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Open Access Highly Accessed Research article

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

Vijay K Mago1*, Hilary K Morden2, Charles Fritz3, Tiankuang Wu4, Sara Namazi15, Parastoo Geranmayeh5, Rakhi Chattopadhyay1 and Vahid Dabbaghian1

Author affiliations

1 The Modelling of Complex Social Systems (MoCSSy) Program, The IRMACS Centre, Simon Fraser University, Burnaby, Canada

2 Department of Criminology, Simon Fraser University, Burnaby, Canada

3 Department of Geography, Simon Fraser University, Burnaby, Canada

4 Department of Mathematics, Simon Fraser University, Burnaby, Canada

5 School of Computing Science, Simon Fraser University, Burnaby, Canada

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Citation and License

BMC Medical Informatics and Decision Making 2013, 13:94  doi:10.1186/1472-6947-13-94

Published: 23 August 2013

Abstract

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.

Keywords:
Homelessness; Complex social system; Fuzzy logic; Fuzzy Cognitive Map; Network analysis