First-Price Auctions with Reimbursement in NSP Alliances with Asymmetric Buyers

Isabel Amigo, Sandrine Vaton, P. Belzarena
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Abstract

The emergence of Network Service Provider Alliances (NSPs) to provide end-to-end, quality-assured services, promises new business and service opportunities for NSPs and customers respectively. This new marketplace requires a suitable pricing mechanism that allows price fixation, control access and customer trust in the new -and thus without reputation-seller. To work in this scenario, we propose a pricing scheme based on First-price auctions and where a pre-announced percentage of the price paid for the service is reimbursed if the quality is not satisfied. An optimal percentage of reimbursement can be analytically computed in simplified scenarios, where all buyers are equally modelled [1]. In more realistic scenarios, customers and services are asymmetric, e.g. buyers value the service on sale in a different way, and no analytical results can be derived. As a remedy, we propose a simulative approach in order to approximate buyers' willingness to pay, and subsequently compute the optimal percentage of reimbursement. We apply our simulator to different case studies, finding in all of them that the optimal percentage of reimbursement is 100% and this value prevents problems such as the market for lemons.
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非对称买家NSP联盟中带有补偿的首价拍卖
网络服务提供商联盟(nsp)的出现提供端到端的、有质量保证的服务,为nsp和客户各自带来了新的业务和服务机会。这个新的市场需要一个合适的定价机制,允许价格固定,控制访问和客户信任新的(因此没有声誉的)卖家。为了在这个场景中工作,我们提出了一个基于第一价格拍卖的定价方案,如果质量不令人满意,则预先公布为服务支付的价格百分比进行报销。一个最优的补偿百分比可以在简化的情况下解析计算,其中所有的购买者都是平等建模的。在更现实的情况下,客户和服务是不对称的,例如,买家以不同的方式评估销售的服务,无法得出分析结果。作为补救措施,我们提出了一种模拟方法,以近似购买者的支付意愿,并随后计算出最优的补偿百分比。我们将模拟器应用到不同的案例研究中,发现所有案例的最佳报销比例都是100%,这个值可以防止出现柠檬市场等问题。
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