Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is misaligned incentives: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption: To understand what users want, platforms look at what users do. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. In this work, we develop a model of media consumption where users have inconsistent preferences. We consider a platform which simply wants to maximize user utility, but only observes user engagement. We show how our model of users' preference inconsistencies produces phenomena that are familiar from everyday experience, but difficult to capture in traditional user interaction models. A key ingredient in our model is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. Whether improving engagement improves user welfare depends on the direction of movement in the content manifold: for certain directions of change, increasing engagement makes users less happy, while in other directions, increasing engagement makes users happier. We characterize the structure of content manifolds for which increasing engagement fails to increase user utility. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media.

翻译：在线平台拥有海量数据，开展无数实验，并运用工业级算法来优化用户体验。尽管如此，许多用户仍对自己在平台上花费的时间感到后悔。一种可能的解释是激励错位：平台并未以用户幸福感为优化目标。但我们认为问题更为深层，它超越了特定平台的激励机制，根源在于一个错误的基础假设：为了理解用户意图，平台观察用户的所作所为。然而研究表明（个人经验也证实），我们常常在当下做出与真实意愿相悖的选择。在本研究中，我们构建了一个用户偏好不一致的媒体消费模型。我们考虑一个旨在最大化用户效用、却仅能观测用户参与度的平台。我们展示了该偏好不一致模型如何产生日常经验中常见但难以用传统用户交互模型捕捉的现象。模型的关键要素在于平台确定内容展示方式的数学框架：平台在由内容底层特征参数化的大量候选内容（即内容流形）上进行优化。参与度提升能否改善用户福祉，取决于内容流形中的移动方向——在某些变化方向上，提升参与度会降低用户幸福感；而在其他方向上，提升参与度则会增强用户幸福感。我们刻画了提升参与度无法增进用户效用的内容流形结构特征。通过将这些效应与平台设计选择的抽象概念相联系，本模型为探索设计、行为科学与社交媒体之间的相互作用提供了理论框架与术语体系。