This paper is motivated by the need to design a robust market mechanism to benefit farmers (producers of agricultural produce) as well as buyers of agricultural produce (consumers). Our proposal is a volume discount auction with a Farmer Collective (FC) as the selling agent and high volume or retail consumers as buying agents. An FC is a cooperative of farmers coming together to harness the power of aggregation and economies of scale. Our auction mechanism seeks to satisfy fundamental properties such as incentive compatibility and individual rationality, and an extremely relevant property for the agriculture setting, namely, Nash social welfare maximization. Besides satisfying these properties, our proposed auction mechanism also ensures that certain practical business constraints are met. Since an auction satisfying all of these properties exactly is a theoretical impossibility, we invoke the idea of designing deep learning networks that learn such an auction with minimal violation of the desired properties. The proposed auction, which we call VDA-SAP (Volume Discount Auction for Selling Agricultural Produce), is superior in many ways to the classical VCG (Vickrey-Clarke-Groves) mechanism in terms of richness of properties satisfied and further outperforms other baseline auctions as well. We demonstrate our results for a realistic setting of an FC selling perishable vegetables to potential buyers.

翻译：本文的动机源于设计一种稳健的市场机制以惠及农民（农产品生产者）与农产品购买者（消费者）的需求。我们提出一种以农民集体作为销售代理、以大批量或零售消费者作为购买代理的数量折扣拍卖。农民集体是农民组成的合作社，旨在利用聚合效应与规模经济。我们的拍卖机制力求满足激励相容性与个体理性等基本性质，以及一个对农业环境极为相关的性质，即纳什社会福利最大化。除了满足这些性质外，我们提出的拍卖机制还确保符合某些实际业务约束。由于设计一个能完全满足所有这些性质的拍卖在理论上是不可能的，我们引入设计深度学习网络的思想，使其学习一种对期望性质违反程度最小的拍卖。我们提出的拍卖（称为VDA-SAP，即农产品销售数量折扣拍卖）在满足性质的丰富性方面优于经典的VCG机制，并进一步超越其他基线拍卖。我们通过一个农民集体向潜在买家销售易腐蔬菜的现实场景来展示实验结果。