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Summary—A synthesis process of a multiple description is defined on a lattice of fuzzy sets which differs from the usual one. Connections between these lattices are stated. Mathematical definitions are correlated with concepts of decision and system theory.

A problem of handling fuzzy quantities in a process of knowledge acquisition and deriving an inference mechanism by means of fuzzy relation equations is studied in extensive way. It is clearly pointed out that both of them are closely related and correspond to various types of fuzzy relation equations that are considered. Their relevance to the form of knowledge collected is also indicated. A problem of dimension reduction of a knowledge base is considered as well. Two modes of the use of the knowledge base (goal‐, and data‐driven) are also studied.

In this paper we deal with fuzzy numbers that modelize uncertain quantities present in many fields of applications, such as man‐machine systems. Main attention is paid to inverse operations for fuzzy numbers which allow one to solve equations or systems of equations with fuzzy numbers. The relevance of the method proposed for the determination of parameters of fuzzy models is also stressed.

Develops a general framework for processes of matching fuzzy quantities. Indicates how different hierarchy levels of matching indices are constructed from a relational way of description of the matching process. Making use of max‐min and min‐max fuzzy relation equations, respectively, clarifies how the entire matching proceeds. Moreover formulates an inverse problem. The method provided here enables us to distinguish regions of the universe of discourse in which the quantities are specified, which are considered as fully supporting the given concept (completely matching observed); completely excluded with respect to this concept; and being of a borderline character. As a consequence the results of matching can have a thorough interpretation.

The aim of this paper is to demonstrate that synergy between firms and local institutions may lead to success even in an economic environment which cannot offer competitive advantages to high tech industrial clustering. However, the condition for such a complex result is convergence between cluster‐based regional development policy, the related industry national strategy and the central government preferential policy for less developed areas.

The experiences selected as case studies are the Xi'an National Civil Aerospace Base in Shaanxi Province (China) and CampaniAerospace in Campania region (Italy). As information was gathered both through policy documents (China and Italy) and direct interviews to cluster's stakeholders (Italy), the outcome is, according to the criteria suggested by Eisenhardt and Yin, a qualitative research. Comparing economic data provided by the respective countries' Statistical Offices, the authors assessed that Shaanxi Province and the Campania region share a “peripheral” position within their respective national context. Starting from this, the authors analyzed the formal documents which reported the experience of the two clusters.

The finding is that the outcome (the status of cluster which is assumed as “a value in itself”) of the convergence between firms' will to gain agglomeration advantages and the policymakers' plan to redress interregional economic disparities is definitely not an efficiency‐driven process. In the high tech sectors which are very sensitive to the increasing competition for technology on the international market, the pursuit of efficiency may, on the contrary, be dissipated.

As attested by the comparative literature, aims and performance of industrial clusters differ in accordance with the stage of economic development as well as the institutional and regulatory framework. The paper demonstrates, however, that in the take‐off stage, it is the position occupied by the host region within the national context which determines the behaviour of the actors concerned as well as the outcome of their commitment.