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Spatial Dynamics and Government Policy: An Artificial Intelligence Approach to Comparing Complex Systems

In: Knowledge, Complexity and Innovation Systems

Author

Listed:
  • Peter Nijkamp

    (Free University Amsterdam)

  • Jacques Poot

    (Victoria University of Wellington)

  • Gabriella Vindigni

    (University of Catania)

Abstract

Complexity is concerned with the unpredictable nature of non-linear and dynamic systems. Complexity can relate to a dynamic causal sequence of events at an object-specific micro-level [such as in the case of the weather, business performance, market impact of innovation, individual well-being, etc.], but it may also refer to the outcomes of repeated experiments in a semi-controlled setting. A comparison of the results of case studies is a good illustration of the latter interpretation of complexity. In this case, it is useful to see to what extent the outcome is shaped by the systemic background and the specific research methodologies used.

Suggested Citation

  • Peter Nijkamp & Jacques Poot & Gabriella Vindigni, 2001. "Spatial Dynamics and Government Policy: An Artificial Intelligence Approach to Comparing Complex Systems," Advances in Spatial Science, in: Manfred M. Fischer & Josef Fröhlich (ed.), Knowledge, Complexity and Innovation Systems, chapter 18, pages 369-401, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-04546-6_18
    DOI: 10.1007/978-3-662-04546-6_18
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    Cited by:

    1. Mehmet Güney Celbiş & Pui‐hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 559-584, April.
    2. Hemert, P. van & Nijkamp, P., 2008. "Thematic research prioritization in the EU and the Netherlands: an assessment on the basis of content analysis," Serie Research Memoranda 0023, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    3. Márton Gosztonyi & Csákné Filep Judit, 2022. "Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    4. Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.

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