Modeling innovation by a kinetic description of the patent citation system

Details Modeling innovation by a kinetic description of the patent citation system Modeling innovation by a kinetic description of the patent citation system

2005-08-18

arxiv.org/abs/physics/0508132v1

Physics

Physics and Society

8 pages, 5 figures

Scientific paper

This paper reports results of a network theory approach to the study of the United States patent system. We model the patent citation network as a discrete time, discrete space stochastic dynamic system. From data on more than 2 million patents and their citations, we extract an attractiveness function, $A(k,l)$, which determines the likelihood that a patent will be cited. $A(k,l)$ is approximately separable into a product of a function $A_k(k)$ and a function $A_l(l)$, where $k$ is the number of citations already received (in-degree) and $l$ is the age measured in patent number units. $A_l(l)$ displays a peak at low $l$ and a long power law tail, suggesting that some patented technologies have very long-term effects. $A_k(k)$ exhibits super-linear preferential attachment. The preferential attachment exponent has been increasing since 1991, suggesting that patent citations are increasingly concentrated on a relatively small number of patents. The overall average probability that a new patent will be cited by a given patent has increased slightly during the same period. We discuss some possible implications of our results for patent policy.

Affiliated with

Also associated with

No associations

Rating

Modeling innovation by a kinetic description of the patent citation system does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Modeling innovation by a kinetic description of the patent citation system, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Modeling innovation by a kinetic description of the patent citation system will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-120090