A Framework for Building and Reasoning with Adaptive and Interoperable PMESII Models

Abstract

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Service, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) NOV 20072. REPORT TYPE Final DATES COVERED (From -To AUTHOR(S)John Langton and Subrata Das 5f. WORK UNIT NUMBER E4 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Charles River Analytics, Inc. 625 Mount Auburn St. Cambridge MA 02138 PERFORMING ORGANIZATION REPORT NUMBER SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)AFRL/RISE 525 Brooks Rd Rome NY 13441-4505 SPONSORING/MONITORING AGENCY REPORT NUMBER AFRL-RI-RS-TR-2007-241 DISTRIBUTION AVAILABILITY STATEMENT APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. PA# WPAFB 07-0301 SUPPLEMENTARY NOTES ABSTRACTA framework for the development and integration of Political, Military, Economic, Social, Information, and Infrastructure (PMESII) models in support of a Commander's Predictive Environment (CPE) was investigated and developed. Key challenges were allowing analysts to share their models, interconnect their models, and explore the relationships between elements in a network of integrated models. Areas explored included how to translate between different interfaces, ontologies, sub-domains, and formalisms, e.g., Bayesian networks or causal diagrams for representing and executing or simulating models. Modeling paradigms investigated included, causal graphical models, stability and reconstruction operations models (SROM), concept graphs, concept maps, and various tools for building neural network models and production rules. Also included are results of investigations, recommendations for resolving model incompatibilities, methods for reasoning with heterogeneous networks of models, and a system design specification. Our primary approach for CPEF centered on graphical models, given their capability of providing human-centric, easy-to-understand visual representations of relationships amongst the models' variables, along with specific underlying procedural interpretations for query execution to understand dependencies among variables. We developed methods for building such graphical PMESII models, along with a methodology for wrapping existing models to facilitate interoperability. The net result provides a strong foundation for an integrated development environment (IDE) for producing networks of interoperable PMESII models. SUBJECT TERMS LIST OF FIGURES LIST OF ...

A cognitive prosthesis for complex decision-making

“…While a variety of analytical tools have been designed to support understanding and planning in complex domains (e.g., Allen, Corpac, and Frisbie, 2006;Chappell et al, 2004;Chen and Lee, 2003;Langton and Das, 2007;Surdu and Kitka, 2008;Vester, 2007), the effectiveness of such tools is not often investigated in a systematic manner using the experimental research method (e.g., Lafond et al, 2012;Lerch and Harter, 2001).…”

Support Requirements for Cognitive Readiness in Complex Operations

“…Although human reasoning about complex dynamic systems is limited by the computational capabilities of the brain (Halford, Baker, McCredden, & Bain, 2005), technology, training, and teamwork may augment these capabilities (Gonzalez, 2005; Karakul & Qudrat-Ullah, 2008). According to many researchers and practitioners, there has been significant progress in the past decade in developing technologies and methods that support human sensemaking and decision-making processes in complex domains, such as command and control (e.g., Allen, Corpac, & Frisbie, 2006), operational analysis (Langton & Das, 2007; Lizotte et al, 2008), and management (Busemeyer & Pleskac, 2009; Vester, 2007). Nonetheless, examples in which cognitive support fails to improve performance abound (e.g., Arnold, Collier, Leech, & Sutton, 2004; Lerch & Harter, 2001).…”