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Journal of library and information science in agriculture

   

Evolutionary Game Study of the Digital Hoarding Behavior of Social Media Users under Algorithm Recommendations

LI Shuqi1, LI Jian2   

  1. 1. Business College of Southwest University, Chongqing 402460
    2. Archives of Southwest University, Chongqing 400715
  • Received:2025-08-30 Online:2025-12-02

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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

CLC Number: 

  • G204

Table 1

Cost-benefit table of evolutionary game between social media platforms and users"

社交媒体平台 用户
F：平台固定收益 P：用户在社交媒体平台获取有益信息的基础收益
N：平台固定成本 Q：用户在社交媒体平台使用时间的基础成本
R：强化算法推荐时的额外收益 E：用户选择囤积行为获取有益信息的潜在收益

K：用户囤积行为造成的平台额外收益（K=θC）

θ：用户额外使用时间带给平台的单位收益

φ：不同强化算法推荐时用户使用时间强化系数

 C：用户选择囤积行为造成的额外使用成本及心理成本

Table 2

Payoff matrix of the platform-user evolutionary game"

博弈主体与决策类型 用户
选择囤积（x） 不选择囤积（1-x）
社交平台 强化算法推荐（y） P-φQ+E-φC，F-N+φR+K/φ P-φQ，F-N+φR
无强化算法推荐（1-y） P-Q+E-C，F-N+K P-Q，F-N

Fig.1

Replicator dynamics phase diagram of the user population"

Fig.2

Replicator dynamics phase diagram of the social media platform"

Fig.3

Replicator dynamics and stability of the platform-user evolutionary system"

Table 3

Partial derivatives of the evolutionarily stable strategy for the replicator dynamics equations at equilibrium points"

均衡点 a 11 a 12 a 21 a 22
(0,0) E-C 0 0 φR
(1,0) -(E-C) 0 0 K/φ+φR-K
(0,1) E-φC 0 0 -φR
(1,1) -(E-φC) 0 0 -(K/φ+φR-K)
(x*,y*) 0 a 12(x0,y0) a 21(x0,y0) 0

Fig.4

Simulation results of instant messaging platforms"

Fig.5

Simulation results of public social media platforms"

Fig.6

Simulation results of vertical social media platforms"

Fig.7

Simulation results of short-video social media platforms"

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