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 welfare. To circumvent this limitation, we introduce Backward Rewarding Mechanisms (BRMs) and show that the competition games resulting from BRM possess a potential game structure, which naturally induces the strategic creators' behavior dynamics to optimize any given welfare metric. In addition, the class of BRM can be parameterized so that it allows 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，从而影响其生产选择，进而影响内容分发和系统福利。因此，设计平台的奖励机制以长期引导创作者竞争走向理想的福利结果至关重要。本研究就此做出两项主要贡献：首先，我们揭示了一类广泛采用的机制（即基于绩效的单调机制）的根本局限，证明它们不可避免会导致福利的持续比例损失。为规避这一局限，我们引入了反向奖励机制（BRMs），并表明由BRM产生的竞争博弈具有势博弈结构，这自然驱动策略性创作者的行为动力学优化任意给定的福利指标。此外，BRM类机制可通过参数化实现，使得即使在福利指标未明确界定的情况下，平台也能在可行机制空间内直接优化福利。