Advanced Search
MyIDEAS: Login to save this article or follow this journal

The epidemic of innovation - playing around with an agent-based model

Contents:

Author Info

  • Pietro Terna

Abstract

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/).

Download Info

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.
File URL: http://www.tandfonline.com/doi/abs/10.1080/10438590802564808
Download Restriction: Access to full text is restricted to subscribers.

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.

Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Economics of Innovation and New Technology.

Volume (Year): 18 (2009)
Issue (Month): 7 ()
Pages: 707-728

as in new window
Handle: RePEc:taf:ecinnt:v:18:y:2009:i:7:p:707-728

Contact details of provider:
Web page: http://www.tandfonline.com/GEIN20

Order Information:
Web: http://www.tandfonline.com/pricing/journal/GEIN20

Related research

Keywords: artificial neural networks; reinforcement learning; innovation; agent-based simulation; Swarm protocol; NetLogo;

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Antonelli Cristiano & Ferraris Gianluigi, 2012. "Endogenous knowledge externalities: an agent based simulation model where schumpeter meets Marshall," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201202, University of Turin.
  2. Cristiano, Antonelli & Ferraris, Gianluigi, 2009. "Innovation as an Emerging System Property: an Agent Based Model," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 200911, University of Turin.
  3. Marcel Ausloos & Herbert Dawid & Ugo Merlone, 2014. "Spatial interactions in agent-based modeling," Papers 1405.0733, arXiv.org.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.