在雙邊平台上進行線上 A/B 測試具有挑戰，因為實驗政策可能透過共享供給、地理鄰近性與平台配對機制，在使用者之間產生外溢效果，在叫車服務中，採用固定流量分配的標準實驗通常依賴獨立性與平穩性假設，上述外溢效果可能使這兩項假設不再成立，針對存在干擾的叫車平台定價實驗，本研究提出一套自適應實驗架構，首先在空間網格層級分配定價策略，以降低個體隨機分派所造成的污染，接著透過乾淨曝光條件與 HT-IX 估計器，將乘客回饋轉換為經干擾調整後的手臂層級回饋，為因應非平穩的市場環境，本架構透過 Hedge 混合機制結合 ThompsonSampling 與 EXP3，使平台能在穩定市場中的學習效率與波動環境下的穩健性之間取得平衡。以曼哈頓道路網路與歷史需求資料進行的數值實驗顯示，相較於單獨使用 Thompson Sampling 或 EXP3，此整合方法能提升穩定性、減少過早收斂，並達成較低的累積後悔值。

Online A/B testing on two-sided platforms is challenging because experimental interventions may create spillovers across users through shared supply, geographic proximity, and platform matching. In ride-hailing services, these effects can violate the independence and stationarity assumptions underlying standard experiments with fixed traffic allocation. This thesis develops an adaptive experimentation framework for pricing experiments in ride-hailing platforms under interference. The framework first assigns pricing policies at the spatial grid level to reduce contamination from individual randomization. It then uses a clean exposure condition and an HT-IX estimator to transform rider feedback into arm-level feedback that is adjusted for interference. To address nonstationary market conditions, the framework combines Thompson Sampling and EXP3 through a Hedge mixing mechanism, allowing the platform to balance efficient learning in stable markets with robustness in volatile environments. Numerical experiments based on the Manhattan road network and historical demand data show that the integrated approach improves stability, reduces premature convergence, and achieves lower cumulative regret than using Thompson Sampling or EXP3 alone.

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