Bidding efficiently in Simultaneous Ascending Auctions with incomplete information using Monte Carlo Tree Search and determinization

TL;DR

This paper introduces an advanced Monte Carlo Tree Search algorithm for spectrum auction bidding, effectively handling incomplete information and key strategic issues, outperforming existing methods in realistic scenarios.

Contribution

It extends a Monte Carlo Tree Search approach to incomplete information settings in simultaneous ascending auctions, addressing multiple strategic challenges and demonstrating superior performance.

Findings

Outperforms state-of-the-art algorithms in expected utility

Achieves higher utility with less risk across various scenarios

Effectively manages strategic issues like exposure and budget constraints

Abstract

For decades, Simultaneous Ascending Auction (SAA) has been the most widely used mechanism for spectrum auctions, and it has recently gained popularity for allocating 5G licenses in many countries. Despite its relatively simple rules, SAA introduces a complex strategic game with an unknown optimal bidding strategy. Given the high stakes involved, with billions of euros sometimes on the line, developing an efficient bidding strategy is of utmost importance. In this work, we extend our previous method, a Simultaneous Move Monte-Carlo Tree Search (SM-MCTS) based algorithm named $SMS^{\alpha}$ to incomplete information framework. For this purpose, we compare three determinization approaches which allow us to rely on complete information SM-MCTS. This algorithm addresses, in incomplete framework, the four key strategic issues of SAA: the exposure problem, the own price effect, budget constraints, and the eligibility management problem. Through extensive numerical experiments on instances of realistic size with an uncertain framework, we show that $SMS^{\alpha}$ largely outperforms state-of-the-art algorithms by achieving higher expected utility while taking less risks, no matter which determinization method is chosen.