Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management

Mourhir, Asmaa

doi:10.1016/j.envsoft.2020.104891

Cited by 25 publications

(13 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This point is emphasized in Section 4. We check for the two termination conditions (lines 10-14) and, if they are not satisfied, we continue by applying crossover (lines [16][17][18][19][20] and mutations (lines 24-31) before calculating the fitness again (lines 24-31). Weight matrices are selected for the next generation (lines 32-35) and we repeat the process (line 16) until the termination conditions are met.…”

Section: Proposed Algorithm To Generate An Individual Fcmmentioning

confidence: 99%

“…A Fuzzy Cognitive Map (FCM) is an aggregate computational model consisting of factors (weighted labelled nodes), which interact via causal links (directed weighted edges). This approach has been used across a broad range of domains [5], ranging from medical applications [1] to socio-environmental systems [18] and engineering [2]. Many of these applications are motivated by the need to make decisions in complex systems characterized by high uncertainty, feedback loops, and limited access to the detailed temporal datasets (e.g., delays, rates per unit of time) that would support alternative approaches such as System Dynamics (SD).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data

Wozniak¹,

Mkhitaryan²,

Giabbanelli³

2022

Preprint

View full text Add to dashboard Cite

Fuzzy Cognitive Maps (FCMs) are computational models that represent how factors (nodes) change over discrete interactions based on causal impacts (weighted directed edges) from other factors. This approach has traditionally been used as an aggregate, similarly to System Dynamics, to depict the functioning of a system. There has been a growing interest in taking this aggregate approach at the individual-level, for example by equipping each agent of an Agent-Based Model with its own FCM to express its behavior. Although frameworks and studies have already taken this approach, an ongoing limitation has been the difficulty of creating as many FCMs as there are individuals. Indeed, current studies have been able to create agents whose traits are different, but whose decision-making modules are often identical, thus limiting the behavioral heterogeneity of the simulated population. In this paper, we address this limitation by using Genetic Algorithms to create one FCM for each agent, thus providing the means to automatically create a virtual population with heterogeneous behaviors. Our algorithm builds on prior work from Stach and colleagues by introducing additional constraints into the process and applying it over longitudinal, individual-level data. A case study from a real-world intervention on nutrition confirms that our approach can generate heterogeneous agents that closely follow the trajectories of their real-world human counterparts. Future works include technical improvements such as lowering the computational time of the approach, or case studies in computational intelligence that use our virtual populations to test new behavior change interventions.

show abstract

Section: Proposed Algorithm To Generate An Individual Fcmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data

Wozniak¹,

Mkhitaryan²,

Giabbanelli³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Despite the suitability of both FCMs and BBNs in addressing similar issues-for example, environmental resource management (Phan et al, 2016;Mourhir, 2021) and risk management (Kabir and Papadopoulos, 2019;Tepes and Neumann, 2020;Kaikkonen et al, 2021)-they have been mostly applied separately even in similar issues, and very few works aimed at their combination Shenoy, 2001, 2004;Azar and Dolatabad, 2019), with pros and cons thoroughly described in Azar and Dolatabad (2019). In such cases, FCMs are mainly used for defining the causal structure of the BBN model, and a procedure is required to convert the FCM into the causal model of BBNs by making some structural changes to the map.…”

Section: Developing the Stakeholder-based Bayesian Belief Networkmentioning

confidence: 99%

Bayesian Belief Networks for Integrating Scientific and Stakeholders’ Knowledge to Support Nature-Based Solution Implementation

et al. 2021

View full text Add to dashboard Cite

There is a growing interest worldwide on the potential of nature-based solutions (NBSs) as measures for dealing with water-related risks while producing multiple co-benefits that can contribute to several societal challenges and many of the sustainable development goals. However, several barriers still hamper their wider implementation, such as mainly the lack of stakeholders’ engagement and the limited integration of stakeholders’ knowledge throughout the phases of NBS design and implementation. This is a crucial aspect to guarantee that the multidimensional implications of NBSs are adequately understood and considered by decision-makers. Innovative methods and tools for improving NBS design and supporting decision-makers in overcoming the main barriers to implementation, ultimately enhancing their effectiveness, are therefore needed. The present work proposes a combined approach based on the integration of fuzzy cognitive maps, hydraulic modeling, and participatory Bayesian belief networks aiming to facilitate the stakeholders’ engagement and the knowledge integration process in NBS design and assessment. The approach was developed and implemented within the NAIAD project in the Lower Danube demo site, specifically oriented to support the process of the Potelu Wetland restoration. First, fuzzy cognitive maps are adopted as a problem structuring method for eliciting stakeholders’ risk perception and problem understanding, and for constructing a causal model describing the system as a whole, with specific attention to the expected role of the NBS in reducing flood risk and addressing the key local challenges. Second, hydraulic modeling is used to analyze the effect of extreme floods starting from the retrospective analysis of a specific event and to model the potential benefits of risk reduction measures. Last, a Bayesian belief network is used to support the model integration process and a scenario analysis with a user-friendly tool. The whole process can be replicated in other areas and is particularly suitable to support an active engagement of stakeholders (both institutional and not) in the process of NBS design and assessment.

show abstract

“…In many mental model studies, participants generate their own concepts while constructing their cognitive map ( Henly-Shepard et al, 2015 ). Without a fixed set of concepts, participants’ freely associated concepts need to be homogenized to resolve variations in language ( Mourhir, 2020 ). This is often done qualitatively by classifying the concepts into overarching categories and using these categories as nodes in mental model network analysis ( Özesmi and Özesmi, 2004 ; Olazabal et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers may also need to judge the redundancy of concepts and eliminate those that are perceived to be synonyms or align concepts with opposite directions of the same concept ( Vasslides and Jensen, 2016 ). Such a process is resource-intensive ( Mourhir, 2020 ) and sensitive to the researcher’s influence. Hence, some studies employ a two-step approach in which the mental model concepts are first generated through interviews or the literature reviews.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Application of the Mental Model Mapping Tool (M-Tool)

et al. 2021

View full text Add to dashboard Cite

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool’s application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool’s convergent validity compared to standard interviewing methods (n = 30). Study 2 investigated M-Tool’s construct validity by relating mental model complexity to participants’ education level (n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups.

show abstract

Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management

Cited by 25 publications

References 100 publications

Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data

Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data

Bayesian Belief Networks for Integrating Scientific and Stakeholders’ Knowledge to Support Nature-Based Solution Implementation

Evaluating the Application of the Mental Model Mapping Tool (M-Tool)

Contact Info

Product

Resources

About