Shiyu Lei | Searcharxiv

arXiv2026

"Dragon Slayer Becomes the Dragon": How Players Perceive and Respond to Inequality in the Game World of Whiteout Survival

Inequality in real-world societies are associated with psychological distress and behavioral consequences. However, less is known about whether similar dynamics emerge when inequality exists within virtual environments or make-belief worlds. As online games increasingly constitute meaningful social spaces, it becomes critical to examine how players perceive and react to structural and resource differences online to optimize their experiences. This study studies perceptions of inequality in the online simulation game "Whiteout Survival," using semi-structured interviews and think-aloud gameplay walkthrough protocols. By focusing on players' interpretations of resource distribution, ranking systems, gaming mechanisms, and in-game social dynamics, our analyses revealed that players' attitudes on inequality vary according to their relative status: those occupying lower positions often criticize unfair structures, yet as they acquire stakes through resource accumulation or social integration, many defend the same systems they previously opposed. These shifts reveal how hierarchies reproduce position-dependent evaluations of fairness. The consequences of inequality on player actions depended on the transparency of game mechanisms, the structure of community hierarchies, and differential social capital. This work shows how human social perception and consequent actions are transformed when enacted in virtual processes in make-belief.

cs.HC↗

arXiv2020

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4.0 benefits. In this paper, we consider a new architecture of digital twin empowered Industrial IoT where digital twins capture the characteristics of industrial devices to assist federated learning. Noticing that digital twins may bring estimation deviations from the actual value of device state, a trusted based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning, to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of Industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.

cs.LG↗