A fuzzy Cognitive Mapping Approach for Housing Affordability Policy Modelling
Introduction:
Considering the Fuzzy Cognitive Mapping potential to be used in the policy modeling, this paper applies Fuzzy Cognitive Map (FCM) in the field of housing, in order to help policy maker to decide the best policy in supporting the Housing Affordability. FCMs are capable of participative process, mapping, analysis, modeling, and scenarios in terms of significant events or factors, named concepts and their cause-effect relationships. Our approach is based on examining the perceptions of different stakeholders’ groups on housing affordability policy issues, in order to facilitate the development of a comprehensive housing policy modeling.
Within this process, We propose to quantify the subjective perceptions of the different stakeholder groups, using Fuzzy Cognitive Mapping FCM methodology, generally known as suitable tool for livelihood analysis. This paper presents a FCM approach used into FUPOL project (www.fupol.eu) financed by PPP Program. The FUPOL project proposes a comprehensive new governance model to support the design of complex policies and their implementation, to further advance the research and development in simulation, urban policy process modeling, semantic analysis, visualization and integration of those technologies.
Housing Affordability Policy Model:
It is well known that local officials in city governments need to develop a comprehensive housing policy to guide their current and future housing–related decisions in the context of a specific community that often face different issues. Access to affordable housing is an essential prerequisite for any community based on humanitarian principles.
Cognitive Maps and Fuzzy Cognitive Mapping:
Cognitive maps are qualitative models of a system, consisting of variables and the causal relationships between those variables. FCMs integrate the cognitive maps of accumulated experience and knowledge concerning the factors and the underlying causal relationships between factors of the modeled system. Kosko (1986) modified Axelrod’s cognitive maps, which were binary, by applying fuzzy causal functions with real numbers in [−1, 1] to the connections.
Modeling Methodology:
Description of Fuzzy Cognitive Mapping (FCMs):
Fuzzy Cognitive Mapping FCM are capable of modeling scenarios where nodes represent concepts, and edges represent causal links among the concepts. One of the most useful aspects of the FCM is its potential for use in decision support as a prediction tool. Given an initial state of a system, a FCM can simulate its evolution over time to predict its future behavior where the system converges to a point of a certain state of balance.
Selection of Factors and Causal Relations:
This subsection explains the factors used in the model of housing affordability to set later the causal relationships among the factors. Housing affordability policy means the ability to select the location and type of housing that a household can ‘afford’. An affordable house, whether rented or owned, is commonly defined as house (including taxes, insurance, and utilities), which does not cost more than a fixed percent (for example30%) of the gross income of a household.
Drawing Fuzzy Cognitive Mapping:
Transformation of the linguistic weights into fuzzy sets:
After the interviews, the Cognitive Maps are transformed into matrices in the form (Wij)ij (Khan and Quaddus 2004). The linguistic variables that describe each arc, for each interviewed are characterized by the fuzzy sets. The linguistic variables are combined, and the aggregated linguistic variable is transformed to a single linguistic weight, through the SUM technique (Lin and Lee 1996).
Conclusion:
Considering the Fuzzy Cognitive Mapping FCM’s potential to be used in the policy modeling, this paper explores how FCM can be applied to housing affordability policy. The advantage of such model is that it provides a better and more comprehensive understanding of citizen needs regarding housing affordability while it offers a way to involve stakeholders in participatory modeling. Even the FCMs can be used initially to evaluate behavior of the system and his equilibrium states, for further quantitative predictions of system behavior over time, other simulation methods may follow the analyses for visualization and provide as a feedback to stakeholder.
Also Read: Reducing Land Use Barriers to Affordable Housing PLANNING SERIES #10