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Analyzing the impact of social factors on homelessness: a Fuzzy Cognitive Map approach

机译:分析社会因素对无家可归者的影响:模糊认知图方法

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Background The forces which affect homelessness are complex and often interactive in nature. Social forces such as addictions, family breakdown, and mental illness are compounded by structural forces such as lack of available low-cost housing, poor economic conditions, and insufficient mental health services. Together these factors impact levels of homelessness through their dynamic relations. Historic models, which are static in nature, have only been marginally successful in capturing these relationships. Methods Fuzzy Logic (FL) and fuzzy cognitive maps (FCMs) are particularly suited to the modeling of complex social problems, such as homelessness, due to their inherent ability to model intricate, interactive systems often described in vague conceptual terms and then organize them into a specific, concrete form (i.e., the FCM) which can be readily understood by social scientists and others. Using FL we converted information, taken from recently published, peer reviewed articles, for a select group of factors related to homelessness and then calculated the strength of influence (weights) for pairs of factors. We then used these weighted relationships in a FCM to test the effects of increasing or decreasing individual or groups of factors. Results of these trials were explainable according to current empirical knowledge related to homelessness. Results Prior graphic maps of homelessness have been of limited use due to the dynamic nature of the concepts related to homelessness. The FCM technique captures greater degrees of dynamism and complexity than static models, allowing relevant concepts to be manipulated and interacted. This, in turn, allows for a much more realistic picture of homelessness. Through network analysis of the FCM we determined that Education exerts the greatest force in the model and hence impacts the dynamism and complexity of a social problem such as homelessness. Conclusions The FCM built to model the complex social system of homelessness reasonably represented reality for the sample scenarios created. This confirmed that the model worked and that a search of peer reviewed, academic literature is a reasonable foundation upon which to build the model. Further, it was determined that the direction and strengths of relationships between concepts included in this map are a reasonable approximation of their action in reality. However, dynamic models are not without their limitations and must be acknowledged as inherently exploratory.
机译:背景技术影响无家可归者的力量是复杂的,并且通常是互动的。诸如成瘾,家庭破裂和精神疾病等社会力量与缺乏可用廉价住房,经济条件恶劣以及精神卫生服务不足等结构性力量更加复杂。这些因素共同影响着无家可归者的动态关系。本质上是静态的历史模型在捕获这些关系方面仅取得了很小的成功。方法模糊逻辑(FL)和模糊认知图(FCM)特别适合于建模复杂的社会问题,例如无家可归者,因为它们固有的能力可以对复杂的交互式系统进行建模,这些系统通常以模糊的概念术语进行描述,然后将其组织为一种特定的,具体的形式(即FCM),社会科学家和其他人很容易理解。使用FL,我们从最近发表的同行评审文章中获取了与无家可归相关的一组因素的信息,然后计算了成对因素的影响力(权重)。然后,我们在FCM中使用这些加权关系来测试增加或减少单个或成组因素的影响。根据有关流浪者的最新经验知识,这些试验的结果是可以解释的。结果由于与无家可归相关的概念的动态性质,先前的无家可归图形地图用途有限。与静态模型相比,FCM技术可捕获更大程度的动态性和复杂性,从而可以操纵和交互相关概念。反过来,这可以使人们更加真实地了解无家可归者。通过对FCM的网络分析,我们确定教育在模型中发挥了最大作用,因此影响了诸如无家可归之类的社会问题的活力和复杂性。结论建立的FCM是为复杂的无家可归的社会系统建模而建立的,可以合理地代表所创建示例场景的现实。这证实了该模型的有效性,并且对同行评审的学术文献进行搜索是构建该模型的合理基础。此外,已确定此地图中包含的概念之间的关系的方向和强度是其实际作用的合理近似值。但是,动态模型并非没有局限性,必须承认其本质上是探索性的。

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