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

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  • 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/).

Suggested Citation

  • Pietro Terna, 2009. "The epidemic of innovation - playing around with an agent-based model," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 18(7), pages 707-728.
  • Handle: RePEc:taf:ecinnt:v:18:y:2009:i:7:p:707-728
    DOI: 10.1080/10438590802564808
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    References listed on IDEAS

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    1. Scott E. Page, 2007. "Prologue to The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies," Introductory Chapters,in: The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies Princeton University Press.
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    Cited by:

    1. 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.
    2. Marcel Ausloos & Herbert Dawid & Ugo Merlone, 2014. "Spatial interactions in agent-based modeling," Papers 1405.0733, arXiv.org.
    3. Antonelli, Cristiano & Ferraris, Gianluigi, 2017. "The Creative Response and the Endogenous Dynamics of Pecuniary Knowledge Externalities: An Agent Based Simulation Model," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201717, University of Turin.
    4. 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.

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