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Modelling Learning and R&D in Innovative Environments: a Cognitive Multi-Agent Approach

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Abstract

Evolutionary arguments are an appropriate approach to the analysis of industry dynamics in a knowledge-based economy, because they can deal properly with innovation processes, technological change, path-dependence and knowledge. But in order to formalise all of this verbal accounting, researchers need methodological tools which support their theoretical analysis. In this paper we suggest some of the main requirements for computer simulation to have the same standing as the traditional tools used by neoclassical economists. Among others, aggregated behaviour should “emerge†from micro-foundations, economic agents should exhibit bounded rational behaviour, learning must be endogenous and human learning should be in agreement with some stylised facts from cognitive science and psychology. We argue that multi-agent systems is a methodology which fulfills some of the requirements above. We also propose an alternative way for modelling cognitive learning in evolutionary environments, which is in agreement with some basic concepts from cognitive science. Agents are endowed with both declarative and procedural knowledge. We have used our approach to build evolutionary models of innovative industries, where firms learn how to change their decisions about R&D budget, production, technology, etc. We refer as well to some applications using the same framework to model behavioural financial markets, economic geography and water resource management.

Suggested Citation

  • Javier Pajares & Cesáreo Hernández-Iglesias & Adolfo López-Paredes, 2004. "Modelling Learning and R&D in Innovative Environments: a Cognitive Multi-Agent Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(2), pages 1-7.
  • Handle: RePEc:jas:jasssj:2004-11-1
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    File URL: http://jasss.soc.surrey.ac.uk/7/2/7.html
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    Cited by:

    1. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.

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