State space analysis for model plausibility validation in multi-agent system simulation of urban policies

M A Piera, R Buil and E Ginters

Journal of Simulation, 2016, vol. 10, issue 3, 216-226

Abstract: Multi-agent system (MAS) models have been increasingly applied to the simulation of complex phenomena in different areas, providing successful and credible results. However, model validation is still an open problem. The complexity of the stochastic interaction between agents, together with a large number of parameters, can make validation procedures intractable. Particular validation difficulties appear in social science using MAS models when agents are defined as spatial objects to computationally represent the behaviour of individuals to study emergent patterns arising from micro-level interactions. This paper considers some of the difficulties in establishing the verification and validation of agent-based models (ABMs) and proposes the use of coloured Petri net (CPN) formalism to specify agent behaviour to check whether the model looks and behaves logically. Model plausibility is used to express the conformity of the model with a priori knowledge about the process. A proof-of-concept is presented by means of a case study to test the robustness of emergent patterns through sensitivity analyses and can be used for model calibration. The proposed methodology has been applied in the European Future Policy Modelling project (www.fupol.eu) to create trust and increase the credibility of the ABMs developed to foster e-participation in the design of urban policies by means of simulation techniques.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1057/jos.2014.42 (text/html)
Access to full text is restricted to subscribers.

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:taf:tjsmxx:v:10:y:2016:i:3:p:216-226

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20

DOI: 10.1057/jos.2014.42

Access Statistics for this article

Journal of Simulation is currently edited by Christine Currie

More articles in Journal of Simulation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().