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Beliefs, Values, Interests and Practices of Identities, Networks, Groups for Planning & Analysis



OBJECTIVE: Using open source data, characterize population identities and interrelationships in terms of beliefs, values, interests and practices, and detect, quantify, track and provide analytical tools.

DESCRIPTION: To protect national interests and effectively plan, coordinate and execute military operations, support training and foster partnerships, the U.S. military must better understand populations involved in or impacted by operations. Well-publicized challenges of operating in and amongst the peoples across the globe, including but not limited to the Middle East, Africa, Eurasia, and Southeast Asia, and the consequences of our military operations have highlighted the criticality of better understanding and incorporating cultural, behavioral, and demographic considerations in plans. These considerations are critical for: (1) meaningful assessments of progress towards tactical, operational and strategic objectives; (2) supporting analysis of changing population patterns to discover emerging issues or evolving relationships; (3) guiding decisions about maneuver through one area or another; (4) understanding key leaders and constituent stakeholders; (5) site selection and design considerations for contingency bases; (6) finding better partnership methods or opportunities; and many other issues.

The U.S. Army Operating Concept [1] states that, "to compel enemy actions requires putting something of value to them at risk," (p. iii) and that, "future enemies will act to remain indistinguishable from protected populations," while "Army forces [must] possess cross-cultural capabilities that permit them to operate effectively among populations" (p. 18). The Army must enable effective integration of multinational efforts, in contested environments (p. iv). The concept emphasizes the significant impact of, "Increased velocity and momentum of human interaction and events requires forces capable of responding rapidly in sufficient scale to seize the initiative, control the narrative, and consolidate order" (p. 11). The "Army Vision - Force 2025" [2] provides that "Reduce surprise" is a key line of effort, and continues noting, "Small unit leaders will be decentralized... and required to process large amounts of information... [and] the Army should exploit ways to reduce... cognitive burdens to enhance Soldiers' ability to perform in these challenging environments" (p. 6).

When fully addressed, this challenge requires historical, as well as up-to-date, dynamic and interacting population data, in order to support analytical tools that can help expose insights into population drivers and relationships. The resulting capability will also support the U.S. Army Functional Concept for Engagement [3], which notes the importance of, "understanding the relationships between actors and influencers, their allegiances and behaviors, and trends that shape their interaction, will be critical to understanding the complexity of the operating environment" (pg 10).

Relevant data about diverse beliefs, values, interests and practices, associated with identities, groups and collectives could conceivably be derived from any number of potential unclassified sources to include surveys, experimental data, biophysiological data, books [4], print and broadcast media [5], social media [6], peer-reviewed publications [7], economic and agricultural data, subject matter expertise, blogs, interviews, opinion columns, general websites, photographs, aerial imagery, massively multiplayer online games, chat forums, or many other resources. The U.S. Army requires such data be accessible, historical and current, fused, modeled, and made meaningful to Army personnel at the appropriate echelons with suitable, innovative analytic tools. Appropriate representational forms could include dynamic maps [8], network graphs [9], other conventional, novel or combinatorial forms, to include integration potential with other relevant mission data.

While 'big' social and cultural data offers promise for analysis and situational understanding [10], it also imposes significant challenges. Architectural and collection issues, updating data, data storage and processing requirements, privacy considerations, incongruities of data forms and scales, source material trustworthiness and reliability, and vastly varied availability of data are just some of the challenges impacting this topic. Although population impact planning presents a canonically wicked problem [11], analysis of groups and their associated beliefs, values, interests and practices has been demonstrated to be valuable for specialized military and intelligence analyses [12, 13], as well as within the private sector especially for marketing, advertising and product design. Such analyses has conventionally been successfully conducted by skilled experts on very narrow, focused topics.

This topic seeks to provide automated or semi-automated innovative approaches to organizing and exposing meaning from messy data, with tools to support collection and processing of big open source data, and yield meaningful focused analytical products.

PHASE I: In order to successfully address this topic, Phase 1 proposals are expected to address challenges including: (1) a means for identifying, collecting, updating and storing appropriate raw data for a topic of study using secure protocols; (2) mitigating incongruities across data sources and processing data into appropriate data representations with useful scoring methods; (3) how to inference across data and approximate information about identities, groups, organizations, networks and collectives, along with associated beliefs, values, interests and practices; (4) representations and analytics that make the data and derived information sensible; (5) measures of success and improvement for such data, tools and analytical findings.

Phase 1 deliverables are expected to include: (a) a documented conceptual design characterizing the technical method, services, tools and techniques to be implemented to collect data, perform processing and provide user-facing analytics; (b) outline and document exemplary sources, data, analytical examples and mock-up solutions for militarily-relevant topics; (c) define metrics and performance goals to be used for assessing progress towards accurate and appropriate processing (including validation and verification strategies) as well as estimating confidence, uncertainty and relevance for processed data; (d) document and report findings of pilot studies into the proposed conceptual design and technical implementation including metrics and initial performance estimates.

PHASE II: At the end of Phase II, a budget activity 6.2 effort, the expected result will be the construction and demonstration of a prototype with a technology readiness level of between 4 and 5. It is expected the prototype will have demonstrated relevance through limited user tests with Army stakeholders, and is expected to require additional funding for further military development and integration. Expected Phase II deliverables include: (a) an experimental prototype that has been demonstrated as functional, feasible and relevant on Army research and development networks with a diverse range of open sources; (b) detailed specifications for further development requirements including proposed additional sources, computer and human interfaces, algorithm enhancements resulting in improved computational processing and reduced cognitive load; (c) analyses of experiments to assess functionality and feasibility of the prototype, along with metrics to assess U.S. Army relevance and performance; (d) documented initial integration plans; and (e) technical documentation of the prototype, configuration, machine and user interfaces, data, extensibility and known limitations of the prototype (e.g., processing or storage capacity, coverage, etc.).

PHASE III DUAL USE APPLICATIONS: At the successful conclusion of Phase III, the capability is expected to have commercial use for international marketing and business, especially market and social network analysis, and potentially organizational culture [14]. It is also expected to result in a capability relevant to multiple program offices across the Department of Defense and have applications for the intelligence community. The capability is expected to support fusion across multiple intelligence sources, and include future extensibility through maximized use of open data models and software standards, and provide application programming interfaces to efficiently support the evolving information environment. The ultimate capability is expected to capture, quantify and model information (past and present, vetted and unvetted) about the many types of affiliations with which people in a region (physical, conceptual and virtual) may identify, and how those identities relate with respect to beliefs, values, interests and practices.


    • Department of the Army, United States Army Training and Doctrine Command. "The U.S. Army Operating Concept ¨C Win in a Complex World." TRADOC Pamphlet 525-3-1. 31 October 2014.


    • Department of the Army, Headquarters. "The Army Vision - Strategic Advantage in a Complex World." 11 May 2015.


    • Department of the Army, United States Army Training and Doctrine Command. "The U.S. Army Functional Concept for Engagement." TRADOC Pamphlet 525-8-5. 24 Feb 2014.


    • Jean-Baptiste Michel, et al. "Quantitative Analysis of Culture Using Millions of Digitized Books." Science. 14 January 2011. Volume 331, no 6014 pp 176-182. DOI: 10.1126/science.1199644.


    • Kalev Leetaru. "Culturomics 2.0: Forecasting large-scale human behavior using global news media tone in time and space." First Monday. 5 September 2011. Volume 16, no 9.


    • Frederico Botta, et al. "Quantifying crowd size with mobile phone and Twitter data." Royal Society Open Science. 27 May 2015. DOI: 10.1098/rsos.150162.


    • Kalev Leetaru, et al. "Cultural computing at literature scale. Encoding the cultural knowledge of tens of billions of words of academic literature." D-Lib Magazine. September/October 2014. Volume 20, no 9/10. doi:10.1045/september2014-leetaru.


    • Joseph Kerski. "Geoawareness, Geoenablement, Geotechnologies, Citizen Science, and Storytelling: Geography on the World Stage." Geography Compass. 28 Jan 2015. Volume 9, Issue 1, pp 14-26. DOI: 10.1111/gec3.12193.


    • Maximilian Schich, et al. "A network framework of cultural history." Science. 1 August 2014. Volume 345, no 6196 pp 558-562. DOI: 10.1126/science.1240064.


    • Jonathon Kopecky, et al. "Social identity modeling: past work and relevant issues for socio-cultural modeling." Proceedings of the 19th Conference on Behavior Representation in Modeling and Simulation. 24 March 2010.


    • Rittel, H., and M. Webber. 1973. "Dilemmas in a General Theory of Planning," Policy Sciences. Volume 4, pp 155-159.


    • MG Andrew Mackay, et al. "The Effectiveness of US Military Information Operations in Afghanistan 2001-2010: Why RAND missed the point." Defence Academy of the United Kingdom ¨C Central Asia Series. Volume 12/02a. 14 December 2012.


    • Arturo Munoz. "U.S. Military Information Operations in Afghanistan - Effectiveness of Psychological Operations 2001-2010." RAND Monograph. 30 April 2012.


  • Blake E. Ashforth and Fred Mael. "Social Identity Theory and the Organization." Academy of Management Review. Volume 14, number 1, pp 20-39.

KEYWORDS: Human, Identity, Fusion, Analysis, Narrative, Human Geography, Social, Networks, Open Source, Sociocultural, ABI, Behavior, Modeling, Big Data

  • TPOC-1: Timothy K. Perkins
  • Phone: 843-754-4652
  • Email:
  • TPOC-2: Dr. Micheline K. Strand
  • Phone: 919-549-4343
  • Email:
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