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

Peter Nijkamp, Jacques Poot and Gabriella Vindigni
Additional contact information
Gabriella Vindigni: University of Catania

Chapter 18 in Knowledge, Complexity and Innovation Systems, 2001, pp 369-401 from Springer

Abstract: 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.

Keywords: Total Factor Productivity; Fiscal Policy; Government Spending; Public Capital; Decision Attribute (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations: View citations in EconPapers (4)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:adspcp:978-3-662-04546-6_18

Ordering information: This item can be ordered from
http://www.springer.com/9783662045466

DOI: 10.1007/978-3-662-04546-6_18

Access Statistics for this chapter

More chapters in Advances in Spatial Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().