Brief

Researchers have developed a new framework for analyzing repeated bilateral trade, focusing on fairness rather than solely maximizing profit. This framework introduces a one-parameter family of objectives, the Rawls-to-Nash family, which aggregates seller and buyer gains using nonpositive Hölder means. The study characterizes optimal learning rates and provides bounds for this new statistical structure, which differs from standard gain-from-trade objectives. AI