实证研究 • Empirical Research封面文章

志愿服务计算社会学：理论、模型与治理

Computational Sociology of Volunteering: A Cross-Level, Multi-Model Framework Integrating Artificial Intelligence

作者：张网成^1,^*，余卓尔²

¹北京师范大学社会学院北京

²北京师范大学社会学院社会工作与志愿服务研究中心北京

^*通信作者：张网成；单位：北京师范大学社会学院社会工作与志愿服务研究中心北京

提交：2025-10-15录用：2025-12-15出版：2025-12-30

社会工作与志愿服务, 2025, 2(2), 70-97; https://doi.org/10.58244/jswv.263981

课题资助：自筹经费，无利益冲突需要说明

摘要：

摘要：传统志愿服务社会学依赖小样本调查与静态分析，难以应对大规模志愿行为的动态性、网络化与跨尺度复杂性。本研究提出“志愿服务计算社会学”框架，融合多源数据表示学习、图神经网络（GCN/GAT）、时序模型（LSTM/Transformer）、生存分析、多智能体仿真与因果推断，构建覆盖微观个体行为、中观组织网络与宏观系统生态的多尺度建模体系。通过可解释人工智能方法（注意力机制【更多...】

关键词：志愿服务社会学；计算社会科学；图神经网络；可解释人工智能；政策仿真

摘要

摘要：

传统志愿服务社会学依赖小样本调查与静态分析，难以应对大规模志愿行为的动态性、网络化与跨尺度复杂性。本研究提出 “志愿服务计算社会学” 框架，融合多源数据表示学习、图神经网络 (GCN/GAT)、时序模型 (LSTM/Transformer)、生存分析、多智能体仿真与因果推断，构建覆盖微观个体行为、中观组织网络与宏观系统生态的多尺度建模体系。通过可解释人工智能方法 (注意力机制、节点扰动、反事实干预) 及伦理治理机制 (公平性约束、差分隐私、审计制度)，该框架将社会资本、参与轨迹、组织协作等核心社会学概念转化为可计算变量，并支持政策干预的仿真评估。文中三个实验 (参与持续性预测、组织网络结构洞分析、政策干预多智能体仿真) 目前为理论设想，将在后续研究中基于真实志愿平台数据开展实证验证。本研究为公益领域的计算社会科学提供了可迁移的方法论路径。

关键词：志愿服务社会学；计算社会科学；图神经网络；可解释人工智能；政策仿真

Abstract:

Traditional sociology of volunteering relies on small-sample surveys and static analyses, which struggle to capture the large-scale dynamics, network complexity, and cross-scale nature of volunteering behavior. This study proposes a framework of “computational sociology of volunteering” that integrates multi-source data representation learning, graph neural networks (GCN/GAT), sequential models (LSTM/Transformer), survival analysis, multi-agent simulation, and causal inference. It builds a multi-scale modeling system covering micro-level individual behavior, meso-level organizational networks, and macro-level system ecology. Through explainable artificial intelligence methods (attention mechanisms, node perturbation, counterfactual interventions) and ethical governance mechanisms (fairness constraints, differential privacy, auditing systems), the framework translates core sociological concepts—such as social capital, participation trajectories, and organizational collaboration—into computable variables and supports simulation-based evaluation of policy interventions. The three experiments described in the paper (prediction of participation persistence, structural hole analysis of organizational networks, and multi-agent simulation of policy interventions) are currently theoretical designs and will be empirically validated in future research using real-world volunteering platform data. This study provides a transferable methodological pathway for computational social science in the public service domain.

Keywords: Sociology of volunteering; Computational social science; Graph neural networks; Explainable artificial intelligence; Policy simulation

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作者

作者 / Authors：

张网成， 北京师范大学社会学院，教授。

余卓尔， 北京师范大学社会工作与志愿服务研究中心，助研。

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