The epidemic of innovation - playing around with an agent-based model
The artificial units of an agent-based model can be played around to diffuse innovation and new ideas or act to conserve the status quo, escaping from the advances in technology or organizational methods or new ideas and proposals, exactly as the agents in an epidemic situation can act to diffuse or to avoid the contagion. The emerging structure is obviously a function of the density of the agents, but its behavior can vary in a dramatic way if a few agents are able to evolve some form of intelligent behavior. In our case, intelligent behavior is developed allowing the agents to plan actions using artificial neural networks or, as an alternative, reinforcement learning techniques. The proposed structure of the neural networks is self-developed via a trial and error process: the reinforcement learning model is built upon the Swarm-like agent protocol in Python (SLAPP) tool, a recent implementation of the standard Swarm function library for an agent-based simulation (www.swarm.org), written using Python (www.python.org), a powerful and simple language: the result is also very useful from a didactic perspective. A more powerful tool, the cross targets (CTs) algorithm, is also introduced as a key interpretation and as a perspective methodology; at present, CTs are running only in Swarm; a SLAPP version is under development. A control implementation of the reinforcement learning model has also been developed and placed on-line as an applet, using NetLogo (http://ccl.northwestern.edu/netlogo/).
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 18 (2009)
Issue (Month): 7 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/GEIN20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/GEIN20|
When requesting a correction, please mention this item's handle: RePEc:taf:ecinnt:v:18:y:2009:i:7:p:707-728. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If references are entirely missing, you can add them using this form.