关键词: 算法推荐, 数字囤积行为, 社交媒体平台, 演化博弈

Abstract:

[Purpose/Significance] Digital hoarding has emerged as a significant behavioral phenomenon in the digital age, particularly prevalent among social media users who engage in the excessive acquisition and retention of digital content. This behavior is further amplified by algorithmic recommendation systems that continuously personalize content delivery. Although existing research has examined individual psychological factors or platform characteristics using static approaches, it lacks a dynamic perspective to understand the co-evolutionary relationship between platform strategies and user behaviors. This study addresses this research gap by introducing evolutionary game theory as an innovative analytical framework. Theoretically, the significance lies in modeling the dynamic interactions between platforms' algorithmic adjustments and users' hoarding behaviors. This provides new insights into the adaptive mechanisms within socio-technical systems. From a practical standpoint, this research offers valuable implications for promoting healthier digital environments and developing sustainable governance models for platforms that balance commercial objectives with user well-being. [Method/Process] This study employs evolutionary game theory to model the dynamic interactions between social media platforms and boundedly rational users. This method is well-suited for analyzing how strategies co-evolve over time towards stable states. Based on literature from user behavior and platform economics, a game-theoretic model was developed. Numerical simulations in MATLAB analyzed evolutionary paths across four platform types (Instant Messaging, Public, Short Video, and Vertical Community), with the model calibrated against empirical typologies to investigate how key factors influence long-term outcomes. [Results/Conclusions] The simulation results reveal that the evolutionary path of the platform-user interaction system is highly sensitive to key parameters, ultimately converging to different evolutionarily stable strategies (ESS) under varying conditions. A principal finding is that a unilateral increase in algorithmic recommendation intensity by platforms, while potentially boosting short-term engagement, does not guarantee long-term benefits and may instead drive users towards non-hoarding strategies due to increased cognitive burden. Crucially, the reasonable regulation of recommendation intensity is identified as the key to achieving sustainable, positive interactions. Moderate algorithmic recommendations can effectively alleviate information overload, reduce the negative impacts of hoarding, enhance user experience and satisfaction, and ultimately increase long-term platform benefits, creating a win-win scenario. The study provides significant managerial implications, suggesting that platform operators should incorporate user well-being metrics into algorithm evaluation frameworks, moving beyond purely engagement-driven models. Differentiated governance strategies are recommended for various platform types, such as implementing intelligent filtering on instant messaging apps and content quality incentives on vertical communities. However, this study has limitations, primarily its assumption of user homogeneity, which overlooks the impact of individual differences in preferences and digital literacy. Future research should introduce user heterogeneity, explore multi-platform competition scenarios, and validate the model with empirical data to enhance its practical predictive power and application value.

Key words: algorithm recommendation, digital hoarding behavior, social media platforms, evolutionary game