Four cognitive-ecological biases that reduce integration between medical and cyber intelligence and represent a threat to cybersecurity

Paolo Zucca
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引用次数: 1

Abstract

Cognitive biases are consistent and predictable mental errors caused by our simplified information processing strategies. Some cognitive-ecological biases have a negative and specific impact on the organization of Intelligence Communities and in particular on the mechanisms of integration of competencies between Medical and Cyber Intelligence, and they represent a serious threat to Cyber security. The spread of these systematic errors is practically ubiquitous and most, if not all, Intelligence analysts are at risk of error due to bias since it is a generalized phenomenon neither correlated with intelligence nor with other specific cognitive ability. The lack of exposure to the natural world of our species during the delicate phase of development only increases our propensity as a species for ecological destruction, generates a lack of knowledge about biological risks and amplifies the negative effects of these cognitive biases. We are not immune to evolutionary influence and since these biases have been present for a long time in our evolutionary history, it is very difficult to overcome them and implement "debiasing strategies". A potential "debiasing" model organization based on the competence’s integration between Medical and Cyber Intelligence is proposed. The key role within this model is represented by the “Symbiont or Cybiont”. This figure will be able to utilize the computer network as a mean for rapid communications, storage and retrieval of large bodies of knowledge. This augmented knowledge will be used also for reducing human’s ecological impact on nature and improving the debiases strategies of the Intelligence Communities.

四种认知生态偏差降低了医疗和网络智能之间的整合,并对网络安全构成威胁
认知偏差是由我们简化的信息处理策略引起的一致和可预测的心理错误。一些认知生态偏差对情报界的组织产生了负面和特定的影响,特别是对医疗和网络智能之间能力整合的机制产生了负面影响,它们对网络安全构成了严重威胁。这些系统性错误的传播实际上是无处不在的,大多数(如果不是全部的话)情报分析人员由于偏见而面临错误的风险,因为这是一种普遍现象,既与智力也与其他特定的认知能力无关。在人类发展的微妙阶段,缺乏与自然世界的接触只会增加我们作为一个物种的生态破坏倾向,导致我们缺乏对生物风险的知识,并放大这些认知偏见的负面影响。我们并非不受进化的影响,由于这些偏见在我们的进化史上已经存在了很长时间,因此很难克服它们并实施“去偏见策略”。提出了一种基于医疗智能与网络智能能力整合的潜在“去偏”模型组织。该模型中的关键角色由“共生体或Cybiont”表示。这个数字将能够利用计算机网络作为快速通信、储存和检索大量知识的手段。这种增强的知识也将用于减少人类对自然的生态影响,并改善情报界的消除偏见策略。
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来源期刊
Forensic science international. Animals and environments
Forensic science international. Animals and environments Pollution, Law, Forensic Medicine, Veterinary Science and Veterinary Medicine (General)
CiteScore
2.00
自引率
0.00%
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0
审稿时长
142 days
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