:Archaeological looting correlates with a number of problems, including the destruction of stratigraphic data and the damage and loss of artifacts. Looting is also understood to generate revenue, but systematic analysis of this issue is challenged by its opacity: how can we study the economic effects of archaeological looting when the practice is rarely directly observable? To address this problem, we estimate the market value of archaeological sites where artifacts have been previously excavated and documented, using a machine-learning approach. The first step uses 41,587 sales of objects from 33 firms to train an algorithm to predict the distribution channel, lot packaging, and estimated sale price of objects based on their observable characteristics. The second step uses the trained algorithm to estimate the value of sites in which a large number of artifacts have been legally excavated and documented. We make an out-of-sample prediction on two Syrian sites, Tell Bi’a and Dura Europos.

中文翻译：

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古物交易的站点级市场模型

：考古掠夺与许多问题相关，包括地层数据的破坏以及文物的损坏和丢失。掠夺也被理解为产生收入，但对这个问题的系统分析因其不透明性而受到挑战：当这种做法很少直接观察到时，我们如何研究考古掠夺的经济影响？为了解决这个问题，我们使用机器学习方法估计了先前已经挖掘和记录文物的考古遗址的市场价值。第一步使用来自 33 家公司的 41,587 件物品销售来训练一种算法，以根据物品的可观察特征来预测物品的分销渠道、批次包装和估计的销售价格。第二步使用经过训练的算法来估计大量文物已被合法挖掘和记录的地点的价值。我们对两个叙利亚站点 Tell Bi'a 和 Dura Europos 进行了样本外预测。