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Massachusetts Institute of Technology

A framework for quantifying complexity and understanding its sources : application to tow large-scale systems

Abstract

dc:description.abstract

The motivation for this work is to quantify the complexity of complex systems and to understand its sources. To study complexity, we develop a theoretical framework where the complex system of interest is embedded in a broader system: a complex large-scale system. In order to understand and show how the complexity of the system is impacted by the complexity of its environment, three layers of complexity are defined: the internal complexity which is the complexity of the complex system itself, the external complexity which is the complexity of the environment of the system (i.e., the complexity of the large- scale system in which the system is embedded) and the interface complexity which is defined at the interface of the system and its environment. For each complexity we suggest metrics and apply them to two examples. The examples of complex systems used are two surveillance radars: the first one is an Air Traffic Control radar, the second one is a maritime surveillance radar. The two large-scale systems in which the radars are embedded are therefore the air and the maritime transportation system. The internal complexity metrics takes into account the number of links, the number of elements, the function and hierarchy of the elements. The interface complexity metric is based upon the information content of the probability of failure of the system as it is used in its environment. The External complexity metric deals with the risk configuration of large- scale systems emphasizing the reliability and the tendency to catastrophe of the system.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Technology and Policy Program.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2004

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Martin, Pierre-Alain J. Y
Advisor dc:contributor.advisor
  • Christopher L. Magee.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/32270
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/32270

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Martin, Pierre-Alain J. Y. A framework for quantifying complexity and understanding its sources : application to tow large-scale systems. Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/32270