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Simulating Entrepreneurial Opportunity Recognition Processes: An Agent-Based and Knowledge-Driven Approach

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Part of the Studies in Computational Intelligence book series (SCI,volume 416)

Abstract

This chapter describes how we abstract a complex phenomenon– opportunity recognition–and build a simulation model for studying strategic entrepreneurship in a knowledge-based economy. We develop an agent-based simulation tool that enables researchers to explore entrepreneurial strategies, their associated financial payoffs, and their knowledge creation potential under different environmental conditions. Opportunity recognition processes can be analyzed in detail both on a micro- and macro-level. To illustrate the modeling capabilities of the tool, we conduct some basic simulation runs and model three distinct entrepreneurial strategies–the innovator, the inventor, and the reproducer–and compare their knowledge progression and financial performance profiles. The software allows us to study the individual and the societal level effects that arise from competitive agent behavior in both national and international settings. It can account for international knowledge spillovers that occur in a globalized knowledge economy. The simulation can be used to conduct innovative research that will result in theory-driven hypotheses that can inform corporate and public-sector decision makers, which would be difficult to derive from empirical analyses.

Keywords

  • Agent Group
  • Knowledge Item
  • Knowledge Group
  • Opportunity Recognition
  • Knowledge Asset

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-28888-3_2
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Correspondence to Martin Ihrig .

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Ihrig, M. (2012). Simulating Entrepreneurial Opportunity Recognition Processes: An Agent-Based and Knowledge-Driven Approach. In: Byrski, A., Oplatková, Z., Carvalho, M., Kisiel-Dorohinicki, M. (eds) Advances in Intelligent Modelling and Simulation. Studies in Computational Intelligence, vol 416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28888-3_2

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  • DOI: https://doi.org/10.1007/978-3-642-28888-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28887-6

  • Online ISBN: 978-3-642-28888-3

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