The emergence of Consumer-to-Consumer (C2C) platforms has allowed consumers to buy and sell goods directly, but it has also created problems, such as commodity fraud and fake reviews. Trust Management Algorithms (TMAs) are expected to be a countermeasure to detect fraudulent users. However, it is unknown whether TMAs are as effective as reported as they are designed for Peer-to-Peer (P2P) communications between devices on a network. Here we examine the applicability of `EigenTrust', a representative TMA, for the use case of C2C services using an agent-based model. First, we defined the transaction process in C2C services, assumed six types of fraudulent transactions, and then analysed the dynamics of EigenTrust in C2C systems through simulations. We found that EigenTrust could correctly estimate low trust scores for two types of simple frauds. Furthermore, we found the oscillation of trust scores for two types of advanced frauds, which previous research did not address. This suggests that by detecting such oscillations, EigenTrust may be able to detect some (but not all) advanced frauds. Our study helps increase the trustworthiness of transactions in C2C services and provides insights into further technological development for consumer services.

翻译：消费者对消费者（C2C）平台的出现使得消费者可以直接买卖商品，但也带来了商品欺诈和虚假评论等问题。信任管理算法（TMA）被视为检测欺诈用户的对策。然而，由于TMA最初是为网络设备间的点对点（P2P）通信设计的，尚不清楚其是否如报告所述同样有效。本文采用基于智能体的模型，以代表性TMA——"EigenTrust"为例，研究其在C2C服务中的适用性。首先，我们定义了C2C服务中的交易流程，假设了六种欺诈交易类型，随后通过仿真分析了EigenTrust在C2C系统中的动态行为。研究发现，EigenTrust能够正确评估两种简单欺诈类型的低信任分数。此外，我们还发现了两种高级欺诈类型的信任分数振荡现象，而以往研究未涉及此问题。这表明通过检测此类振荡，EigenTrust或可识别部分（而非全部）高级欺诈行为。本研究有助于提升C2C服务中交易的可信度，并为消费者服务的进一步技术发展提供了洞见。