SOCIAL FORECASTING: A literature review of research promoted by the United States National Security System to model human behavior

Conteúdo do artigo principal

Rodrigo Fileto Cuerci Maciel
Marta Macedo Kerr Pinheiro
Petra Saskia Bayerl


The development of new information and communication technologies increased the volume of information flows within society. For the security forces, this phenomenon presents new opportunities for collecting, processing and analyzing information linked with the opportunity to collect a vast and diverse amount data, and at the same time it requires new organizational and individual competences to deal with the new forms and huge volumes of information. Our study aimed to outline the research areas funded by the US defense and intelligence agencies with respect to social forecasting. Based on bibliometric techniques, we clustered 2688 articles funded by US defense or intelligence agencies in five research areas: a) Complex networks, b) Social networks, c) Human reasoning, d) Optimization algorithms, and e) Neuroscience. After that, we analyzed qualitatively the most cited papers in each area. Our analysis identified that the research areas are compatible with the US intelligence doctrine. Besides that, we considered that the research areas could be incorporated in the work of security forces provided that basic training be offered. The basic training would not only enhance capabilities of law enforcement agencies but also help safeguard against (unwitting) biases and mistakes in the analysis of data.


Não há dados estatísticos.

Detalhes do artigo

Como Citar
MACIEL, R. F. C.; KERR PINHEIRO, M. M.; BAYERL, P. S. SOCIAL FORECASTING: A literature review of research promoted by the United States National Security System to model human behavior. Revista Brasileira de Ciências Policiais, Brasília, Brasil, v. 12, n. 4, p. 23–52, 2021. DOI: 10.31412/rbcp.v12i4.612. Disponível em: Acesso em: 29 fev. 2024.
Biografia do Autor

Rodrigo Fileto Cuerci Maciel, Polícia Federal do Brasil

Rodrigo Fileto C. Maciel is a Brazilian police officer with experience in intelligence analysis at the tactical and strategic level. He has just finished his PhD in Information Science at the Universidade Federal de Minas Gerais. His research interests lie at the intersection of social and technological networks, innovation systems and security issues.

Marta Macedo Kerr Pinheiro, Escola de Ciência da Informação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brasil; Sistema de Informação e Gestão do Conhecimento, Universidade FUMEC, Belo Horizonte, Minas Gerais, Brasil

Marta Kerr Pinheiro is professor of Information Science at UFMG and FUMEC. Her research interests are focused on Information Policy, Informational state, intelligence and governmental knowledge.

Petra Saskia Bayerl, Centre of Excellence in Terrorism, Resilience, Intelligence and Organised Crime Research), Sheffield Hallam University, Sheffield, United Kingdom

Saskia Bayerl is Professor of Digital Communication and Security at Sheffield Hallam University. Her research interests lay at the intersection of human-computer interaction, organisational communication, and organisational change with a special focus on ICT implementation, privacy, and the management of transparenc


ALANYALI, M.; MOAT, H. S.; PREIS, T. Quantifying the relationship between financial news and the stock market. Scientific Reports, v. 3, p. 1–6, 2013.

ANTUNES, P. C. B. SNI & ABIN: Entre a teoria e a prática: uma leitura da atuação dos Serviços Secretos. Rio de Janeiro: FGV editora, 2002.

ARENAS, A. et al. Modeling human mobility responses to the large-scale spreading of infectious diseases. Scientific Reports, v. 1, n. 1, p. 1–7, 2011.

AYAZ, H. et al. Optical brain monitoring for operator training and mental workload assessment. NeuroImage, v. 59, n. 1, p. 36–47, 2012. available at: <>.

BARRETT, L. F.; SATPUTE, A. B. Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain. Current Opinion in Neurobiology, v. 23, n. 3, p. 361–372, 2013. available at: <>.

BASSETT, D. S. et al. Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences, v. 108, n. 18, p. 7641–7646, maio 2011. available at: <>.

BRITO, V. D. P. Poder Informacional e desinformação. 2015. Universidade Federal de Minas Gerais, 2015.

CAPURRO, R. Epistemología y ciencia de la información. Enl@ce: Revista Venezolana de Información, Tecnología y Conocimiento, v. 4, n. 1, p. 11–29, 2007. available at: <>.

CHEN, J. Y.; BARNES, M. J.; HARPER-SCIARINI, M. Supervisory control of multiple robots: Human-performance issues and user-interface design. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, v. 41, n. 4, p. 435–454, 2011.

CHOO, C. W. A organização do conhecimento. São Paulo: Editora Senac, 2003.

CLARK, R. M. Intelligence Analysis: a target-centric approach. 2. ed. Washington, DC: CQ Press, 2006.

COLE, M. W.; PATHAK, S.; SCHNEIDER, W. Identifying the brain’s most globally connected regions. NeuroImage, v. 49, n. 4, p. 3132–3148, 2010. available at: <>.

CORNELIUS, I. Theorizing information for information science. Annual Review of Information Science and Technology, v. 36, n. 1, p. 392–425, 2005. available at: <>.

CURME, C. et al. Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Scientific Reports, v. 3, n. 1, p. 1–5, 2013.

DEVILLE, P. et al. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, v. 111, n. 45, p. 15888–15893, 2014. available at: <>.

DING, C. et al. Collaborative sensing in a distributed PTZ camera network. IEEE Transactions on Image Processing, v. 21, n. 7, p. 3282–3295, 2012.

ECK, N. J. van; WALTMAN, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, v. 84, n. 2, p. 523–538, ago. 2010. available at: <>.

ECK, N. J. van; WALTMAN, L. Text mining and visualization using VOSviewer. Text Mining and Visualization, p. 1–5, set. 2011. available at: <>.

EZELL, B. et al. Probabilistic risk analysis and terrorism risk. Improving Homeland Security Decisions, v. 30, n. 4, p. 5–31, 2017.

FONSECA, G. D.; LASMAR, J. M. Passaporte para o terror: os voluntários do Estado Islâmico. Curitiba: Appris editora, 2017.

FORTUNATO, S.; HRIC, D. Community detection in networks: A user guide. Physics Reports, v. 659, p. 1–44, 2016. available at: <>.

GAO, J. et al. Robustness of a network of networks. Physical Review Letters, v. 107, n. 19, 2011.

GARCIA, P. et al. Live Transference of Surgical Subspecialty Skills Using Telerobotic Proctoring to Remote General Surgeons. Journal of the American College of Surgeons, v. 211, n. 3, p. 400–411, 2010. available at: <>.

GOODCHILD, M. F.; GLENNON, J. A. Crowdsourcing geographic information for disaster response: A research frontier. International Journal of Digital Earth, v. 3, n. 3, p. 231–241, 2010.

HANCOCK, P. A. et al. A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, v. 53, n. 5, p. 517–527, 2011.

HAVLIN, S. et al. Catastrophic Cascade of Failures in Interdependent Networks. Nature, v. 464, n. 7291, p. 1025–1028, dez. 2010. available at: <>.

HEUER JR, R. J. Psychology of intelligence analysis. Central Intelligence Agency, 1999.

HOSSEIN MANSHAEI, M.; ZHU, Q. Game Theory Meets Network Security and Privacy. v. 45, n. 3, p. 1-39, 2013.

HUANG, I. B.; KEISLER, J.; LINKOV, I. Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of the Total Environment, v. 409, n. 19, p. 3578–3594, 2011. available at: <>.

HUANG, X. et al. Robustness of interdependent networks under targeted attack. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, v. 83, n. 6, p. 1–11, 2011.

JAIN, M. et al. Software assistants for randomized patrol planning for the lax airport police and the Federal Air Marshal Service. Interfaces, v. 40, n. 4, p. 267–290, 2010.

JONES, M.; LOVE, B. C. Bayesian fundamentalism or enlightenment? on the explanatory status and theoretical contributions of bayesian models of cognition, 2011..

KASSIN, S. M.; DROR, I. E.; KUKUCKA, J. The forensic confirmation bias: Problems, perspectives, and proposed solutions. Journal of Applied Research in Memory and Cognition, v. 2, n. 1, p. 42–52, 2013. available at: <>.

KENT, S. Strategic intelligence for American world policy. 2nd ed. New Jersey: Princeton University Press, 1966.

KENT, S. Sherman Kent and the Board of National Estimates. Washington, DC: Central Intelligence Agency, 1994.

KOBER, J.; BAGNELL, J. A.; PETERS, J. Reinforcement Learning in Robotics: A Survey. In: Springer Tracts in Advanced Robotics. 97p. 9–67.

KONIDARIS, G. et al. Robot learning from demonstration by constructing skill trees. International Journal of Robotics Research, v. 31, n. 3, p. 360–375, 2012.

KORZHYK, D. et al. Stackelberg vs. nash in security games: An extended investigation of interchangeability, equivalence, and uniqueness. Journal of Artificial Intelligence Research, v. 41, p. 297–327, 2011.

LI, N.; MARDEN, J. R. Designing games for distributed optimization. IEEE Journal on Selected Topics in Signal Processing, v. 7, n. 2, p. 230–242, 2013.

LINKOV, I. et al. Weight-of-evidence evaluation in environmental assessment: Review of qualitative and quantitative approaches. Science of the Total Environment, v. 407, n. 19, p. 5199–5205, 2009. Disponível em: <>.

LUM, M. J. H. et al. The RAVEN: Design and validation of a telesurgery system. International Journal of Robotics Research, v. 28, n. 9, p. 1183–1197, 2009.

MACIEL, R. F., BAYERL, P. S., & KERR PINHEIRO, M. M. (2019). Technical research innovations of the US national security system. Scientometrics, 120(2), 539–565.

MILLER, J. C. Percolation and epidemics in random clustered networks. Physical Review E, v. 80, n. 2, p. 020901, ago. 2009. available at: <>.

MORRIS, S. A. et al. Time line visualization of research fronts. Journal of the American Society for Information Science and Technology, v. 54, n. 5, p. 413–422, 2003.

Mowery, D. C. (2012). Defense-related R&D as a model for “grand Challenges” technology policies. Research Policy, v. 41, n. 10, p. 1703–1715. Elsevier B.V.

NATIONAL ACADEMIES OF SCIENCES, ENGINEERING; MEDICINE. Social and Behavioral Sciences for National Security. Washington, D.C.: National Academies Press, 2017.

NATIONAL RESEARCH COUNCIL. New Research Directions for the National Geospatial-Intelligence Agency. Washington, D.C.: National Academies Press, 2010.

NATIONAL RESEARCH COUNCIL. Intelligence Analysis for Tomorrow. Washington, D.C.: National Academies Press, mar. 2011.. available at: <>.

NATURE. Complex networks, 2019.. available at: <>.

NONAKA, I.; TAKEUCHI, H. Teoria da criação do conhecimento organizacional. In: TAKEUCHI, H.; NONAKA, I. (Ed.). Gestão do Conhecimento. Porto Alegre: Bookman, 2008. p. 54–90.

OECD. (2020). Main science and technology indicators. Paris: OECD.

OFFICE OF THE DIRECTOR OF NATIONAL INTELLIGENCE - IARPA. Research programs, 2016.. available at: <>. Acesso em: 5 out. 2016.

ONNELA, J. P. et al. Geographic constraints on social network groups. PLoS ONE, v. 6, n. 4, 2011.

PEEL, L.; LARREMORE, D. B.; CLAUSET, A. The ground truth about metadata and community detection in networks. Science Advances, v. 3, n. 5, p. e1602548, maio 2017. available at: <>.

PITA, J. et al. Using Game Theory for Los Angeles Airport Security. AI Magazine, v. 30, n. 1, p. 43, 2009.

POOR, H. V. et al. Self-Organization in Small Cell Networks: A Reinforcement Learning Approach. IEEE Transactions on Wireless Communications, v. 12, n. 7, p. 3202–3212, 2013.

PREIS, T.; MOAT, H. S.; EUGENE STANLEY, H. Quantifying trading behavior in financial markets using google trends. Scientific Reports, v. 3, p. 1–6, 2013.

PROPHETS. Preventing Radicalisation Online through the Proliferation of Harmonised ToolkitS, 2018. available at: <>. Acesso em: 20 mar. 2019.

SCHNEIDER, C. M. et al. Mitigation of Malicious Attacks on Networks. v. 108, n. 10, p. 3838–3841, 2011. available at: <>.

SIMINI, F. et al. A universal model for mobility and migration patterns. Nature, v. 484, n. 7392, p. 96–100, 2012.

SMITH, R.; PATEL, V.; SATAVA, R. Fundamentals of robotic surgery: a course of basic robotic surgery skills based upon a 14-society consensus template of outcomes measures and curriculum development. The International Journal of Medical Robotics and Computer Assisted Surgery, v. 10, n. 3, p. 379–384, set. 2014. available at: <>.

SONG, C. et al. Modelling the scaling properties of human mobility. Nature Physics, v. 6, n. 10, p. 818–823, 2010a. available at: <>.

SONG, C. et al. Limits of Predictability in Human Mobility. Science, v. 327, n. 5968, p. 1018–1021, fev. 2010b. available at: <>.

TEGLAS, E. et al. Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference. Science, v. 332, n. 6033, p. 1054–1059, maio 2011. available at: <>.

TENENBAUM, J. B. et al. How to Grow a Mind: Statistics, Structure, and Abstraction. Science, v. 331, n. 6022, p. 1279–1285, mar. 2011. available at: <>.

UNITED STATES INTELLIGENCE COMMUNITY. 100 Day PlanWashington, DCOffice of the Director of National Intelligence,, 2007.. available at: < and Pubs/100_Day_Plan.pdf>.

VAGVOLGYI, B. P. et al. Augmented Reality During Robot-assisted Laparoscopic Partial Nephrectomy: Toward Real-Time 3D-CT to Stereoscopic Video Registration. Urology, v. 73, n. 4, p. 896–900, 2009. available at: <>.

XIE, J.; KELLEY, S.; SZYMANSKI, B. K. Overlapping Community Detection in Networks: the State of the Art and Comparative Study. ACM Computing Surveys, v. 45, n. 4, p. 1–37, 2011. available at: <>.