Literature on innovation networks reveals little of to what extent different types of knowledge are exchanged and combined by collaborating firms to foster innovation. Based on field research in the aerospace industrial cluster of Rome, this study investigates the exchange of technological, market and managerial knowledge. Using social network analysis, the paper shows that the three types of knowledge are unevenly distributed and exchanged, thus revealing that the process of exchange is knowledge-specific. Further, it is found that in most collaborative relationships, partners exchange technological knowledge together with market and managerial knowledge, emphasizing the complex nature of the innovation process which requires access to and recombination of diverse knowledge. This phenomenon concerns not only large companies, but also small-to-medium enterprises. The reconsideration of the relative salience of the three types of knowledge has remarkable managerial and theoretical implications, which are developed in terms of propositions offered for further research. Copyright (c) Blackwell Publishing Ltd 2008.

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Industrial districts are local hyper-networks of self-organizing and innovating small and medium-sized companies (SMEs), which, in terms of competitiveness and employment, play an important role in Italy's society and economy. Italy's industrial structure is deeply embedded in social relations, which are stratified and vary from territory to territory. The university, partially replaced by innovation centres, plays a weak role. For industrial districts to survive the current crisis, an industrial policy based on new theoretical approaches is needed, capable of analysing and dealing with emergent forms of industrial organization.

The recent tumor research has lead scientists to recognize the central role played by cancer stem cells in sustaining malignancy and chemo-resistance. A model of cancer presented by one of us describes the mechanisms that give rise to the different kinds of cancer stem-like cells and the role of these cells in cancer diseases. The model implies a shift in the conceptualization of the disease from reductionism to complexity theory. By exploiting the link between the agent-based simulation technique and the theory of complexity, the medical view is here translated into a corresponding computational model. Two main categories of agents characterize the model, 1) cancer stem-like cells and 2) stem cell differentiation stage factors. Cancer cells agents are then distinguished based on the differentiation stage associated with the malignancy. Differentiation factors interact with cancer cells and then, with varying degrees of fitness, induce differentiation or cause apoptosis. The model inputs are then fitted to experimental data and numerical simulations carried out. By performing virtual experiments on the model's choice variables a decision-maker (physician) can obtains insights on the progression of the disease and on the effects of a choice of administration frequency and or dose. The model also paves the way to future research, whose perspectives are discussed.

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