The paper is devoted to fuzzy cognitive modeling, which is an effective tool for studying semi-structured socio-economic systems. The emphasis is onthe process of developing (identification) fuzzy cognitive models, which are the most complex and critical stage of cognitive modeling.
      Existing identification methods are classified as either expert or statistical, depending on the source of information used.
      Typically, when constructing fuzzy cognitive models of semi-structured systems, the system under consideration possesses both quantitative (measurable) factors and factors of a relative, qualitative nature. While statistical data on the quantitative factors may be available, the only available source of information on the qualitative factors is expert knowledge. However, each of the existing identification approaches focuses on just one source type, either expert or statistical.
      Thus, it is crucial to develop a more general approach to the development of fuzzy cognitive models for semi-structured systems to ensure reliable and consistent results by coordinated processing of information of both expert and statistical origins. We developed such an approach based on several identification methods with the subsequent coordination of intermediate results.
      To demonstrate the proposed approach, we applied it to a management problem of integrated development of rural areas. The fuzzy cognitive model we obtained can be used to predict the state of rural areas depending on initial trends and managerial actions, as well as to search and analyze effective managerial strategies for their development.