Using LLMs for Multi-Criteria Decision Analysis: The Swedish Nord-Syd Case

Large language models (LLMs) are increasingly useful as front-end tools for decision support, particularly when extensive textual reports must be read and transformed into a structured analytical form. However, summarisation and decision making are different tasks: summarisation is primarily epistemic compression, whereas choice is normative aggregation. An LLM such as ChatGPT can identify alternatives, criteria, and qualitative assessments in policy documents, but it does not by itself provide a transparent and reproducible method for aggregating conflicting criteria under uncertainty. This paper examines a hybrid architecture in which ChatGPT prepares the decision basis while a multi-criteria decision analysis (MCDA) tool performs the formal evaluation. The approach is examined through the Swedish Nord-Syd infrastructure case. It was first analysed manually in 2011, based on a human panel’s binary and ordinal assessments. In 2026, ChatGPT 5.6 translated the same report evidence into interval-valued assessments using the same 19 terminal criteria, which were supplied as part of the experimental setup, while the panel’s assessments were withheld from the LLM. The panel-derived 2011 model and the LLM-generated 2026 model were analysed using the same −5 to +5 preference scale, criteria hierarchy, equal local weights, and MCDA procedure. The panel-derived assessments implied 31 strict pairwise ordering relations and 26 ties among the three active alternatives. The LLM interval midpoints reproduced the direction of all 31 strict relations; including ties, exact three-state relation agreement was 43/57 (75.4%). In addition, 50 of 57 non-reference interval pairs overlapped, with a mean absolute midpoint difference of 1.12 points. In this case study, the results illustrate how LLM-based extraction and MCDA evaluation can play complementary roles: the former structures complex textual evidence, while the latter provides an explicit and inspectable evaluative framework.