Measuring the Globalization of Knowledge Networks

1. Introduction This paper presents a method of understanding the growth of global science as resulting from a mechanism of preferential attachment within networks. The paper seeks to contribute to the development of indicators of knowledge creation and transfer by presenting a theory and case study of network structures in science. It is our view that indicator development has suffered from a lack of attention to the theoretical basis for understanding the dynamics of knowledge creation. This lack has been due, in part, to the difficulty of measuring dynamic systems within social organizations. This paper attempts to fill this gap by proposing theory-based indicators of knowledge creation using network theory and analysis. The paper presents a hypothesis about the knowledge system that is explored by analyzing the growth of international collaboration in science. Scientific research creates knowledge through a process of hypothesis, experimentation, testing, and verification. Funds and infrastructure are committed to experimental research. The processes within the knowledge-creation process produce outputs such as publications and patents. These outputs can be tracked through analysis of the authorship, citations and references within the published materials. A factor limiting analysis has been the lack of direct measures of the process. ST Astley, 1985; Monge & Contractor, 2003; Mohrman, Galbraith & Monge, forthcoming), complexity theory and the study of self-organization (Kauffman, 1993; Holland 1995; Tuomi, 2002), network theory (Barabasi & Albert 1999; Newman 2001) and communications theory (Luhmann 1988; Maturana & Varela 1987, Leydesdorff 2000) we have developed the hypothesis that scientific knowledge emerges along the lines of a emergent, self-organizing system similar to a biological eco-system, but with a different dynamics (also, Wagner & Mohrman, forthcoming). The individual agents in the landscape (in this case, individual scientific researchers and engineers) seek resources, recognition, and reward (Whitely 1984). As they examine the landscape for the most efficient way to obtain these benefits, agents engage in both cooperation and competition in a field of finite resources (Axelrod & Epstein 1994). The agents connect through a series of local searches and connections, as well as through weak links to connect to formerly unknown people or resources. Through their non-linear and complex interactions, the agents create a system that takes on emergent properties of its own. The emergent system then becomes a constraint and a reference point for researchers (Leydesdorff 2001). The combination and recombination of know-how and ideas leads to the creation of knowledge (Weitzman 1993). New knowledge then provides opportunities of preferential attachment in a network. The dynamics of the knowledge-creating system can be considered and studied as a network. The flow of value from scientific research is best understood through a network lens. This view stems from an understanding of the work of science as fundamentally relational, of advances in knowledge as stemming from knowledge sharing and knowledge combining activities that do not know organizational or, increasingly, disciplinary boundaries, and of value as accruing through use, or application. All this happens in a way that is not anticipated or planned ahead of time—the discoveries emerge from the combination of elements across the landscape as they fit the needs of agents and use available resources. Science can be considered as a communications system that self-organizes into a complex adaptive system. Complex adaptive systems have been found to share common features (Waldrop 1999). According to Amaral (2004) and others, complex systems have the following features: • a dynamic internal structure that evolves and interacts in a complex manner; • emergent behaviors and patterns that are not caused by a single entity in the system but may arise from

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