Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach

Abstract

Many types of ecological or environmental problems would benefit from models based on people's knowledge. To create ecological models with both expert and local people's knowledge, a multi-step fuzzy cognitive mapping approach is proposed. A cognitive map can be made of almost any system or problem. Cognitive maps are qualitative models of a system, consisting of variables and the causal relationships between those variables. We describe how our cognitive mapping research has been used in real environmental management applications. This research includes examining the perceptions of different stakeholders in an environmental conflict, obtaining the perceptions of different stakeholders to facilitate the development of participatory environmental management plans, and determining the wants and desires for resettlement of people displaced by a large scale dam project. Based on our research, which involved six separate studies, we have found that interviewees complete their cognitive maps in 40-90 min on average. These maps contain an average of 23 ± 2 S.D. variables with 37 ± 3 S.D. connections. People generally put more forcing functions into their maps than utility variables. Fuzzy cognitive mapping offers many advantages for ecological modeling including the ability to include abstract and aggregate variables in models, the ability to model relationships which are not known with certainty, the ability to model complex relationships which are full of feedback loops, and the ease and speed of obtaining and combining different knowledge sources and of running different policy options.

Pathways for drought resilient livelihoods based on people’s perception

“…The main drawbacks of FCMs are: (i) the respondents' fallacies as misconceptions and biases get encoded in the maps (Özesmi and Özesmi 2004); (ii) FCM simulated results are relative and not real-value parameter estimates (Kim and Lee 1998;Özesmi and Özesmi 2004); (iii) they do not yield data with respect to a timeframe (Schneider et al 1998;Özesmi and Özesmi 2004); and (iv) require a large amount of post-processing time (Diniz et al 2015).…”

“…The main advantages that FCMs have over other tools like structural equation modelling, (SEM), systems dynamics model, and causal loop diagrams are: (i) their participatory nature that allow them to utilize the perception of stakeholders (Vliet et al 2009;Diniz et al 2015); (ii) they can model complex systems with many concepts-even those dealing with uncertainty (Kosko 1986;Özesmi and Özesmi 2004); (iii) they are not driven by data unavailability (Kok 2009;Özesmi and Özesmi 2004) but generate data on the other hand; (iv) they highlight and reveal the role of hidden and important feedbacks in the system (Kok 2009;Özesmi and Özesmi 2004); (v) they can easily integrate data from multiple sources and can divulge several diverging viewpoints (for example, they can use expert opinion as well as indigenous knowledge (Özesmi and Özesmi 2004)); and (vi) enable multiple policy simulations (Kok 2009;Özesmi and Özesmi 2004).…”

Livelihood vulnerability assessment to climate variability and change using fuzzy cognitive mapping approach

“…Each fuzzy cognitive map may be mathematically represented through adjacency matrices as A (D)=[a ij ] (Harary et al 1965;Özesmi and Özesmi 2004;Reckien et al 2010) where variable v i is listed in the vertical axis and the variables v j in the horizontal axis to form a square matrix. When a connection exists, a value between −1 and 1 is coded into the square matrix.…”

“…FCMs use fuzzy-graph structures that represent causal reasoning allowing for systematic causal propagation, particularly forward and backward chaining (Kosko 1986). FCMs consist of a set of nodes representing variables connected through links denoted by arrows (Harary et al 1965;Özesmi and Özesmi 2004). Each variable represents characteristics of a system while interconnections between the variables depict the dynamics of the system (Papageorgiou and Kontogianni 2012).…”

“…These polarities describe the arrangement of the system with the positive sign indicating a direct relationship and the negative sign an inverse one. Weights delineating the strength between variables, have been assigned values ranging between 0 and 1 (Özesmi and Özesmi 2004;Reckien et al 2010). FCM depicts interconnected relationships between variables and explains complex interactions occurring within a dynamic system.…”