
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
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Initial Amendment Date: | January 19, 2010 |
Latest Amendment Date: | January 19, 2010 |
Award Number: | 1002519 |
Award Instrument: | Standard Grant |
Program Manager: |
Eduardo Misawa
emisawa@nsf.gov (703)292-5353 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
Start Date: | February 1, 2010 |
End Date: | January 31, 2012 (Estimated) |
Total Intended Award Amount: | $30,000.00 |
Total Awarded Amount to Date: | $30,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
660 S MILL AVENUE STE 204 TEMPE AZ US 85281-3670 (480)965-5479 |
Sponsor Congressional District: |
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Primary Place of Performance: |
660 S MILL AVENUE STE 204 TEMPE AZ US 85281-3670 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
COMPUTATIONAL MATHEMATICS, DYNAMICAL SYSTEMS |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
Increasingly, the engineering of complex systems requires consideration of an intricate web of components and their interaction in diverse social and technical environments. Simulation can assist in designing and testing socio-technical systems by allowing the potential space of outcomes to be explored under given designs. Agent-based models have been developed as a method for building models of complex systems, with great success. Agents may be designed to represent system components and to specify the interactions between them in an incredible level of detail. While popular, the full potential of the methodology to support engineering of complex systems has not been reached, however, because of a set of key challenges. First, there exists a relative lack of robust methods for calibrating agent-based models to theory. Second, there is a paucity of reliable approaches for extracting coarse-grained, system level information as it emerges in agent-based simulations. Third, there is a dearth of schemes for handling uncertainty in the application of agent-based rules to system behavior. Fourth, computation of agent-based models is inefficient when agents are numerous in volume and richly-specified in behavior. Together, these impediments constrain the ability of agent-based modeling to enable prediction, to support decisions, and to facilitate the design, control, and optimization of complex systems. The main objective of this project is to broaden the extensibility of agent-based modeling beyond these constraints. This will be achieved by developing novel computational methods to fuse agent-based modeling, uncertainty measurement and quantification, and mathematics for pattern-extraction.
This project will expand the capabilities of agent-based modeling in supporting the design, engineering, and testing of complex systems. Our initial focus is to develop a prototype scheme that can be applied to complex socio-behavioral systems, but the project is of potential relevance across a diverse array of substantive areas. Indeed, one of our central aims is to provide the glue that can bridge diverse schemes for agent-based simulation across application areas. This could be incredibly useful in reconciling agent-based modeling into a larger "ecology" of mathematical modeling and computation, fundamentally expanding the range of questions that can be posed and systems that can be explored in simulation, while better linking simulation to real-world dynamics.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Our appreciation for the complex dynamics behind the phenomena that shape our everyday lives has advanced remarkably in recent years. Across disciplines, we have developed tremendous insight into the dynamic systems that connect our world and those that shape it and form it. But, our exploration of these connections and phenomena is still nascent and our capacity to design for complex systems, to plan for them, and to engineer them is constrained by an inherent challenge—how we might understand the dynamic intricacies of a web of components that characterize complex systems and that determine their dynamics across diverse themes, contexts, scales, systems, and data. Our existing science is, in some respects, ill-suited to address this challenge. This is particularly true of computer modeling and simulation, which we often rely upon to test ideas and hypotheses in synthetic worlds and phenomena of our own making, as proxy laboratories for real-world experimentation. Often, these models may rely upon well-understood knowns or truths that hold for one domain, space, scale, or time, but not for others. Other models have limited capacity to represent and handle novelty as it emerges in simulation. Cross-system interactivity and emergence are, however, hallmarks of many complex systems. In this project, we researched, tested, and applied new, innovative, and extensible science to directly address these limitations. We have developed, tested, and applied a meta-simulation scheme for representing, modeling, and querying computer models of complex systems. We achieved this by developing computational wrappers to embed within models; these wrappers are used to poll the evolving complexity of the represented system and to test its likely trajectories within the confines of the model, even when the rules of the system may not be well-understood or may present with large amounts of uncertainty. The meta-simulation scheme can run efficiently for massively interactive models and has the advantage of accelerating the computation of the complex systems. This work has, by necessity, required interfacing methods from mathematics, statistics, engineering, informatics, computing, and the social sciences with the result that it presents a new approach that will broaden the range of systems that can be represented in simulation and that can fundamentally expand the range of questions that models of complex systems can answer. We proved the usefulness of this concept by exploring how the scheme could be used to develop insight into the formation of urban systems and the potential for the emergence of sustainable urban morphology under varying scenarios, plans, policies, and hypotheses. More details are available at http://www.geosimulation.org.
Last Modified: 02/10/2012
Modified by: Paul M Torrens