Optimal Online Learning in Bilateral Trade Through Interest
Mengfan Ma, Haoyu Song, Yiran Tong
We consider online learning in bilateral trade on platforms such as Uber, Airbnb, and eBay. In each round, a platform posts a payment to a seller and a price to a buyer, both with private valuations, and observes only whether trade occurs. Its objective is to maximize cumulative gains-from-trade, the sum of the differences between buyers' and sellers' valuations across completed trades. We measure the platform's performance by its regret against the best fixed common price. Financial self-sufficiency motivates global budget balance (Bernasconi et al., 2024), which allows profits from some trades to subsidize others, provided that the final balance is nonnegative. Such financial planning should also account for the time value of money: profits earned earlier can accumulate interest, providing additional funds for future subsidies. Motivated by this observation, we introduce global budget balance with interest, under which a platform starting with no funds can use both trading profits and accrued interest to finance subsidies. For a horizon of $T$ rounds and a fixed exponent $θ\ge0$, we consider the per-round interest rate $r_T = T^{-θ}$. We study two classic valuation models, adversarial and correlated i.i.d., and obtain tight bounds on regret against the best fixed common price in both. As the interest rate grows, optimal regret in each model drops from $\tildeΘ(T^{3/4})$ to $\tildeΘ(T^{5/7})$. The intermediate rates differ: $\tildeΘ(T^{3θ/4})$ for adversarial valuations when $20/21 < θ< 1$, and $\tildeΘ(T^{(2θ+3)/7})$ for correlated i.i.d. valuations when $1 < θ< 9/8$. Finally, as an extension, we study $α$-regret, with approximation factor $α\ge1$, for adversarial valuations against the best feasible distribution over price pairs, and show that the optimal competitive ratio rises from 2 to 3 as interest strengthens.
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