Title page for ETD etd-03092008-221211

Type of Document Dissertation
Author Guo, Donghang
URN etd-03092008-221211
Title Large-Scale Simulations for Complex Adaptive Systems with Application to Biological Domains
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Santos, Eunice E. Committee Chair
Arthur, James D. Committee Member
Lin, Tao Committee Member
Ribbens, Calvin J. Committee Member
Santos, Eugene Jr. Committee Member
  • Adaptivity
  • Self-Organization
  • Multi-Agent System
  • Complex Adaptive System
  • Dictyostelium Discoideum
  • Interaction
Date of Defense 2007-12-12
Availability unrestricted
Modeling or simulating Complex Adaptive Systems (CASs) is both important and challenging. As the name suggests, CASs are systems consisting of large numbers of interacting adaptive compartments. They are studied across a wide range of disciplines and have unique properties. They model such systems as multicellular organisms, ecosystems, social networks, and many more. They are complex, in the sense that they are dynamical, nonlinear, and heterogeneous systems that cannot be simply scaled up/down. However, they are self-organized, in the sense that they can evolve into specific structures/patterns without guidance from outside sources. Modeling/Simulating CASs is challenging, not only because of the high complexity, but also because of the difficulty in explaining the underlying mechanism behind self-organization.

The goal of this research is to provide a modeling framework as well as a simulation platform to advance the study of CASs. We argue that there are common principles behind self-organization processes of different systems across different domains. We explore, analyze, and perform experiments into these principles. We propose and implement modeling templates such as short-term and long-term adaptivity. We incorporate techniques from systems theory, employing computing paradigms, including multi-agent system and asynchronous message passing. We also consider an application from the biological domain to model and simulate under our framework, treating it as a CAS for validation purposes.

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