Analyzing the impact of social factors on homelessness: a fuzzy cognitive map approach.
Analyzing the impact of social factors on homelessness: a fuzzy cognitive map approach.
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DOI：
10.1186/1472-6947-13-94
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发表时间：
2013-08-23
影响因子：
3.5
通讯作者：
Dabbaghian V
中科院分区：
文献类型：
--
作者：
Mago VK;Morden HK;Fritz C;Wu T;Namazi S;Geranmayeh P;Chattopadhyay R;Dabbaghian V
影响无家可归者的力量是复杂的，而且往往是相互作用的。上瘾、家庭破裂和精神疾病等社会力量，加上缺乏可用低成本住房、经济条件差和精神健康服务不足等结构性因素，使情况更加复杂。这些因素通过它们的动态关系共同影响无家可归的程度。历史模型本质上是静态的，但在捕捉这些关系方面只取得了轻微的成功。模糊逻辑(FL)和模糊认知图(FCM)特别适合于对无家可归等复杂社会问题进行建模，因为它们能够对经常用模糊概念描述的复杂、交互系统进行建模，然后将它们组织成社会科学家和其他人容易理解的特定、具体的形式(即FCM)。使用FL，我们转换了最近发表的同行评议文章中与无家可归相关的一组因素的信息，然后计算出这几对因素的影响强度(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个别或一组因素的影响。根据目前与无家可归有关的经验知识，这些试验的结果是可以解释的。由于与无家可归有关的概念的动态性质，以前的无家可归者图形地图的使用有限。与静态模型相比，FCM技术捕捉到了更大程度的动态化和复杂性，允许相关概念被操纵和交互。反过来，这又让无家可归的情况变得更加现实。通过对FCM的网络分析，我们确定教育在该模型中发挥了最大的作用，从而影响了无家可归等社会问题的动态化和复杂性。为模拟无家可归的复杂社会系统而建立的FCM合理地代表了所创建的样本场景的现实。这证实了该模型是有效的，搜索同行评议的学术文献是建立该模型的合理基础。此外，还确定了该地图所包含的概念之间关系的方向和强度是它们在现实中的行动的合理近似值。然而，动态模型并非没有其局限性，必须承认其本质上是探索性的。
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. 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. 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. 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.
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DOI：
10.1111/j.1530-0277.2007.00449.x
发表时间：
2007-09-01
影响因子：
3.2
作者：
Buu, Anne;Mansour, MaryAnn;Zucker, Robert A.
通讯作者：
Zucker, Robert A.
影响因子：
1.7
作者：
Chui, Wing Hong
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影响因子：
12
作者：
Faez, F.;Ghodsypour, S. H.;O'Brien, C.
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影响因子：
12.7
作者：
Conklin, TJ;Lincoln, T;Tuthill, RW
通讯作者：
Tuthill, RW
影响因子：
3.9
作者：
Fiorentine, R;Anglin, MD
通讯作者：
Anglin, MD