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IDEAS Factory - Detecting Terrorist Activities: Shades of Grey - Towards a Science of Interventions for Eliciting and Detecting Notable Behaviours

IDEAS Factory - 检测恐怖活动：灰色阴影 - 迈向引发和检测显着行为的干预科学

基本信息

批准号：
EP/H02302X/1
负责人：
Matt Jones
金额：
$ 133.83万
依托单位：
Swansea University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FH02302X%2F1
关键词：
IDEAS Factory Detecting Terrorist Activities

项目摘要

The Shades of Grey (SoG) research project is designed in response to a pressing need for novel surveillance interventions that elicit robust, reliable and usable indicators of notable behaviours in public areas and ports of entry. From a research perspective, this translates into a need for an active research paradigm to detect notable behaviours more effectively: a science of interventions. The project draws upon expertise from behavioural psychology, social and physical sciences, and engineering to define, design, and deliver a science of interventions aimed at improving our understanding of the relationship between environmental and interpersonal stimuli and behavioural responses. Using scientific principles, this will be brought about via the design of controlled laboratory and field experiments to empirically test the effectiveness of a suite of interventions that are designed to aid practical, real-time identification of factors (and combinations thereof) that aid detection of notable behaviours. This project aims to address these needs in three ways. First, it aims to develop a sophisticated palate of interventions that will amplify the signal-to-noise ratios of notable to normal activities. This is based on the premise that nuanced manipulations of social or physical contexts will render intent more visible by eliciting particular responses from individuals. This will address the limitations of passive interventions by enabling intent to become more conspicuous and, by encouraging self-selection, reduce the potential for false positives whilst overcoming the potential social costs of many actuarial profiling models. Secondly, most current techniques - such as CCTV - present greatest utility only when reassembling information after incidents. SoG will seek to apply its interventions at an earlier stage, for example, intercepting individuals during anticedent events such as target selection or phases of planning. Addressing current uncertainties surrounding the most appropriate timing and location for interventions, SoG will offer guidance on how interventions link together and become optimized based on robust empirical enquiry. This will assist the optimal allocation of scarce resources by better understanding where and when interventions should take place in order to maximise efficiency. Third, SoG is committed to designing interventions that can map onto and augment exiting strategies and operational environments.

灰色阴影（SoG）研究项目是为了应对对新的监测干预措施的迫切需求而设计的，这些干预措施可以在公共区域和入境口岸获得强大，可靠和可用的显着行为指标。从研究的角度来看，这意味着需要一个积极的研究范式，以更有效地发现值得注意的行为：干预科学。该项目借鉴了行为心理学，社会和物理科学以及工程学的专业知识，以定义，设计和提供干预科学，旨在提高我们对环境和人际刺激与行为反应之间关系的理解。利用科学原理，这将通过设计受控实验室和现场实验来实现，以经验性地测试一套干预措施的有效性，这些干预措施旨在帮助实际，实时识别有助于检测显着行为的因素（及其组合）。该项目旨在通过三种方式满足这些需求。首先，它旨在开发一种复杂的干预手段，以放大显著活动与正常活动的信噪比。这是基于这样一个前提，即对社会或物理环境的细微操纵将通过引起个人的特定反应而使意图更加明显。这将解决被动干预的局限性，使意图变得更加明显，并通过鼓励自我选择，减少假阳性的可能性，同时克服许多精算分析模型的潜在社会成本。第二，目前的大多数技术-如闭路电视-只有在事件发生后重新收集信息时才能发挥最大的效用。SoG将寻求在早期阶段实施干预措施，例如，在目标选择或规划阶段等前期事件期间拦截个人。为了解决目前围绕干预措施的最佳时机和地点的不确定性，SoG将提供指导，说明干预措施如何结合在一起，并根据强有力的经验调查进行优化。这将有助于优化稀缺资源的分配，更好地了解应在何时何地采取干预措施，以实现效率最大化。第三，SoG致力于设计能够映射和增强现有战略和运营环境的干预措施。