# Binary Decision Support Using AHP: A Model for Alternative Analysis
###### Abstract  
Decision-making is a fundamental challenge in science and engineering, mainly when subjective factors influence the process. This paper introduces a decision support model based on the Analytic Hierarchy Process (AHP) that was specifically adapted for binary decisions and we term Binary AHP. The model facilitates structured decision-making when evaluating two opposing alternatives, such as yes/no scenarios. To demonstrate its applicability, we applied the Binary AHP model to a real-world case in the Brazilian public sector, where agencies must determine whether a technological solution qualifies as an Information and Communication Technology (ICT) solution. This classification is crucial since it directly impacts procurement policies and regulatory compliance. Our results show that Binary AHP enhanced the decision consistency, transparency, and reproducibility, and reduced the subjective discrepancies between the evaluators. Additionally, by inverting the priority vectors, the model allowed for a comparative analysis of both decision alternatives, thus offering more profound insights into the classification process. This study highlights the flexibility of AHP-based decision support methodologies and proposes a structured approach to refining binary decision frameworks in complex, multi-criteria environments. AHP method; binary decisions; multi-criteria methodologies +
Footnote †: journal: _Algorithms_  
0000-000-000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000000-000000-000000-000000-0000000-0 fields, including public policy, economics, engineering, and business, to ensure rationality, transparency, and the incorporation of multiple perspectives [4]. Among these methodologies, the Analytic Hierarchy Process (AHP) provides a structured framework that enables decision-makers to compare alternatives based on predefined criteria [5]. Despite its widespread application, the AHP presents a notable limitation: it typically requires the evaluation of multiple alternatives, which complicates its direct use in binary decisions (i.e., yes/no choices) [6]. Many real-world decision-making scenarios, especially in public administration and regulatory frameworks, involve precisely these binary classifications. Researchers actively investigate binary decision models, particularly in economics, the social sciences, and financial markets [7; 8; 9]. [PERSON] [10] explored collective decision-making in ants through a simple binary choice model, and [PERSON] [11] analyzed the fat-tailed distribution in stock markets and its relation to binary decisions. Researchers have proposed several multi-criteria decision-making (MCDM) approaches to address decision challenges in various domains. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) evaluates alternatives by measuring their distances from positive and negative ideal solutions [12]. A recent contribution, the Full Consistency Method (FUCOM), determines the criteria weights by minimizing the deviation from complete consistency in pairwise comparisons. In FUCOM, decision-makers rank criteria by importance and compare only the adjacent criteria, increasing the process's efficiency and consistency [13]. [PERSON] introduced the Best-Worst Method (BWM) in 2015 to streamline criteria weighting by requiring decision-makers to identify the most and least important criteria and then perform pairwise comparisons between these and all other criteria [14; 15]. The Best-Worst Method (BWM) reduces the number of pairwise comparisons and improves the consistency by focusing on the most and least important criteria. However, certain limitations of BWM led to the decision not to adopt it in this study.