The forces that affect the homeless are complex and often interactive. Social forces such as addiction, family breakdown and mental illness are exacerbated by structural forces such as the lack of affordable housing, poor economic conditions and inadequate mental health services. These factors work together to influence the level of homelessness through dynamic relationships. Historical models are static in nature and have had only marginal success in capturing these relationships.


Fuzzy logic (FL) and fuzzy cognitive map (FCMs) is particularly suited for modeling of complex social problems, such as homelessness because they have the ability to complex system modeling, interactive, these systems are usually described in terms of the concept of fuzzy, then organize them into a specific, concrete forms (for example, social scientists and others are easy to understand. Using FL, we converted information from recently published peer-reviewed papers on a set of factors related to homelessness and then calculated the magnitude (weight) of the impact on those factors. We then used these weighting relationships in FCM to test the effects of increasing or decreasing individual or group factors. The results of these trials are interpretable, based on current empirical knowledge about homelessness.

The results

Because of the dynamic nature of the concepts related to homelessness, previous diagrams of homelessness have been of limited use. FCM technology achieves a greater degree of dynamism and complexity than static models, allowing for manipulation and interaction with related concepts. This, in turn, provides a more realistic picture of homelessness. Through network analysis of FCM, we determined that education plays the biggest role in this model, thus influencing the dynamic and complexity of social issues such as homelessness.



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