Multi-agent systems of large language models as weight assigners: An approach to collaborative weighting in spatial multi-criteria decision-making
Bibliographic record
Abstract
The integration of artificial intelligence (AI) technologies in decision-making processes is gaining momentum. Specifically, Large Language Models (LLMs) and multi-agent systems (MAS) hold considerable potential for transforming the landscape of multi-criteria decision-making (MCDM), particularly in addressing challenges posed by complex, multifaceted group decision-making environments. Conventionally, the collaborative expert weighting approach has been instrumental in spatial MCDM to ensure the accuracy and robustness of decisions. However, this approach is often subject to biases, significant time consumption, and logistical challenges in expert aggregation. This paper explores the feasibility of employing MAS and LLMs as substitutes for group expert-based weighting mechanisms in spatial MCDM by introducing the Weight Assignment by LLM-based MAS (WALMAS) method. In this method, LLMs such as OpenAI GPT-4o, Google Gemini, and Microsoft Copilot were regarded as primary agents, with multiple decision-making agents such as environment, urban planning, geography, and social specialists considered as a substitute for human experts depending on the nature of the problem. In the MAS space, following the parsing and extraction of initial weights from LLMs, a two-level algorithm was developed. The first level of this method involved the removal of outlier weights using the interquartile range (IQR) method. The second level of the method involved gradual negotiation and reaching consensus in an iterative process based on Kendall's W index. The proposed method, grounded in a GeoAI framework, was evaluated through its application to the landfill site selection problem. The findings and sensitivity analysis demonstrated that this method facilitates the efficient and reliable weighting of criteria, while ensuring the convergence of weights. Additionally, an analysis was conducted to identify the similarities and differences between LLMs in terms of weighting, as well as to determine the most effective expert agents in weighting. The analysis of human experts' satisfaction with the proposed method was also evaluated as very promising. This research demonstrates the effectiveness of AI-based tools in enhancing decision-making efficiency, consistency, and adaptability across spatial planning contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".