# Enhancing Multi-Criteria Decision Analysis with AI: Integrating Analytic Hierarchy Process and GPT-4 for Automated Decision Support

arXiv: https://arxiv.org/abs/2402.07404 · HTML: https://mlsanity.com/paper/2402.07404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variation and Hyperparameters](https://mlsanity.com/paper/2312.17476/) (2023)

## Built on by (most cited first, 2 of 2)

- [AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses](https://mlsanity.com/paper/2410.01246/) (2024)
- [Doc2AHP: Inferring Structured Multi-Criteria Decision Models via Semantic Trees with LLMs](https://mlsanity.com/paper/2601.16479/) (2026)

## Abstract (arXiv)

Our study presents a new framework that incorporates the Analytic Hierarchy Process (AHP) and Generative Pre-trained Transformer 4 (GPT-4) large language model (LLM), bringing novel approaches to cybersecurity Multiple-criteria Decision Making (MCDA). By utilizing the capabilities of GPT-4 autonomous agents as virtual experts, we automate the decision-making process, enhancing both efficiency and reliability. This new approach focuses on leveraging LLMs for sophisticated decision analysis, highlighting the synergy between traditional decision-making models and cutting-edge AI technologies. Our innovative methodology demonstrates significant advancements in using AI-driven agents for complex decision-making scenarios, highlighting the importance of AI in strategic cybersecurity applications. The findings reveal the transformative potential of combining AHP and LLMs, establishing a new paradigm for intelligent decision support systems in cybersecurity and beyond.