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

Published: 19 February 2026| Version 1 | DOI: 10.17632/f6y4wfspbw.1
Contributors:
Mubashir Wani, Faizal Hafiz, Jan Broekaert, Davide La Torre, Akshya Swain

Description

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

Files

Steps to reproduce

1. Install Python 3.8+ with dependencies: numpy, pandas, matplotlib, scipy (pip install numpy pandas matplotlib scipy) 2. Place MO_results.mat and resGame_NBS.py in the same directory 3. Run: python resGame_NBS.py 4. Outputs: NBS_Equal_Prefs.jpg, NBS_Comfort_Focused_Pref.jpg, NBS_Results.csv To test custom scenarios, modify the scenarios list in resGame_NBS.py as described in README.md

Institutions

Categories

Game Theory, Multi-Objective Optimization, Intelligent Building Energy Management System, Multicriteria Optimization

Licence