Social forecasting: a literature review of research promoted by the United States National Security System to model human behavior

FILETO CUERCI MACIEL, Rodrigo, MACEDO KERR PINHEIRO, Marta and BAYERL, Petra Saskia (2021). Social forecasting: a literature review of research promoted by the United States National Security System to model human behavior. Revista Brasileira de Ciências Policiais, 12 (4), 23-52.

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Official URL: https://periodicos.pf.gov.br/index.php/RBCP/articl...
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Abstract

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 is 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.

Item Type: Article
Additional Information: Published date taken from hidden metadata
Identification Number: 10.31412%2Frbcp.v12i4.612
Page Range: 23-52
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 16 Nov 2020 16:55
Last Modified: 12 Oct 2023 08:04
URI: https://shura.shu.ac.uk/id/eprint/27596

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