Parameterized Analysis of Bribery in Challenge the Champ Tournaments
<|reference_start|>Parameterized Analysis of Bribery in Challenge the Champ Tournaments: Challenge the champ tournaments are one of the simplest forms of competition, where a (initially selected) champ is repeatedly challenged by other players. If a player beats the champ, then that player is considered the new (current) champ. Each player in the competition challenges the current champ once in a fixed order. The champ of the last round is considered the winner of the tournament. We investigate a setting where players can be bribed to lower their winning probability against the initial champ. The goal is to maximize the probability of the initial champ winning the tournament by bribing the other players, while not exceeding a given budget for the bribes. Mattei et al. [Journal of Applied Logic, 2015] showed that the problem can be solved in pseudo-polynomial time, and that it is in XP when parameterized by the number of players.   We show that the problem is weakly NP-hard and W[1]-hard when parameterized by the number of players. On the algorithmic side, we show that the problem is fixed-parameter tractable when parameterized either by the number of different bribe values or the number of different probability values. To this end, we establish several results that are of independent interest. In particular, we show that the product knapsack problem is W[1]-hard when parameterized by the number of items in the knapsack, and that constructive bribery for cup tournaments is W[1]-hard when parameterized by the number of players. Furthermore, we present a novel way of designing mixed integer linear programs, ensuring optimal solutions where all variables are integers.<|reference_end|>
@article{chaudhary2024parameterized,
    title={Parameterized Analysis of Bribery in Challenge the Champ Tournaments},
    author={Juhi Chaudhary and Hendrik Molter and Meirav Zehavi},
    journal={arXiv preprint arXiv:2403.17587},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17587},
    primaryClass={cs.DS cs.AI}
}