An Artificial Intelligence Framework for Conflict Mapping and Resolution for Sustainability of Systems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908707223240704 |
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| author | Chakrabarti, Apala |
| author_facet | Chakrabarti, Apala |
| contents | Early design decisions strongly influence environmental, economic and social outcomes, yet sustainability assessment tools rarely reveal trade-offs among these three pillars. This study presents a framework for Conflict Mapping and Resolution for Sustainability of Systems (CONFARM). CONFARM consists of four steps: lifecycle documentation, cause-effect mapping, conflict database construction and multi-criteria scoring. A conflict is recorded when a single decision produces positive and negative effects across pillars. Each effect is evaluated using impact magnitude and pillar weight to generate a sustainability ratio. CONFARM may be applied manually or through automated extraction using natural-language processing and large language models. The method is demonstrated in three sectors representing different data structures and system scales: agriculture (rice and corn), fashion (slow and fast fashion) and energy (nuclear and natural gas). Each system was analysed at increasing conflict densities. Results consistently showed that sustainability scores converged as more conflicts were mapped, indicating stable evaluation across methods. Slow fashion and nuclear systems exhibited relatively higher sustainability performance, while fast fashion and natural gas systems showed lower performance. CONFARM improves early-stage decision support by making trade-offs explicit and enabling comparative evaluation. It offers a structured approach for cleaner production and scalable sustainability assessment across domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Artificial Intelligence Framework for Conflict Mapping and Resolution for Sustainability of Systems Chakrabarti, Apala Physics and Society Early design decisions strongly influence environmental, economic and social outcomes, yet sustainability assessment tools rarely reveal trade-offs among these three pillars. This study presents a framework for Conflict Mapping and Resolution for Sustainability of Systems (CONFARM). CONFARM consists of four steps: lifecycle documentation, cause-effect mapping, conflict database construction and multi-criteria scoring. A conflict is recorded when a single decision produces positive and negative effects across pillars. Each effect is evaluated using impact magnitude and pillar weight to generate a sustainability ratio. CONFARM may be applied manually or through automated extraction using natural-language processing and large language models. The method is demonstrated in three sectors representing different data structures and system scales: agriculture (rice and corn), fashion (slow and fast fashion) and energy (nuclear and natural gas). Each system was analysed at increasing conflict densities. Results consistently showed that sustainability scores converged as more conflicts were mapped, indicating stable evaluation across methods. Slow fashion and nuclear systems exhibited relatively higher sustainability performance, while fast fashion and natural gas systems showed lower performance. CONFARM improves early-stage decision support by making trade-offs explicit and enabling comparative evaluation. It offers a structured approach for cleaner production and scalable sustainability assessment across domains. |
| title | An Artificial Intelligence Framework for Conflict Mapping and Resolution for Sustainability of Systems |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2512.11414 |