Replication Code and Data for: Towards Sustainable Decision-Making in Multi-Criteria Building Design: A Game-Theoretic Approach

This dataset contains the replication code and Pareto-optimal solution data for the paper "Towards Sustainable Decision-Making in Multi-Criteria Building Design: A Game-Theoretic Approach". The code implements a Nash Bargaining Solution (NBS) for two-player cooperative game-theoretic selection from a pre-computed set of Pareto-optimal building design parameters. Two decision makers — a policy maker (focused on energy and emissions reduction) and a building developer (focused on energy, occupant comfort, and constraint slack) — negotiate over the non-dominated set identified via NSGA-II constrained multi-objective optimization. Preference weights are derived using Multiplicative Preference Relations and Saaty's Magnitude Scale. Two scenarios are provided: equal preferences and comfort-focused preferences. Users can define custom scenarios by modifying criteria rankings and preference intensities. Files included: (1) resGame_NBS.py — Nash Bargaining analysis script, (2) MO_results.mat — Pareto-optimal objective and constraint values from NSGA-II, (3) README.md — usage instructions本数据集包含论文《面向多准则建筑设计的可持续决策：一种博弈论方法》（Towards Sustainable Decision-Making in Multi-Criteria Building Design: A Game-Theoretic Approach）的复现代码与帕累托最优解数据（Pareto-optimal solution data）。 该代码实现了双人合作博弈论选择的纳什议价解（Nash Bargaining Solution, NBS），用于从预先计算得到的帕累托最优建筑设计参数集中选取最优方案。两位决策者分别为政策制定者（聚焦能源与减排）与建筑开发商（聚焦能源、使用者舒适度与约束裕度），二者将就通过NSGA-II约束多目标优化所识别出的非支配解集进行协商。 偏好权重通过乘法偏好关系（Multiplicative Preference Relations）与萨蒂量级量表（Saaty's Magnitude Scale）推导得出。本次数据集提供两种预设场景：等权重偏好场景与聚焦舒适度的偏好场景。用户可通过修改准则排序与偏好强度来自定义专属场景。 包含的文件如下：(1) resGame_NBS.py — 纳什议价分析脚本；(2) MO_results.mat — 来自NSGA-II的帕累托最优目标与约束值数据；(3) README.md — 使用说明文档