The objective of this paper is to critically review contemporary insights derived from studies that focus on relatedness. A well-established body of literature has identified three approaches for measuring relatedness, those based on co-occurrence matrices, industrial hierarchy and resource similarity. From these measures, several authors have begun developing relational networks to capture the branching capabilities of products, industries, technologies and skills. Thereafter the present contribution then shifts from analysing 'what is' and begins considering what 'could be'? It argues that the concept of relatedness lies at the heart of deconstructing issues of unrelated diversification and smart specialisation.

This chapter synthesises the literatures of evolutionary economic geography and the geography of innovation in order to demonstrate the path dependent and evolutionary logic inherent to knowledge creation and diffusion processes. Critically, this synthesis reaffirms the continued importance of geography as a palpable medium to organize economic activity. Making explicit use of the 'knowledge space' methodology developed by Kogler et al., (2013) this chapter examines the technological evolution of Ireland (1981-2010) and provides new insights on how regional knowledge trajectories are shaped by path-dependent, recombinant, and co-evolutionary network dynamics. For Ireland, we show that its technological development can be understood as a branching phenomenon, whereby new technological trajectories branched out from previously existing or related pieces of knowledge. The chapter concludes by theorising how the knowledge space framework has an important bearing on the recently proposed Smart Specialisation thesis, which is envisioned to underpin knowledge-based regional economic development throughout Europe for the coming decade.

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It is now commonplace to assume that the production of economically valuable knowledge is central to modern theories of growth and regional development. At the same time, it is also well known that not all knowledge is equal, and that the spatial and temporal distribution of knowledge is highly uneven. Combing insights from Evolutionary Economic Geography (EEG) and Economic Complexity (EC) the primary aim of this paper is to investigate whether more complex knowledge is generated by local of non-local (foreign) firms. From this perspective, a series of recent contributions have highlighted the role of foreign firms in enacting structural transformation, but such an investigation has yet to account for the complexity of the knowledge produced. Exploiting information contained within a recently developed Irish patent database our measure of complexity uses a modified bipartite network to link the technologies produced within regions, to their country of origin i.e. local or nonlocal. Results indicate that the most complex technologies tend to be produced in a few diverse regions. For Ireland, our results indicate that the most complex technologies tend to be produced in a few diverse regions. In addition, we find that the majority of this complex knowledge is generated in technology classes where the share of foreign activity is greater than local firms. Lastly, we generate an entry model to compute the process of complex regional diversification. Here the focus is on how regions develop a comparative advantage in a technological domains more complex than those already present in that region. As such, we focus our attention only on those technologies with the highest complexity values, as these technologies are said to underpin the European Union's Smart Specialisation thesis.

Focusing explicitly on the regional dimensions of technological change, this paper examines the technological evolution of Irish NUTS-3 regions. Employing an index of average relatedness, it is shown that over an extended 25-year period (1981-2005) that the production of technological knowledge exhibits a strong path dependency whereby regions are technologically predisposed to diversify into those areas of the knowledge space that are proximate to their current specialization. Implications for the Smart Specialisation thesis are then discussed.