买决定的原因）是为B2C电子商务市场设计的。先前在电子商务市场中选择卖方代理的买方代理策略领域的研究集中于频繁购买。在本文中，我们提出了基于声誉的策略，供买方代理商在分散的，基于多代理商的电子商务市场中选择卖方代理商以进行频繁或不频繁的购买;我们考虑一个市场，卖方代理商和买方代理商的行为可以发生变化，可以随时进入和退出市场，这可能是不诚实的，并且可以在实际收到产品后评估产品的质量。买方代理商与市场上的其他买方代理商根据自己的经验交换卖方代理商的信息。但是，不能保证当其他买方代理提供信息时，他们是真实的或有相似的观点。首先，我们提出了一种买方代理建模卖方代理声誉的方法。买方代理商根据与竞争对手相比满足其产品质量和价格预期的能力来计算卖方代理商的声誉。我们证明，使用我们维持卖方代理人声誉和购买策略的模型，单独采取行动的买方代理要比采用其他研究人员先前针对频繁购买和不频繁购买的战略提出的单独采取行动的买方代理要好。供买方代理基于彼此共享卖方代理的信息，找出其他诚实且对卖方代理有相似观点的值得信赖的买方代理朋友。在第一种方法中，买方代理利用其他买方代理的观点和卖方代理的评级来确定可信赖的买方代理朋友。调整可信赖的买方代理商朋友提供的卖方代理商的声誉，以考虑评级系统的差异，并结合其自身的卖方代理商信息来选择高质量，低价的卖方代理商。在第二种方法中，买方代理仅利用其他买方代理对卖方代理的意见来识别可信赖的买方代理朋友。评级是由买方代理商根据可信赖的朋友买方代理商的意见分配给卖方代理商的，并结合其自身对卖方代理商的评级，以选择高质量，低价的卖方代理商进行购买。能够成功地区分应在决策中使用其意见的可信赖的买方代理朋友和不诚实或有不同意见的不可信的买方代理朋友。我们还表明，使用我们的模型来识别可信赖的买方代理朋友的买方代理要比单独购买不频繁购买和增加市场中卖方数量的买方代理具有更高的绩效。;最后，我们分析了承担风险和承担风险的买方代理的绩效保守的态度。具有冒险态度的买方代理最初认为新卖方代理是信誉良好的，并且如果他们在信誉良好的卖方代理中提供最低价格，则倾向于从新卖方代理购买产品。具有保守态度的买方代理在采取谨慎态度时会以与未开发卖方代理与已发送投标的所有卖方代理之比成比例的比率来探索新的卖方代理。我们的结果表明，当买方代理根据自己的信息做出决策时，具有保守态度的买方代理表现最佳。当买方代理利用其可信赖的朋友提供的信息时，具有冒险态度并仅使用可信赖的朋友买方代理对卖方代理的观点的买方代理具有最佳的表现。概括而言，本论文的主要贡献是： ）一种新的基于信誉的方法，可以由买方代理根据直接互动为卖方代理建模。 （2）一种基于好友与卖方代理的直接交互来与其他买方代理朋友交换有关卖方代理的信誉信息的协议。 （3）两种方法可以识别诚实并分享相似观点的可信赖的买方代理商朋友，并利用他们提供的信息来最大化买方代理商选择高质量，低价卖方代理商的机会。

著录项

  • 作者

    Beldona, Sandhya.;

  • 作者单位

    University of Kansas.;

  • 授予单位 University of Kansas.;
  • 学科 Artificial Intelligence.;Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:26

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