Optimal auctions through deep learning

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30--40 years of intense research, the problem remains unsolved for settings with two or more items. We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions auctions. In this approach, an auction is modeled as a multilayer neural network, with optimal auction design framed as a constrained learning problem that can be addressed with standard machine learning pipelines. Through this approach, it is possible to recover to a high degree of accuracy essentially all known analytically derived solutions for multi-item settings and obtain novel mechanisms for settings in which the optimal mechanism is unknown.

Emerging Methods of Auction Design in Social Networks

In recent years, a new branch of auction models called diffusion auction has extended the traditional auction into social network scenarios. The diffusion auction models the auction as a networked market whose nodes are potential customers and whose edges are the relations between these customers. The diffusion auction mechanism can incentivize buyers to not only submit a truthful bid, but also further invite their surrounding neighbors to participate into the auction. It can convene more participants than traditional auction mechanisms, which leads to better optimizations of different key aspects, such as social welfare, seller’s revenue, amount of redistributed money and so on. The diffusion auctions have recently attracted a discrete interest in the algorithmic game theory and market design communities. This survey summarizes the current progress of diffusion auctions.

Is the local wheat market a ‘market for lemons’? Certifying the supply of individual wheat farmers in Ethiopia

Bulking and mixing of smallholder supply dilutes incentives to supply high quality. We introduce wheat ‘grading and certification shops’ in Ethiopia and use an auction design to gauge willingness-to-pay (WTP) for certification. Bids correlate positively with wheat quality, and ex ante notification of the opportunity of certification improves wheat quality. These findings suggest that local wheat markets resemble a ‘market for lemons’, crippled by asymmetric information. However, aggregate WTP for grading and certification services does not re-coup the sum of fixed, flow and variable costs associated with running a single certification shop.