A deeper look at the collective intelligence phenomenon

Pub Date : 2019-11-13 DOI:10.37380/jisib.v9i2.472
Klaus Solberg Søilen
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Abstract

For the upcoming conference on Intelligence Studies at ICI 2020 in Bad Nauheim, Germany the focus of this issue of JISIB is on collective intelligence and foresight. The first two papers by Søilen and Almedia and Lesca deal with collective intelligence from an intelligence studies perspective. It may be said that the Internet itself is a gigantic collective intelligence effort, the largest in human history. Open source is a prerequisite for this system to work for everyone. The article by Černý et al. is on open source. All other contributions are on the connection between the Internet, software and intelligence. This issue consists of seven articles to compensate for two articles that were taken out by editors in the last issue. The first article by Søilen entitled “Making sense of the collective intelligence field: a review” is a historical review of the field of collective intelligence. The paper shows how collective intelligence is an interdisciplinary field and argues there is a flaw in the notion of “wisdom of crowds”. Collective intelligence can be understood in terms of social systems theory and as such this approach has been fruitful for the social sciences, although so far not very popular. It also bares relevance for the study of business and economics. The second article by Almeida and Lesca is entitled “Collective intelligence process to interpret weak signals and early warnings”. Early warning and the detection of weak signals is a vital topic for any intelligence organization. Two aspects are discussed in the paper, the importance of new technology and collective sense making or interpretation The third article by Shaikh and Singhal entitled “Study on the various intellectual property management strategies used and implemented by ICT firms for business intelligence” deals with intellectual property rights and patenting strategies. The authors identify a number of defensive and offensive IP strategies applied to ICT companies. The results have a bearing on patent acquisitions. The fourth article by Lamrhari et al. is entitled “Web intelligence for understanding customer satisfaction: application of Latent Dirichlet Allocation (LDA) and the Kano model”. Customer satisfaction today is mostly measured with data from the internet, using different business intelligence techniques. The Kano model is still valuablei,ii, but the way we gather information to assess the different levels in the model has changed. The authors use Latent Dirichlet Allocation to analyze the voice of customer (VOC) in online reviews. They suggest that BI techniques and a fuzzy-Kano model can enable companies to better understand their customers’ online reviews. The fifth article by Nahili et al. is entitled “A new corpus-based convolutional neutral network for big data text analysis”. Companies need efficient ways to analyze everything that is said about them on the internet (reviews, comments). The paper suggests a convolutional neural network (CNN) as it has been successfully used for text classification. IMDB movie reviews and Reuters datasets were used for the experiment. The sixth article by Černý et al. is entitled “Using open data and google search data for competitive intelligence analysis”. Taking the Czech antidepressant market as an example, the authors show how competitive intelligence can be obtained using Google Search data, Google Trend and other OSINT sources. The seventh article by Dadkhah et al. is entitled “The potential of business intelligence tools for expert findings”. The paper suggests a way for researchers to find experts using business intelligence tools. The same method may also be used by any business or person looking for experts on a specific topic. As always, we would above all like to thank the authors for their contributions to this issue of JISIB. Thanks to Dr. Allison Perrigo for reviewing English grammar and helping with layout design for all articles and to the Swedish Research Council for continuous financial support. We hope to see you all at the ICI 2020 on the 16-17 March, 2020. The deadline for the two-page abstract submission is March 1st, 2020.
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深入研究集体智力现象
即将在德国巴德瑙海姆举行的ICI 2020智力研究会议上,JISIB的这一问题的重点是集体智慧和远见。Søilen和Almedia以及Lesca的前两篇论文从智力研究的角度探讨了集体智力。可以说,互联网本身就是一个巨大的集体智慧的成果,是人类历史上最大的。开源是这个系统为所有人工作的先决条件。Černý等人的文章是开源的。所有其他的贡献都是关于互联网、软件和智能之间的联系。这一期由七篇文章组成,以弥补上一期编辑删除的两篇文章。索伊伦的第一篇文章《理解集体智慧领域:回顾》是对集体智慧领域的历史回顾。这篇论文表明,集体智慧是一个跨学科领域,并认为“群体智慧”的概念存在缺陷。集体智慧可以用社会系统理论来理解,因此,这种方法对社会科学来说是卓有成效的,尽管到目前为止还不是很流行。它也与商业和经济学的研究相关。阿尔梅达和莱斯卡的第二篇文章题为“解读微弱信号和早期预警的集体智慧过程”。早期预警和微弱信号的检测是任何情报机构的重要课题。本文讨论了两个方面,新技术的重要性和集体意义的制定或解释。Shaikh和Singhal的第三篇文章题为“研究ICT公司在商业智能中使用和实施的各种知识产权管理策略”,涉及知识产权和专利策略。作者确定了一些适用于ICT公司的防御性和进攻性知识产权战略。研究结果对专利收购也有影响。Lamrhari等人的第四篇文章题为“用于理解客户满意度的Web智能:潜在狄利克雷分配(Latent Dirichlet Allocation, LDA)和卡诺模型的应用”。如今,客户满意度主要是通过互联网上的数据来衡量的,使用的是不同的商业智能技术。卡诺模型仍然有价值,ii,但我们收集信息来评估模型中不同层次的方式已经改变。本文采用潜在狄利克雷分配方法对在线评论中的顾客声音进行了分析。他们认为,商业智能技术和模糊卡诺模型可以使公司更好地了解客户的在线评论。Nahili等人的第五篇文章题为“一种新的基于语料库的卷积神经网络用于大数据文本分析”。公司需要有效的方法来分析互联网上关于他们的所有言论(评论、评论)。本文提出了卷积神经网络(CNN),因为它已经成功地用于文本分类。实验使用了IMDB电影评论和路透社数据集。Černý等人的第六篇文章题为“使用开放数据和谷歌搜索数据进行竞争情报分析”。以捷克抗抑郁药市场为例,作者展示了如何利用谷歌Search数据、谷歌Trend和其他OSINT来源获得竞争情报。Dadkhah等人的第七篇文章题为“专家发现的商业智能工具的潜力”。本文为研究人员提供了一种利用商业智能工具寻找专家的方法。同样的方法也可以用于任何企业或个人寻找特定主题的专家。与往常一样,我们首先要感谢作者对本期《伊斯兰支持组织》所作的贡献。感谢Allison Perrigo博士审阅英语语法并帮助设计所有文章的版面,感谢瑞典研究委员会持续的财政支持。我们希望在2020年3月16日至17日的ICI 2020上见到大家。提交两页摘要的截止日期为2020年3月1日。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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