– Agent-based simulation approach.

Young Joon Oh(University of Texas at Dallas)

Abstract

Purpose :

Innovation is a main engine for economic growth. It requires collaboration with firms, universities, governments, etc. In this respect, innovation networks are increasingly recognized as an effective tool for successful R&D processes in the hi-tech industry. Due to the positive externality of innovation networks, policymakers want to establish successful innovation networks in their areas. The intent is to create sustainable and resilient innovation networks that produce knowledge, but all too often their efforts resulted in failure. So, the question arises as to what is the key to building a sustainable innovation network. This paper seeks to propose possible keys for sustainable innovation networks using a simulation framework.

Methodology:

To capture the dynamics of the innovative agent behavior, this paper uses an agent-based SKIN model (Simulating Knowledge Dynamics in Innovation Networks). In the SKIN model, the agents represent firms who try to sell their innovative products to other agents. The firms need to enhance its innovation performance to survive in the market. To improve their inherent knowledge, they can choose some strategies for learning and adaptation such as incremental or radical learning, cooperation and networking. The SKIN model shows the dynamics of the behavior of strategic agents. However, it is difficult for the existing SKIN model to capture link dynamics of the network itself. Here, I modify the SKIN model to capture dynamics of both agents and links.

Findings :

As a result, the modified SKIN model uncovers diversity as a source of innovation network sustainability. There are two types of diversities in the model. First, if an innovation network has agents, possessing diverse knowledge, the network turns out to be more sustainable. Second, if the network has topological diversity (i.e. “small world”), a sustainable innovation network is established. Intuitively, we can understand the results in following way: once a network produces successful outcomes, the agents in the network become more homogeneous, and then networking is less profitable. The presence of agents with diverse knowledge however allows for creation of fresh innovation. The topological diversity produces the beneficial effect by adding more links and arranging them more efficiently, so knowledge can be easily transferred in the network. As a consequence, both agent and link diversity are key factors to build a sustainable innovation network.

Practical implications :

To make a successful innovation network, it is necessary to create a circumstance for knowledge diversity. Thus, it is beneficial if an innovation network contains a university and a research institution as its agent. The outcome could also advise policymakers to nurture and streamline coordinative actions that aim to maximize innovation by connecting enterprises and academia.

Value :

In sum, this study models the role of increasing diversity of agents and their connections in maximizing network innovation.