Brokering Knowledge in Innovation Networks: an Agent-Based Modeling Simulation Approach

This  study explores how to apply Agent-based Modeling Simulation (ABMS) to investigate knowledge brokering in a high-tech innovation system. Knowledge dynamics in innovation networks for emerging technologies have attracted wide attention from both academia and practitioners. The common finding is that brokering agents are important because they facilitate knowledge diffusion, thereby driving the innovation process. In general, knowledge brokering is described as “moving knowledge around and creating connections between researchers, inventors, and the receptors on the market, as well as the way in which scientific knowledge is transported and translated across the boundaries.” Knowledge brokering not only means moving knowledge but also means the transforming of knowledge, including the process of localization and identification, redistribution and dissemination, and the rescaling and transformation of knowledge. For  the past decade, more and more researches focus on taking advantage of ABMS in exploring knowledge dynamics in innovation networks. ABMS is a promising tool to help understand more of a complex system, such as innovation networks, because it can represent important phenomena difficult to capture in other mathematical formalisms. The agent-based model proposed in this paper is developed based on real data from the Taiwanese biopharmaceutical sector. Knowledge  dynamics in innovation networks have been believed to be beneficial to the development of an emerging technology, furthermore, empirical studies indicate that government policy instruments have a profound effect on knowledge dynamics. ABMS in this context is needed to assist us in understanding the effectiveness of different policy interventions and how these policies will affect future development of the industry sector. The contributions of this study are both in the areas of policy-suggestions for knowledge dynamics in an emerging sector and in methodological use of ABMS for studying the impact of policy instruments. The  goal of this paper is to build an agent-based model to formalize and simulate knowledge dynamics through computational models. We furthered our knowledge in how to simulate knowledge dynamics in innovation networks using ABMS, and discuss ABMS-related issues such as types of agents that should include in ABMS, identifying a proxy for the knowledge base and outcome of knowledge dynamics.