An Experimental Evaluation of Large Language Models in Supporting the DEX Multi-Criteria Decision-Making Process

Abstract

We experimentally assessed the capabilities of two mainstream artificial intelligence chatbots, ChatGPT and DeepSeek, to support the multi-criteria decision-making process. Specifically, we focused on using the method DEX (Decision EXpert) and investigated their performance in all stages of DEX model development and utilization. The results indicate that these tools may substantially contribute in the difficult stages of collecting and structuring decision criteria, and collecting data about decision alternatives. However, at the current stage of development, the support for the whole multi-criteria decision-making process is still lacking, mainly due to occasionally inconsistent and erroneous execution of methodological steps. To leverage the strengths of both approaches, we also propose a hybrid workflow for DEX model development that begins in the LLM and continues in the specialized DEXiWin software.

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