A Bipolar Pythagorean Neutrosophic Algebraic Lattice Model with Hybrid Entropy Weighting for Credit Risk Evaluation for Agricultural SMEs Supply Chain Finance

Abstract

In agricultural supply chain finance (SCF), small and medium-sized enterprises 
(SMEs) often face severe credit risk due to unstable production, market volatility, and 
limited access to formal financial records. Traditional risk assessment models fail to 
handle uncertainty, contradiction, and bipolarity in real-world data, especially for 
agriculture. To address this gap, we propose a new mathematical model based on bipolar 
Pythagorean neutrosophic theory. This model introduces an algebraic lattice structure to 
represent the complex relationships between risk criteria and integrates a novel hybrid 
entropy weighting method. The framework allows for the evaluation of both positive and 
negative aspects of credit risk using bipolar neutrosophic sets, while preserving 
mathematical rigor through lattice-based operations. Several new formulas for entropy, 
aggregation, and distance are developed to support accurate and flexible assessment. This 
model is especially suitable for agricultural SMEs, where data is often vague, 
contradictory, or incomplete. A numerical case study is provided to show the practical 
performance of the proposed model. 

DOI: 10.5281/zenodo.16734158