Optimizing Blockchain Platform Selection: A Decision-Making Approach Using LLMs, Type-2 Neutrosophic Numbers, CRITIC, and MAIRCA

Abstract

Blockchain (BC) platforms integrated with Generative AI (GenAI) like Large Language 
Models (LLMs) are the basic infrastructure for significant consumer data like Bitcoin and Smart 
Contracts systems, addressing challenges such as protection from anomalous transactions, 
automation, and easier to interact with BC platforms using LLMs. There are a lot of BC platforms 
with different characteristics. The problem of variety and uncertainty of characteristics in evaluating 
and ranking the most appropriate BC platforms integrated with LLMs can be approached as a multicriteria decision-making (MCDM) problem. In this research paper, we used a hybrid T2NN-CRITICMAIRCA MCDM approach that combines Type-2 Neutrosophic Number (T2NN) to handle uncertain 
information with The Criteria Importance Through Intercriteria Correlation (CRITIC) to assign 
objective weights to criteria and The Multi Attributive Ideal-Real Comparative Analysis (MAIRCA) 
to evaluate and rank ten BC platforms integrated with LLMs from different BC platform types (like 
public, private, and hybrid BC platforms) against three key dimensions: technological, 
organizational, and environmental. The results indicate that Stellar (BC5), Continuous Klaytn (BC8), 
Openchain (BC4), and Hyperledger Fabric (BC2) are the top-ranked alternatives BC platforms 
integrated with LLMs. These findings are significant. Helping business sectors that use Bicton, smart 
contracts, or other BC applications as a comprehensive tool to evaluate and rank optimal BC 
platforms integrated with LLMs. It contributes in increasing security, automation, and easier 
interaction with BC platforms. 

DOI: 10.5281/zenodo.15127966