AHPn

AHPn is an extension of the Analytic Hierarchy Process (AHP) that represents decision criteria and their relationships as a network rather than a strict hierarchical tree. In AHPn, nodes denote criteria, subcriteria, and often interdependencies, while edges encode influence or precedence. The approach aims to capture interdependencies among criteria that a purely hierarchical AHP cannot easily model, with the goal of producing more nuanced, robust priority scores.

Origin and usage: The term AHPn appears in some decision-analytic literature to describe network-based variants of AHP designed for large or interrelated decision problems. There is no universally standardized definition, and implementations vary among authors, sometimes combining elements of AHP with network analysis or optimization techniques.

Methodology: Practitioners identify criteria and potential interdependencies, then construct a network rather than a tree. Local weights are derived from pairwise comparisons, as in AHP, and global scores are synthesized by aggregating influences through the network, which may involve path-based multiplication or solving an optimization problem to satisfy consistency constraints. Consistency diagnostics are adapted to the network context, extending beyond the traditional Saaty consistency ratio where appropriate.

Advantages and limitations: Compared with classical AHP, AHPn can model interdependencies and handle certain complex decision structures more naturally. It can improve realism in problems where criteria influence one another. However, it generally entails greater computational complexity, higher data requirements, and more challenging interpretation, particularly when multiple network pathways affect outcomes.

Applications: AHPn has been explored for project prioritization, supplier selection, policy analysis, and environmental or energy planning, especially where interrelated criteria or systemic effects are important.

See also: Analytic Hierarchy Process, decision analysis, network optimization.