System dynamics modeling for Pro-Active intelligence (PAINT)
The Pro-Active Intelligence (PAINT) program, sponsored by the Intelligence Advanced Research Projects Activity (IARPA), was formed to address the challenges1 posed by distributed human networks, including terrorists and insurgencies, both independent and state-sponsored. In particular, certain threa...
| Main Authors: | , , , , , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
© Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/141602 |
| _version_ | 1811089464698601472 |
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| author | Anderson, Ed Choucri, Nazli Goldsmith, Daniel Madnick, Stuart E. Siegel, Michael D. Sturtevant, Dan |
| author_facet | Anderson, Ed Choucri, Nazli Goldsmith, Daniel Madnick, Stuart E. Siegel, Michael D. Sturtevant, Dan |
| author_sort | Anderson, Ed |
| collection | MIT |
| description | The Pro-Active Intelligence (PAINT) program, sponsored by the Intelligence Advanced Research Projects Activity (IARPA), was formed to address the challenges1 posed by distributed human networks, including terrorists and insurgencies, both independent and state-sponsored. In particular, certain threats (including emerging dual-use technologies) are difficult to detect using traditional intelligence means because: (a) indicators are difficult to discern and may give little warning time, (b) there is usually limited relevant data collection and integration capability, and (c) expertise is generally diverse and disconnected.
Over the course of 18 months from September 2007 to February 2009, an effort, led by researchers from MIT, was initiated to develop computational social science models to study and understand the dynamics of complex intelligence targets for nefarious technology activities (broadly defined as activities outside U.S. national interest). System dynamics models were developed because they offered great opportunities to (a) understand and represent determinants of nefarious technology development, (b) to identify aspects of critical pathways, such as resource management, towards the development of nefarious technologies, and (c) support a modeling based strategy for the identification of new sources of intelligence.
This report describes the “System Dynamics Modeling for Pro-Active Intelligence” effort and its two thrusts: (a) development of a comprehensive holistic system dynamics model to represent, understand, and differentiate nefarious and benign activities and (b) the development of a detailed system dynamics resource model that can be used as a component of a multi-method federation of models. In both cases, simulations were conducted to illustrate the effectiveness of these models in demonstrating system behavior and, on occasion, highlighting potentially counter-intuitive behaviors. |
| first_indexed | 2024-09-23T14:19:36Z |
| format | Technical Report |
| id | mit-1721.1/141602 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:19:36Z |
| publishDate | 2022 |
| publisher | © Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1416022022-05-05T16:24:12Z System dynamics modeling for Pro-Active intelligence (PAINT) Anderson, Ed Choucri, Nazli Goldsmith, Daniel Madnick, Stuart E. Siegel, Michael D. Sturtevant, Dan The Pro-Active Intelligence (PAINT) program, sponsored by the Intelligence Advanced Research Projects Activity (IARPA), was formed to address the challenges1 posed by distributed human networks, including terrorists and insurgencies, both independent and state-sponsored. In particular, certain threats (including emerging dual-use technologies) are difficult to detect using traditional intelligence means because: (a) indicators are difficult to discern and may give little warning time, (b) there is usually limited relevant data collection and integration capability, and (c) expertise is generally diverse and disconnected. Over the course of 18 months from September 2007 to February 2009, an effort, led by researchers from MIT, was initiated to develop computational social science models to study and understand the dynamics of complex intelligence targets for nefarious technology activities (broadly defined as activities outside U.S. national interest). System dynamics models were developed because they offered great opportunities to (a) understand and represent determinants of nefarious technology development, (b) to identify aspects of critical pathways, such as resource management, towards the development of nefarious technologies, and (c) support a modeling based strategy for the identification of new sources of intelligence. This report describes the “System Dynamics Modeling for Pro-Active Intelligence” effort and its two thrusts: (a) development of a comprehensive holistic system dynamics model to represent, understand, and differentiate nefarious and benign activities and (b) the development of a detailed system dynamics resource model that can be used as a component of a multi-method federation of models. In both cases, simulations were conducted to illustrate the effectiveness of these models in demonstrating system behavior and, on occasion, highlighting potentially counter-intuitive behaviors. CONTRACT FA8750-07-C-0101 ISSUED BY AFRL/IFKE CODE FA8750 6. ADMINISTERED BY CODE N62879 USAF, AFMC AIR FORCE RESEARCH LABORATORY 26 ELECTRONIC PARKWAY ROME NY 13441-4514 2022-04-03T18:18:57Z 2022-04-03T18:18:57Z 2009-02-02 Technical Report https://hdl.handle.net/1721.1/141602 Anderson, E., Choucri, N., Goldsmith D., Madnick, S. E., Siegel, M., & Sturtevant, D. (2009). System dynamics modeling for pro-active intelligence (ECIR Working Paper 2009-4). MIT Political Science Department. en_US Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf © Massachusetts Institute of Technology |
| spellingShingle | Anderson, Ed Choucri, Nazli Goldsmith, Daniel Madnick, Stuart E. Siegel, Michael D. Sturtevant, Dan System dynamics modeling for Pro-Active intelligence (PAINT) |
| title | System dynamics modeling for Pro-Active intelligence (PAINT) |
| title_full | System dynamics modeling for Pro-Active intelligence (PAINT) |
| title_fullStr | System dynamics modeling for Pro-Active intelligence (PAINT) |
| title_full_unstemmed | System dynamics modeling for Pro-Active intelligence (PAINT) |
| title_short | System dynamics modeling for Pro-Active intelligence (PAINT) |
| title_sort | system dynamics modeling for pro active intelligence paint |
| url | https://hdl.handle.net/1721.1/141602 |
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