Designing Fair, Cost-optimal Auctions based on Deep Learning for Procuring Agricultural Inputs through Farmer Collectives (2304.07341v1)

Abstract: Procuring agricultural inputs (agri-inputs for short) such as seeds, fertilizers, and pesticides, at desired quality levels and at affordable cost, forms a critical component of agricultural input operations. This is a particularly challenging problem being faced by small and marginal farmers in any emerging economy. Farmer collectives (FCs), which are cooperative societies of farmers, offer an excellent prospect for enabling cost-effective procurement of inputs with assured quality to the farmers. In this paper, our objective is to design sound, explainable mechanisms by which an FC will be able to procure agri-inputs in bulk and distribute the inputs procured to the individual farmers who are members of the FC. In the methodology proposed here, an FC engages qualified suppliers in a competitive, volume discount procurement auction in which the suppliers specify price discounts based on volumes supplied. The desiderata of properties for such an auction include: minimization of the total cost of procurement; incentive compatibility; individual rationality; fairness; and other business constraints. An auction satisfying all these properties is analytically infeasible and a key contribution of this paper is to develop a deep learning based approach to design such an auction. We use two realistic, stylized case studies from chili seeds procurement and a popular pesticide procurement to demonstrate the efficacy of these auctions.