Bidding efficiently in Simultaneous Ascending Auctions with budget and eligibility constraints using Simultaneous Move Monte Carlo Tree Search

TL;DR

This paper introduces SMS^α, an innovative Monte Carlo Tree Search-based bidding algorithm for simultaneous ascending auctions that effectively addresses strategic issues like exposure, price effects, budgets, and eligibility, outperforming existing methods.

Contribution

It presents the first efficient bidding algorithm for SAA that simultaneously tackles multiple strategic challenges using SM-MCTS with a novel price prediction method.

Findings

SMS^α outperforms state-of-the-art algorithms in expected utility.

The algorithm reduces risk while maintaining high performance.

It allows bidders to customize risk preferences through a new reward function.

Abstract

For decades, Simultaneous Ascending Auction (SAA) has been the most popular mechanism used for spectrum auctions. It has recently been employed by many countries for the allocation of 5G licences. Although SAA presents relatively simple rules, it induces a complex strategic game for which the optimal bidding strategy is unknown. Considering the fact that sometimes billions of euros are at stake in an SAA, establishing an efficient bidding strategy is crucial. In this work, we model the auction as a $n$-player simultaneous move game with complete information and propose the first efficient bidding algorithm that tackles simultaneously its four main strategic issues: the $\textit{exposure problem}$, the $\textit{own price effect}$, $\textit{budget constraints}$ and the $\textit{eligibility management problem}$. Our solution, called $SMS^\alpha$, is based on Simultaneous Move Monte Carlo Tree Search (SM-MCTS) and relies on a new method for the prediction of closing prices. By introducing a new reward function in $SMS^\alpha$, we give the possibility to bidders to define their own level of risk-aversion. Through extensive numerical experiments on instances of realistic size, we show that $SMS^\alpha$ largely outperforms state-of-the-art algorithms, notably by achieving higher expected utility while taking less risks.