# Intelligent System Using Data to Support Decision-Making
## 2 Materials and Methods  
Effective decision-making relies on high-quality information: each option is characterized by a set of attributes and evaluated against specific criteria [28]. In this work, we first derive criterion weights via quantitative pairwise comparisons using [PERSON]'s matrix. We then rank alternatives using two complementary multi-criteria methods: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), which orders options by their distance to an ideal (best) and anti-ideal (worst) solution, producing a full preference list [29; 30], and Borda count, where voters assign points to each alternative based on its rank; the sum of points across all voters determines the final ordering [31]. Together, these approaches provide a robust, transparent framework for multi-criteria decision support. In everyday life, we often face the need to make decisions, drawing on our experience and information from our surroundings. Decision-making is a key component of management, where individuals or groups aim to choose options that best match initial conditions and support goal achievement. The process involves selecting the most suitable alternative from available solutions, based on relevant, high-quality information. To evaluate these options, we assess objects according to specific criteria. Each decision-making scenario can thus be represented by a set of attribute values used in multi-criteria decision-making [28]. Theories of multi-criteria decision-making are based on mathematical modeling [29]. Several multi-criteria decision-making methods exist that share the same principle evaluating multiple solution alternatives to a given problem according to selected criteria and establishing a ranking of these variants. These methods differ in how the weight of each criterion is determined and how the extent to which each alternative meets the chosen criteria is numerically assessed. One such method is multi-criteria evaluation of alternatives. The goal of multi-criteria evaluation is to objectively describe the reality of the selection process using standard procedures, thereby formalizing the decision problem--that is, converting it into a mathematical model of a multi-criteria decision-making situation. The assessment criteria and methods for obtaining quantitative data on the values for the individual decision alternatives are determined. This is characterized by the so-called criteria matrix, see Equation (1), where the rows represent the alternatives, and the columns represent the criteria. \[\begin{array}{c}f_{  
The methods for modeling preferences among criteria are divided into three groups:  
Methods with information on aspirational levels of criteria: the decision-maker (user) is required to provide at least the aspirational levels for the criteria values, i.e., the values that each alternative should at least achieve. Methods with cardinal information about the criteria: the user is required to provide cardinal information regarding the relative importance of the criteria, which can be expressed using a vector of criteria weights. This group of methods is the largest and primarily includes  
* the maximum utility method,
* the method of minimizing the distance to the ideal alternative (Technique for Order Preference by Similarity to Ideal Solution--TOPSIS),
* the method of evaluating alternatives based on a preference relation. For a complete ranking of the alternatives, we use the method of minimizing the distance to the ideal alternative, which requires the user to supply substantial information about the criteria. The most used method is TOPSIS. TOPSIS is a multi-criteria decision-making method that ranks alternatives based on their distance from the ideal (best) and the basal (worst) solutions. It identifies the best option by selecting the alternative closest to the ideal and farthest from the worst. The method requires criterion values for each alternative, and the weights of each criterion.