Innovation ecosystems can be understood as a complex set of entities which are interdependent, interrelated, connected through material, financial, people and knowledge flows, whom possess a high performance in creating, transforming, difusing and applying new knowledge continuosly. As such, they may be observed in large organizations, regions, industries or in the product’s value chain. They can be studied from several points of view and geographical delimitations, in a way that the point of view may help defining its components and frontiers, although the latter may be dynamic and fluid. An environment with such features, although not easy to create, may work under different conditions, and there is not a single recipe for its composition and functioning. Such complexity calls for models to simplify its analysis, bringing into focus only the elements essential to the understanding, although full comprehension may requires the use of several different models who address the analysis from different vantage points. This is the gap this thesis seeks to fill. An innovation ecosystem model based on knowledge flows is proposed, whose existence is necessary, although not sufficient, to the continuos functioning of the ecosystem. The presented conceptual model is based on a set of roles related to knowledge, performed by the entities of an ecosystem. These are the roles of knowledge generation, knowledge diffusion, and entity integration, although it is important to mention an implicit role that is implicit in the performance of other roles, which is the transformation of scientific knowledge into technological knowledge. Only entities who possess knowledge that is useful to the ecosystem and the entities who connect them will be part of the analysis. The entities who compose the ecosystem are connected by relationships characterized by knowledge flow affecting factors, such as the level of trust, geographical and relational distance. The environment around these entities is also part of the ecosystem, represented by its support elements, its selection mechanisms and the characteristics of its demands. In order to obtain conceptually prove the model and test the hypothesis that guided this work, an agent based computational model has been created. Flexible, it allows the simulation of several scenarios, from which a few were selected to evaluate the behavior of agents under static and dynamic scenarios, with different populations engaged in knowledge roles, who were able to adapt its motivation according to their experiences throughout the simulations. These scenarios were evaluated to determine whether that set of agents under those conditions would form an innovation ecosystem.
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