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This work formalizes the interaction between AI systems and users as a game with three players: a defender and an attacker that are computationally or informationally bounded, and an AI that provides public advice and is less constrained.
This work considers the economic setting recently introduced by Bergemann et al. (2022), where in addition to the menu of quality-price pairs, the seller offers information on the value of the match between product quality and buyer's taste via a signaling scheme and gives the first FPTAS to compute a scheme that maxim...
Under a $\sigma$-smoothed adaptive adversary, a tight $O(\sqrt{\sigma T})$ bound on the depth of the data structure is proved, and an $O(\log T)$ bound under a random-order adversary is proved -- to the authors' knowledge, the first such results for this class of problems.
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