用于检测投标操纵的深度学习：基于卷积神经网络标记卡特尔参与者
Deep learning for detecting bid rigging: Flagging cartel participants based on convolutional neural networks
摘要：
除了关于数据驱动的投标操纵卡特尔检测的文献之外，我们提出了一种基于深度学习（人工智能的一个子领域）的新方法，该方法根据卡特尔参与者与其他公司的成对投标互动来标记卡特尔参与者。更简洁地说，我们结合了一个所谓的卷积神经网络的图像识别与图形，以成对的方式绘制的归一化投标值的一些参考公司对任何其他公司的归一化投标参与相同的投标作为参考公司。基于日本和瑞士的采购数据，我们构建了这样的图的串通和竞争的情节（即当一个投标操纵卡特尔是或不活跃），并使用一个子集的图训练的神经网络，使它学会区分串通投标模式。我们使用剩余的图来测试神经网络在正确分类串通和竞争性投标交互方面的样本外性能。当在日本，瑞士或混合数据（其中瑞士和日本图形合并）中应用该方法时，我们获得了大约90%或略高的平均准确度。当使用来自一个国家的数据进行训练以测试训练模型在另一个国家（即跨国）的性能时，预测性能下降（可能是由于各国采购程序的制度差异），但通常保持令人满意的高水平。总而言之，卷积神经网络的准确性通常相当高，尽管它是在一个相当小的样本中训练的，只有100个图，这表明深度学习方法在标记和打击操纵投标卡特尔方面具有很大的潜力。
英文摘要：
Adding to the literature on the data-driven detection of bid-rigging cartels, we propose a novel approach based on deep learning (a subfield of artificial intelligence) that flags cartel participants based on their pairwise bidding interactions with other firms. More concisely, we combine a so-called convolutional neural network for image recognition with graphs that in a pairwise manner plot the normalized bid values of some reference firm against the normalized bids of any other firms participating in the same tenders as the reference firm. Based on Japanese and Swiss procurement data, we construct such graphs for both collusive and competitive episodes (i.e when a bid-rigging cartel is or is not active) and use a subset of graphs to train the neural network such that it learns distinguishing collusive from competitive bidding patterns. We use the remaining graphs to test the neural network's out-of-sample performance in correctly classifying collusive and competitive bidding interactions. We obtain a very decent average accuracy of around 90% or slightly higher when either applying the method within Japanese, Swiss, or mixed data (in which Swiss and Japanese graphs are pooled). When using data from one country for training to test the trained model's performance in the other country (i.e. transnationally), predictive performance decreases (likely due to institutional differences in procurement procedures across countries), but often remains satisfactorily high. All in all, the generally quite high accuracy of the convolutional neural network despite being trained in a rather small sample of a few 100 graphs points to a large potential of deep learning approaches for flagging and fighting bid-rigging cartels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
