Myopic vs. farsighted behaviors: The impact of platform bias on the matching system stability and platform sustainability

Platforms serve as intermediaries, efficiently connecting demand and supply and providing significant convenience to the public. However, platform bias remains a prevalent issue, often resulting in substantial negative socio-economic consequences, such as elections manipulation on social media and keyword bidding in search engine advertising. To investigate the impact of platform bias on matching systems and platform sustainability, we first construct a biased matchmaking model by introducing a platform bias control factor, allowing us to systematically analyze the effects of varying bias levels. Then, we develop two metrics to evaluate how varying degrees of bias affect matching fairness and platform revenues. The findings indicate that the platform bias seriously undermines matching fairness in a nonlinear manner. A high platform bias, i.e. myopic recommendations, offers significant short-term revenues to the platform, but growth will stagnate in the long run. Conversely, a low platform bias, i.e. farsighted recommendations, leads to slow short-term revenues but grows exponentially over time. Furthermore, we identify a specific bias interval based on the international standard of the Gini coefficient, within which the matching system can maintain relative stability, and the platform can achieve a balance between long- and short-term revenues. To validate our model, we conduct a case study involving five major shopping platforms, supporting our theoretical findings. This research offers new insights into effectively addressing the challenge of platform bias.