Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making
Abstract
Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 30, 2021
- Source ID
- 10.1145/3453172
Entities
People
- Ankush Khandelwal
- Armen R. Kemanian
- Basel Shbita
- Binh Vu
- Christopher Duffy
- Craig Knoblock
- Dan Feldman
- Daniel Garijo
- Daniel Hardesty-lewis
- Deborah Khider
- Ewa Deelman
- Hayley Song
- Hernán Vargas
- Jay Pujara
- Kelly Cobourn
- Kshitij Tayal
- Lele Shu
- Lissa Pearson
- Lorne Leonard
- Maria Stoica
- Maximiliano Osorio
- Michael Steinbach
- Minh Pham
- Rafael Ferreira Da Silva
- Rajiv Mayani
- Scott Peckham
- Shaoming Xu
- Suzanne A. Pierce
- Varun Ratnakar
- Vipin Kumar
- Yao-yi Chiang
- Yijun Lin
- Yolanda Gil
- Yuning Shi
- Zeya Zhang
Organizations
- Defense Advanced Research Projects Agency
- National Science Foundation
- Pennsylvania State University
- University of California, Davis
- University of Colorado
- University of Minnesota
- University of Southern California
- University of Texas at Austin
- Virginia Tech