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A primary mechanism for increasing intelligence is education, hence the importance of the education system in the innovation economy. Individuals come together to form groups, which have the ability to acquire and apply knowledge and skills at a rate that far exceeds that of the individual.

Innovation Economy

The group innovation network is the most basic form of collective intelligence. Each of these groups connected to each other through the management chain. Finally, the innovation network of a regional cluster is a network of organizational networks, which, themselves, are networks of groups and individuals.

A cluster has a very broad spectrum of nodes — actors and entities from the underlying innovation ecosystem. As within an organization, the ability for members of a regional cluster to acquire and apply knowledge and skills will be determined, to some extent, by the connectivity between members of the region. So what does this mean for economic development? It means that in addition to classic economic development policies, to thrive in the innovation economy we need to develop an additional comprehensive and coherent set of policies and programs at all four levels: individual, group, organization, and region.

Innovation economics - Wikipedia

We need to understand how the micro-foundational process can be facilitated at every level. Finally, we need to be able to visualize and measure the individual, group, organization, and regional innovation networks to inform our actions and measure success.


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Next we will explore mapping innovation networks at all four levels to understand how network science can help define and measure the effects of economic policy for the innovation economy. Mapping innovation networks requires data that reveals the acquisition and application of knowledge and skills — intelligence. There are many types of data that can be used [16] , but we will focus on utility patents that reveal how knowledge and skills have been acquired and applied to create new processes, machines, means of manufacture, and compositions of matter.

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The first network we will examine is the regional-level inter-enterprise collaboration network for the state of Maryland. We insisted that one or more of the inventors reside in Maryland to eliminate the situation where a Maryland organization funded out of state organizations and retained rights to the intellectual property.

With these restrictions we found 2, inventions. Next, we selected for inventions that had two or more Maryland organizations as co-assignees.

Collaboration

This additional restriction reduced the collection to inventions that indicated collaboration between multiple organizations private sector, academic, or government enterprises. Finally, these collaborative inventions were generated by unique enterprises located in Maryland. First, notice there is one very large connected component, color coded in dark blue. The multicolored dyads and one triad in the lower left of the graph are the few inter-enterprise collaborations that were not part of this larger collaborative network. Next, the diameter of each node was scaled according to its betweenness centrality, a network property that indicates how frequently that enterprise is on the shortest path between any two other randomly chosen organizations in the network.

Remove any one of these three organizations and the network degrades significantly. Further, these three organizations are interconnected by a small number of organizations in the center of the network. However, each dominant organization has its own set of collaborating organizations, radiating outwardly, that for the most part, are only sparsely connected to each other.

What do these three organizations know about inter-enterprise collaboration that the others do not? What policies and practices do they have that enable collaboration, and could they be adopted by others? This is the most densely connected set of organizations in the network.

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Why are they so densely connected? It turns out these ten enterprises, and HHS, are all connected by a single invention focused on predicting survival rates for people with diffuse large B cell lymphoma DLBCL [24]. This patent application has 24 inventors and 11 assignees from 5 countries. It is an interesting example of supranational inter-enterprise collaboration to solve an extremely challenging problem.

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How did eleven organizations across five countries collaborate to acquire and apply all of the requisite knowledge and skills? If this is the future in complex fields such as cancer research, what can we learn from this example that would accelerate solutions for other complex challenges in other domains? Next, in total there were 2, inventions, of which, only had multiple assignees. What policies would encourage their collaboration? Finally, we asked if the degree of inter-enterprise collaboration was a function of the technical domain in which the organizations were innovating.

For example do, medical innovations require more collaboration than communications or semiconductor innovations? The inventions spanned a wide spectrum of technical domains indicating that collaboration was related to the practices of the organizations, and not the technical domains [25]. Mapping inter-enterprise collaboration networks will do several things for economic development policy makers.

Second, it will identify the organizations that can be the source of best-of-breed policies and practices. These organizations are a valuable resource to engage in developing successful regional innovation policies and programs. Third, the maps highlight opportunities to increase connectivity, and hence innovation. For example, what could be done to better connect the collaborators of the three dominant enterprises to each other? What can the collaborators teach the non-collaborating organizations? In the figure below we present the innovation network of a sports equipment and apparel company.

Each node represents an individual inventor in that company. Each link between two nodes indicates they have co-invented one or more times. For visual clarity we have removed 23 solo inventors that have never invented with another person in the company. The patents and published patent applications spanned a four year period from January through December , during which inventors, collaborated two or more at a time, to produce 1, inventions. First, it has multiple clusters, highlighted in different colors. Clusters can be caused by many things, e. Second, if we calculate the connected components [26] we find 30 connected groups ranging in size from two inventors to This is important because knowledge propagates more easily within a connected component, and the larger the connected component the easier knowledge is shared across the organization.

Should these people leave, the connected component would break apart, and the average path length between inventors increase, thus decreasing, slowing, or disrupting the flow of knowledge. The figure below illustrates an extreme example of a trusted Bridger, and the impact their departure can have. This particular military organization that has two distinct technology groups that are located a few miles apart in different buildings on the same base.

For over forty years, the inventor highlighted in red, bridged the two groups, and was a co-inventor on every invention that involved inventors from both groups.


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After this inventor left the organization there was never again an invention that involved inventors from both groups. What knowledge did this inventor have, and what behaviors did the inventor practice to help the organization find the inventions at the intersections?

What innovation policies could the organization have created to facilitate this inventor and others to perform the bridging function? How could management have used periodic network mapping to identify this single point of failure and train a replacement in time? First, invention is a social process, people share ideas with those they trust.

Further, in a social network trust decreases dramatically with the degrees of separation between any two randomly chosen inventors. Next, betweenness centrality is a network measure of how frequently an inventor is on the shortest path between any two other randomly chosen inventors, and the shorter the path the higher the trust. Figure 4: Example of an Innovation Backbone.

In the figure above the normalized betweenness centrality NBC , scaled from , is presented in the blue barchart, left side of the figure.

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Note the exponential decay. Only of the inventors have an NBC greater than or equal to 1. In the network diagram we have highlighted the 30 inventors with the greatest NBC in red, along with their first degree link. They form the trust-backbone of the network. IBP has seen the Innovation Backbone phenomenon in every government, university, and industry innovation network we have examined.

In work sponsored by the Office of Naval Research we interviewed 71 backbone inventors from five different warfare center laboratories. We found that these inventors could be characterized as either: 1 the absolute subject matter expert in domain X, that everyone turned to for domain X problems, 2 the owner of a critical facility or capability, e. What are the implications for economic development?