Introduction: Since the financial crisis of 2008, the theory of financial innovation has been a focus at a time of re-evaluation and re-conceptualization. However, little has been done to evaluate the current state of research considering the increasing complexity of financial innovation. This paper examines the hypothesis of a general theory that encompasses increasing complexities in the financial innovation process.

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The role of networks in the development of technological innovation has attracted much theoretical and empirical attention. Yet much of the work has explored the role of undirected and homogeneous networks. In real cases many networks are directed. The flow of information, benefits or observations is directed from one node towards another node. Real networks are also heterogeneous: few nodes have high degree while many others have small degree. In this paper, we report on the results of an evolutionary agent based model in which a group of agents, in our case firms, collectively search a complex (rugged) technological landscape and observe each others solutions with different frequencies through different observation networks. Two groups of networks are considered in the analysis, the first group comprises undirected networks that vary in terms of efficiency while the second group comprises directed networks that vary in terms of homogeneity of node degree. We find that collective innovation (exploration of a technology landscape) improve average fitness over independent search because information about good innovations can diffuse faster through the network at an early stage. Moreover, we find that efficient networks outperform inefficient ones in the first stages of search, but some of the inefficient ones do marginally better in later stages. Finally, we find that degree-homogeneous and undirected networks achieve better fitness on average than heterogeneous and directed ones. We explain these results from the perspective of system-level technological innovation and in light of the theory of technological lock-in. We conclude by discussing implications for technological innovation and possible extensions.

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