利用游戏化实验管控复杂性：以谢林隔离模型（Schelling Model of Segregation）为例
Using Gamified Experiments to Tame Complexity: the case of the Schelling Model of Segregation
AI总结：
本研究通过游戏化实验探究并完善谢林隔离模型，结合贪心优化与可调参数平衡策略目标，用近似贝叶斯计算分析玩家行为，为复杂社会现象研究和微观-宏观模型优化提供了新路径。
摘要：
本研究采用游戏化实验来探究并完善谢林隔离模型（Schelling Model of Segregation）——该框架可展示个体偏好如何引发系统性隔离。研究使用源自经典谢林模型桌游改编版的移动选择算法，在受控环境中考察旨在最小化隔离程度、最大化幸福感的玩家策略。该模型以贪心优化为基础，通过一个可调参数平衡上述两类目标。研究借助近似贝叶斯计算（Approximate Bayesian Computation）分析游戏玩法产生的实证数据，揭示玩家策略及其与系统性结果的匹配情况。研究结果凸显了游戏化作为工具的潜力：可用于研究复杂社会现象、改进基于智能体的模型，以及在涌现行为研究中推广参与式方法。这一双层框架将集体决策纳入微观-宏观模型，回应了关于模型过度简化的批评，并拓展了其在教育与政策场景中的应用价值。
英文摘要：
This study employs gamified experiments to investigate and refine the Schelling Model of Segregation, a framework that demonstrates how individual preferences can lead to systemic segregation. Using a movement selection algorithm derived from a board game adaptation of the classical Schelling Model, the research examines player strategies aimed at minimizing segregation and maximizing happiness within a controlled environment. Rooted in greedy optimization, the model balances these objectives through a tunable parameter. Empirical data from gameplay is analyzed using Approximate Bayesian Computation, providing insights into player strategies and their alignment with systemic outcomes. The findings highlight the potential of gamification as a tool for engaging with complex social phenomena, enhancing agent-based models, and fostering participatory approaches in the study of emergent behaviors. This dual-layered framework incorporates collective decision-making into micro-macro models, addressing critiques of oversimplification and expanding their utility in educational and policy contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
