Active Nonlinear Tests (ANTs) of Complex Simulation Models

John H. Miller ()
Additional contact information
John H. Miller: Carnegie Mellon University, Social and Decision Sciences, Postal: Pittsburgh, PA 15213

Papers from Carnegie Mellon, Department of Decision Sciences

Abstract: Simulation models are becoming increasingly common in the analysis of critical scientific, policy, and management issues.Such models provide a way to analyze complex systems characterized by both large parameter spaces and nonlinear interactions. Unfortunately, these same characteristics make understanding such models using traditional testing techniques extremely difficult. Here we show how a model's structure and robustness can be tested via a simple, automatic, nonlinear search algorithm designed to actively ``break'' the model's implications. Using the active nonlinear tests (ANTs) developed here, one can easily probe for key weaknesses in a simulation's structure, and thereby begin to improve and refine the model's design. We demonstrate ANTs by testing a well-known model of global dynamics (World3),and show how this technique can be used to uncover small, but powerful,nonlinear effects that may highlight vulnerabilities in the original model.

Keywords: Testing Simulation Models; Nonlinear Sensitivity Analysis; World3 Model; and Genetic Algorithms. (search for similar items in EconPapers)
Date: 1996-02-05
References: Add references at CitEc
Citations:

Downloads: (external link)
http://zia.hss.cmu.edu/econ/misc/gentest.html (application/postscript)

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:wop:carnds:_005

Access Statistics for this paper

More papers in Papers from Carnegie Mellon, Department of Decision Sciences Contact information at EDIRC.
Bibliographic data for series maintained by Thomas Krichel ().