The past decade has witnessed the flourishing of a new profession as media content creators, who rely on revenue streams from online content recommendation platforms. The reward mechanism employed by these platforms creates a competitive environment among creators which affect their production choices and, consequently, content distribution and system welfare. It is thus crucial to design the platform's reward mechanism in order to steer the creators' competition towards a desirable welfare outcome in the long run. This work makes two major contributions in this regard: first, we uncover a fundamental limit about a class of widely adopted mechanisms, coined Merit-based Monotone Mechanisms, by showing that they inevitably lead to a constant fraction loss of the optimal welfare. To circumvent this limitation, we introduce Backward Rewarding Mechanisms (BRMs) and show that the competition game resultant from BRMs possesses a potential game structure. BRMs thus naturally induce strategic creators' collective behaviors towards optimizing the potential function, which can be designed to match any given welfare metric. In addition, the BRM class can be parameterized to allow the platform to directly optimize welfare within the feasible mechanism space even when the welfare metric is not explicitly defined.

翻译：过去十年见证了媒体内容创作者这一新职业的蓬勃发展，他们依赖于在线内容推荐平台的收入流。这些平台采用的奖励机制在创作者之间营造了竞争环境，影响着他们的创作选择，进而影响内容分发和系统福利。因此，设计平台的奖励机制对于长期引导创作者竞争走向理想的福利结果至关重要。本文在这方面做出了两大贡献：首先，我们揭示了一类广泛采用的机制（称为基于绩效的单调机制）的基本局限性，证明它们不可避免地导致最优福利的恒定比例损失。为了规避这一局限性，我们引入了反向奖励机制（BRMs），并证明由BRMs产生的竞争博弈具有潜在博弈结构。因此，BRMs自然促使战略性创作者的集体行为朝着优化潜在函数的方向发展，而该潜在函数可被设计为匹配任何给定的福利指标。此外，BRM类别可通过参数化，使平台即使在福利指标未明确界定时，也能在可行的机制空间内直接优化福利。