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The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Published: 13 July 2022 Publication History

Abstract

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 that incentives are misaligned: 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. This is a kind of revealed-preference assumption that is ubiquitous in the way user models are built. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. The behavioral economics and psychology literatures suggest, for example, that we can choose mindlessly or that we can be too myopic in our choices, behaviors that feel entirely familiar on online platforms.
In this work, we develop a model of media consumption where users have inconsistent preferences. We consider an altruistic platform which simply wants to maximize user utility, but only observes behavioral data in the form of the user's 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. These phenomena include users who have long sessions on a platform but derive very little utility from it, and platform changes that steadily raise user engagement before abruptly causing users to go "cold turkey'' and quit. 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 on the same manifold, increasing engagement makes users happier. We provide a characterization of 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.
A full version of this paper can be found at https://arxiv.org/pdf/2202.11776.pdf.

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  1. The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

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      cover image ACM Conferences
      EC '22: Proceedings of the 23rd ACM Conference on Economics and Computation
      July 2022
      1269 pages
      ISBN:9781450391504
      DOI:10.1145/3490486
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 13 July 2022

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      1. engagement optimization
      2. inconsistent preferences
      3. online platforms

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      • (2024)Efficient interactive maximization of BP and weakly submodular objectivesProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702804(2670-2699)Online publication date: 15-Jul-2024
      • (2024)Impact of decentralized learning on player utilities in stackelberg gamesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692518(11253-11310)Online publication date: 21-Jul-2024
      • (2024)Beyond Collaborative Filtering: A Relook at Task Formulation in Recommender SystemsACM SIGWEB Newsletter10.1145/3663752.36637562024:Spring(1-11)Online publication date: 18-Jun-2024
      • (2024)Embedding Democratic Values into Social Media AIs via Societal Objective FunctionsProceedings of the ACM on Human-Computer Interaction10.1145/36410028:CSCW1(1-36)Online publication date: 26-Apr-2024
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      • (2023)The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict BehaviorPerspectives on Psychological Science10.1177/17456916231212138Online publication date: 12-Dec-2023
      • (2023)Take a Fresh Look at Recommender Systems from an Evaluation StandpointProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591931(2629-2638)Online publication date: 19-Jul-2023
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