This research addresses the imperfection in archaeological data — ambiguity, partiality, imprecision, and uncertainty — through a fuzzy logic-based framework to measure uncertainty in site chronologies and typologies. The framework integrates expert interpretation and applies fuzzy operators to quantify uncertainty, particularly in dating and typology, using trapezoidal functions for fuzzification. By introducing metrics like CDEG (Comprehensive Degree of Equivalence) and FEQ (Factor of Equivalence), the framework provides a replicable approach to assessing and managing uncertainty in legacy data. Case studies from San Blas and Santa María de Hito demonstrate the model’s adequacy in quantifying uncertainty at various stages of investigation, offering a more flexible classification and analysis of incomplete or uncertain archaeological data. This approach enhances the reliability of archaeological research and improves the management of data by providing clear, measurable metrics for uncertainty in both chronology and typology.
Pulido et al. (Thu,) studied this question.