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For decades, Simultaneous Ascending Auction (SAA) has been the most popularmechanism used for spectrum auctions. It has recently been employed by manycountries for the allocation of 5G licences. Although SAA presents relativelysimple rules, it induces a complex strategical game for which the optimalbidding strategy is unknown. Considering the fact that sometimes billions ofeuros are at stake in a SAA, establishing an efficient bidding strategy iscrucial. In this work, we model the auction as a $n$-player simultaneous movegame with complete information and propose the first efficient biddingalgorithm that tackles simultaneously its four main strategical issues: the$\textit{exposure problem}$, the $\textit{own price effect}$, $\textit{budgetconstraints}$ 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. Byintroducing scalarised rewards in $SMS^\alpha$, we give the possibility tobidders to define their own level of risk-aversion. Through extensive numericalexperiments on instances of realistic size, we show that $SMS^\alpha$ largelyoutperforms state-of-the-art algorithms, notably by achieving higher expectedutility while taking less risks.

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