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