## 1 Introduction  
The increasing complexity of control systems, economic and social systems, as well as design problems increases the complexity of related decision problems [1]. They concern, among other things, the decision-making of control of complex systems [2; 3], design issues [4; 5; 6], political systems [7], organization management [8; 9; 10; 11; 12; 13], financial management [14; 15], and marketing [16]. Many decision-making problems are characterized by high dimensionality [17], sources of uncertainty, and risk factors [18]. It is important to reconcile conflicting goals, make decisions based on many criteria, and seek compromise [19]. Policy makers are faced with the complexity of decision-making situations and require methods and systems to support decision making [20]. In response to these needs, many solutions have been developed dedicated to selected areas, as well as general purpose methods [21]. In this context, multicriteria decision analysis (MCDA) methods are widely used. In addition to the formal foundations, these methods are characterized by the ability to deal with many conflicting goals, as well as various stakeholders as part of the decision-making process [22]. The dynamic development of MCDA methods has been observed in recent years [23; 24; 25] as evidenced by scientific publications appearing in magazines on this subject. However, they differ significantly in many dimensions, such as complexity, way of representing preferences and assessment criteria, type of data aggregation, the possibility of taking into account uncertain data, and the availability of implementation in decision support systems or criteria compensation [26; 27; 28]. The huge number of possible MCDA methods allow finding a solution to a properly formulated decision situation. Therefore, an important research issue that has not yet been fully resolved is determining the method for selecting the number of criteria to assess situations and selecting the most satisfactory decision. That is why the formulated research problem is so important for the methods of managing industrial, technological, and social systems. This is an especially important issue in the security and control systems of complex objects in dynamic states in conditions of uncertainty and risk. It follows from a very large number of factors influencing the decisions taken and determining the control, nonscalar criterion functions, and difficulties in determining the direct relationship between the components of the criterion function and these factors. In addition, in these types of systems, especially in conflict situations, there are usually strong restrictions on the time allowed to solve the problem. An additional impediment to their implementation for the decision-maker is the stress resulting from high responsibility for the decisions made and usually a strong limitation in terms of the time they require. The above-mentioned conditions imply the legitimacy of making efforts to model the problems in the form of optimization tasks. Hence, it is desirable to provide computer support for activities performed in individual stages of the decision-making process. In the literature on the subject, there are practically no publications on methods of selecting partial criteria, including the selection of numbers and aspects of assessment. Few publications [29; 30; 31] relate primarily to the selection of assessment criteria in the selection process of suppliers in the supply chain. Most often, this problem is solved by experts who arbitrarily define individual partial criteria. This approach may lead to an unnecessary increase in the size of the decision assessment vector or failure to consider relevant criteria. A necessary condition for optimizing decisions is to have a measure of quality assessment [32] formulated to cover all aspects of the problem. For this reason, the main aim of the conducted studies is to formulate a method for selecting the number of criteria to assess the quality of decisions taken. This article contains five sections.