【作者】 陈彬；

【导师】 沈固朝；

【作者基本信息】 南京大学 ， 情报学， 2014， 硕士

【摘要】 随着情报学的不断发展和完善,尤其是在安全情报日益成为情报学研究热点的趋势下,“大情报观”的思想日益得到广大学者的认同和接受,不同领域的情报研究方法相互借鉴和吸收成为必然的趋势。假设方法在社会科学和自然科学的研究中都经过了长期的实践检验,并获得成功。为了提升情报分析的效率和技巧,必须不断加强学习和吸收其他领域的研究思想和方法。假设是情报分析工作的必需品,对情报分析工作有着基础性的决定作用。研究假设在情报分析应用中的一般模式、方法对更好地发挥假设性思维的作用,提升情报分析工作的水平具有较强的现实意义和应用价值。假设性思维如何指导情报工作的开展,如何在情报分析中发挥作用,是值得情报界研究的重要问题。目前国内外情报学界对假设性思维的应用研究是分散的,缺乏系统性,或者是简单进行思想的介绍,或者是介绍某一种假设方法的应用,或者是从某一领域的视角探讨假设性思维运用的流程,尚未有学者归纳出假设性思维在情报分析中应用的一般模式,未能根据不同情况梳理相关方法的适用范围,因此,本文希望能够在假设性思维在情报分析中应用这一主题方面做出一些探讨。本文结合国内外相关研究成果,包括假设在其他社会科学和自然科学领域的实践应用,在假设理论、认知学理论和系统理论等理论基础的指导下,对假设性思维的应用模式进行了归纳分析,将其分为假设生成、假设验证和情报预测三个阶段。在假设生成阶段,对建立假设的思维方法、假设评估和假设的类型进行了分析；在假设验证和情报预测阶段,对相关方法进行了对比分析,阐述了各方法的适用范围和优缺点；最后,以缉私情报为例,对假设性思维在情报分析中的应用进行实证研究。通过具体分析,进行假设验证和情报预测方法的选择和应用,通过简单假设——ACH-AM组合假设验证——组合预测(GM(1,1)预测法、BP神经网络预测法和三次指数平滑法)展示了完整的假设性思维应用过程。本文的创新点主要包括以下几个方面：(1)研究角度新,即将假设性思维系统引入情报分析过程中,建立了假设性思维应用的一般过程,即假设生成、假设验证和情报预测,引入了包括诊断式推理等在国内尚未广泛使用的方法；(2)研究方法新,本文在撰写过程中注意引入了很多跨学科的方法和工具,例如采用了系统分析、认知学分析、哲学分析、理论研究与实证研究相结合、定性与定量相结合等多种方法,在计算灰色关联度过程中使用了南京航空航天大学经济与管理学院刘斌等研发的基于GUI的灰色建模系统,计算贝叶斯网络则选用matlab下的贝叶斯网络工具包,计算三次平滑指数则运用spss的时间序列分析过程。(3)研究内容新,本文在情报分析过程中主要偏向于安全情报领域的应用,而传统情报学研究中很少关注面向安全的情报学研究,例如本文实证研究中涉及的缉私情报分析。更多还原

【Abstract】 With the continuous development and improvement of information science, especially in the context of that security intelligence has increasingly become a hot topic in information science, great intelligence view has been widely accepted by the majority of scholars.and intelligence research methods in different fields are in a process of mutual learning and assimilation. Through a long-term practice, hypothesis based method has achieved resounding success in the fields of social science and natural science. In order to improve the efficiency and skills of intelligence analysis, we must strengthen learning and assimilation to research ideas and methods of other fields. Assumption exerts basic influence on intelligence analysis work. In this paper, an application of assumption to intelligence analysis is studied. It plays an important role in exerting hypothetical thinking’s force and improving the efficiency and skills of intelligence analysis. It’s an important research work to us that how hypothetical thinking guides the intelligence service and how hypothetical thinking strengthens intelligence analysis.The topic that intelligence community’s study to application of hypothetical thinking is scattered and lack of systematicness, includes introduction of idea or hypothesis methodology’s application, or discussion of application flow of hypothetical thinking in different optic angle. The common application flow of hypothetical thinking’s application in intelligence analysis has not been studied, and application scope of related methods isn’t clear. Therefore, the object of this paper is to study common application flow of hypothetical thinking’s application in intelligence analysis.Based on the related research results at home and abroad, including application of hypothesis methodology in social science and natural science, the common application flow of hypothetical thinking is divided into three phases under the guidance of theoretical basis as hypothesis theory, cognitive sciences and system theory. The three phases are hypothesis generation, hypothesis verification and intelligence prediction. In the phase of hypothesis generation, thinking processes, hypothesis assumption and forms are studied. In the phase of hypothesis verification and intelligence prediction, contrastive analysis on related methods and introduction of scope, advantages and disadvantages are exerted. In the end, this paper made an empirical research on hypothetical thinking’s application in intelligence analysis by taking anti-smuggling intelligence analysis as an example. Through making a concrete analysis, this paper exerted the choice and application of hypothesis verification and intelligence prediction. An completed application flow is revealed by simple hypothesis, combined ACH-AM hypothesis verification and combined forecast(GM(1,1), back propagation and three exponential smoothing model).更多还原