Prior-free Auctions for Budgeted Agents

TL;DR

This paper develops prior-free auction mechanisms for agents with budgets, providing approximation guarantees for revenue and welfare in environments like position auctions with downward-closed feasibility constraints.

Contribution

It introduces a clinching auction for position environments and extends profit extraction mechanisms to budgeted settings, achieving improved approximation ratios.

Findings

Clinching auction approximates envy-free welfare in position auctions.

A 10.0-approximation for revenue in budgeted environments.

A 7.5-approximation for profit maximization surpassing previous bounds.

Abstract

We consider prior-free auctions for revenue and welfare maximization when agents have a common budget. The abstract environments we consider are ones where there is a downward-closed and symmetric feasibility constraint on the probabilities of service of the agents. These environments include position auctions where slots with decreasing click-through rates are auctioned to advertisers. We generalize and characterize the envy-free benchmark from Hartline and Yan (2011) to settings with budgets and characterize the optimal envy-free outcomes for both welfare and revenue. We give prior-free mechanisms that approximate these benchmarks. A building block in our mechanism is a clinching auction for position auction environments. This auction is a generalization of the multi-unit clinching auction of Dobzinski et al. (2008) and a special case of the polyhedral clinching auction of Goel et al. (2012). For welfare maximization, we show that this clinching auction is a good approximation to the envy-free optimal welfare for position auction environments. For profit maximization, we generalize the random sampling profit extraction auction from Fiat et al. (2002) for digital goods to give a 10.0-approximation to the envy-free optimal revenue in symmetric, downward-closed environments. The profit maximization question is of interest even without budgets and our mechanism is a 7.5-approximation which improving on the 30.4 bound of Ha and Hartline (2012).