The Modelling of Complex Social Systems (MoCSSy) Program, The IRMACS Centre, Simon Fraser University, Burnaby, Canada.
BMC Med Inform Decis Mak. 2013 Aug 23;13:94. doi: 10.1186/1472-6947-13-94.
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.
影响无家可归现象的因素复杂多样，且通常具有交互性。社会因素，如成瘾、家庭破裂和精神疾病，与缺乏可用的低成本住房、经济条件差和心理健康服务不足等结构性因素交织在一起。这些因素通过其动态关系共同影响无家可归现象的程度。历史模型本质上是静态的，只能在一定程度上捕捉到这些关系。
模糊逻辑（FL）和模糊认知图（FCM）特别适合于无家可归等复杂社会问题的建模，因为它们具有内在的能力，可以对复杂的、相互作用的系统进行建模，这些系统通常用模糊的概念术语来描述，然后将其组织成一个具体的、具体的形式（即 FCM），以便社会科学家和其他人能够理解。我们使用 FL 将从最近发表的同行评议文章中获取的与无家可归相关的一组特定因素的信息进行转换，然后计算出因素对之间的影响强度（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群体因素的效果。这些试验的结果可以根据与无家可归相关的现有实证知识来解释。
由于与无家可归相关的概念具有动态性，先前的无家可归图形地图的使用受到限制。FCM 技术比静态模型捕捉到更大程度的动态性和复杂性，允许相关概念被操纵和相互作用。这反过来又使无家可归的现实情况更加真实。通过对 FCM 的网络分析，我们确定教育在模型中发挥最大的作用，因此对无家可归等社会问题的动态性和复杂性产生影响。
为建模无家可归这一复杂社会系统而构建的 FCM 合理地反映了为创建的示例场景的现实情况。这证实了模型的有效性，并且对同行评议的学术文献的搜索是构建模型的合理基础。此外，还确定了该图中包含的概念之间的关系的方向和强度是对其在现实中的作用的合理近似。然而，动态模型并非没有其局限性，必须被视为具有内在探索性。
