论文标题
实施层次深度学习方法来模拟多层次拍卖数据
Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data
论文作者
论文摘要
我们提出了一种深度学习解决方案，以解决模拟现实的合成第一名密封拍卖数据的挑战。在这种类型的拍卖数据中遇到的复杂性包括高心电图离散特征空间和由与单个拍卖实例相关的多个出价引起的多级结构。我们的方法将深层生成建模（DGM）与人工学习者结合在一起，该学习者根据拍卖特征预测有条件的出价分布，这有助于基于模拟的研究的进步。这种方法为创建适合基于代理的学习和建模应用程序的现实拍卖环境奠定了基础。我们的贡献是双重的：我们引入了一种综合方法来模拟多级离散拍卖数据，并强调了DGM作为提炼模拟技术的强大工具的潜力，并促进了以生成AI为基础的经济模型的发展。
We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI.
