Most online platforms strive to learn from interactions with users, and many engage in exploration: making potentially suboptimal choices for the sake of acquiring new information. We study the interplay between exploration and competition: how such platforms balance the exploration for learning and the competition for users. Here users play three distinct roles: they are customers that generate revenue, they are sources of data for learning, and they are self-interested agents which choose among the competing platforms. We consider a stylized duopoly model in which two firms face the same multi-armed bandit problem. Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit problem only if it is chosen. Through a mix of theoretical results and numerical simulations, we study whether and to what extent competition incentivizes the adoption of better bandit algorithms, and whether it leads to welfare increases for users. We find that stark competition induces firms to commit to a "greedy" bandit algorithm that leads to low welfare. However, weakening competition by providing firms with some "free" users incentivizes better exploration strategies and increases welfare. We investigate two channels for weakening the competition: relaxing the rationality of users and giving one firm a first-mover advantage. Our findings are closely related to the "competition vs. innovation" relationship, and elucidate the first-mover advantage in the digital economy.

翻译：大多数在线平台致力于从与用户的交互中学习，其中许多平台会进行探索：为了获取新信息而做出可能次优的选择。本文研究探索与竞争之间的相互作用：此类平台如何平衡学习所需的探索与争夺用户的竞争。在此，用户扮演三种不同角色：他们是产生收入的客户，是学习的数据来源，同时也是在竞争平台间进行选择的利己主体。我们构建了一个程式化的双寡头模型，其中两家公司面临相同的多臂赌博机问题。用户逐一到达并在两家公司间选择，因此每家公司仅在被用户选中时才能在其赌博机问题上取得进展。通过理论结果与数值模拟相结合的方式，我们研究了竞争是否以及在何种程度上激励企业采用更优的赌博机算法，以及竞争是否会导致用户福利提升。研究发现，激烈竞争会促使企业固守导致低福利的"贪婪型"赌博机算法；而通过向企业提供部分"免费"用户来削弱竞争，则会激励更好的探索策略并提高福利。我们考察了两种削弱竞争的途径：放宽用户理性假设以及赋予某家企业先发优势。本研究结论与"竞争与创新"关系密切相关，并阐明了数字经济中的先发优势现象。