No-Regret Learning in Bilateral Trade via Global Budget Balance

Bilateral trade revolves around the challenge of facilitating transactions between two strategic agents -- a seller and a buyer -- both of whom have a private valuations for the item. We study the online version of the problem, in which at each time step a new seller and buyer arrive. The learner's task is to set a price for each agent, without any knowledge about their valuations. The sequence of sellers and buyers is chosen by an oblivious adversary. In this setting, known negative results rule out the possibility of designing algorithms with sublinear regret when the learner has to guarantee budget balance for each iteration. In this paper, we introduce the notion of global budget balance, which requires the agent to be budget balance only over the entire time horizon. By requiring global budget balance, we provide the first no-regret algorithms for bilateral trade with adversarial inputs under various feedback models. First, we show that in the full-feedback model the learner can guarantee $\tilde{O}(\sqrt{T})$ regret against the best fixed prices in hindsight, which is order-wise optimal. Then, in the case of partial feedback models, we provide an algorithm guaranteeing a $\tilde{O}(T^{3/4})$ regret upper bound with one-bit feedback, which we complement with a nearly-matching lower bound. Finally, we investigate how these results vary when measuring regret using an alternative benchmark.