Modelling socio-ecological systems: Implementation of an advanced Fuzzy Cognitive Map framework for policy development for addressing complex real-life challenges

This study implements a novel Fuzzy Cognitive Map (FCM) framework for addressing large complex socio-ecological problems. These are characterized as qualitative, dominated by uncertainty, human involvement with different and vague perceptions/expectations, and complex systems dynamics due to feedback relations. The FCM framework provides a participatory soft computing approach to develop consensus solutions. We demonstrate its implementation in a case study: a national-scale acute water scarcity crisis. The model has eight steps starting from collecting data from stakeholders in the form of FCMs (bi-directional graphs) represented by nodes and imprecise connections. All subsequent steps operate within a new fuzzy 2-tuple framework that overcomes previous FCM limitations through advanced processing methods, where large FCMs are fuzzified and analyzed, condensed, and aggregated using graph-theoretic measures. FCMs are simulated as Auto-Associative Neural Networks (AANN) to assess policy solutions to address the problem. In this study, very large cognitive maps were developed through interviews capturing perceptions of five different stakeholder groups taking into consideration the causes, consequences and challenges of the acute water scarcity problem in Jordan. The complex FCMs containing 186 variables comprehensively covered all aspects of water scarcity. FCMs were condensed into smaller maps in two levels. They were also combined into five stakeholder group FCMs and one whole system FCM (total 123 FCMs). AANN simulations of policy scenarios were conducted on the whole system FCM, first at the most condensed level and then moved top-down through the next two levels of granularity to explore potential solutions. These were ranked by a novel fuzzy Appropriateness criterion to provide a number of high level and effective strategies to mitigate the water crisis

A collaborative approach for disaster risk reduction: mapping social learning with Mistawasis Nêhiyawak

Social learning and its relation to disaster risk reduction (DRR) have been increasingly highlighted in the literature. Yet, limited empirical research has hampered practical DRR applications. This thesis demonstrated social learning loops and their outcomes by reflecting on the case of 2011 flooding in Mistawasis Nêhiyawak. Using a mixed-methods research design, I explored the role of participatory processes, including communication of scientific knowledge to lay-experts, in social learning. First, I created flood extent maps for the community using spatial data and modeling techniques. In the second phase, I presented the maps in a workshop held at the community center to understand their value in regard to what people learn from them. This included deliberating not only about physical parameters of the flood but also exploring the social (and human) parameters. Hence, I used fuzzy cognitive mapping (FCM) as a novel method to represent the human perception of flood risk and to measure social learning. In the workshop, FCM was complemented by focus group discussions and participatory mapping. From the results, it was found that i) social learning can be measured using social sciences tools, ii) sharing experiences and stories from past events augmented learning, and iii) awareness on the role of emergency planning in DRR was found to be a significant outcome of social learning. In the growing urgency of climate uncertainties, social learning theory will be critical in helping design practical and ethical research approaches to DRR that emphasize knowledge sharing, two-way communication, and reflexivity. These will ultimately have enhanced emphasis on behavioral responses to disasters that are complementary to the investments in structural responses

Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping

By aggregating semi-quantitative mind maps from multiple agents, fuzzy cognitive mapping (FCM) allows developing an integrated, cross-sectoral understanding of complex systems. However, and especially for FCM based on individual interviews, the map-building process presents potential pitfalls. These are mainly related to the different understandings of the interviewees about the FCM semantics as well as the biases of the analyst during the elicitation and treatment of data. This paper introduces a set of good practice measures to increase transparency and reproducibility of map-building processes in order to improve credibility of results from FCM applications. The case study used to illustrate the proposed good practices assesses heatwave impacts and adaptation options in an urban environment. Agents from different urban sectors were interviewed to obtain individual cognitive maps. Using this set of data, we suggest good practices to collect, digitalize, interpret, pre-process and aggregate the individual maps in a traceable and coherent way. © 2018 The Authors Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley and Sons Ltd. © 2018 The Authors Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley and Sons LtdThis study is part of the project Bottom-up Climate Adaptation Strategies for a Sustainable Europe (BASE) funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. 308337. MO (FPDI-2013-16631 and IJCI-2016-28835) and MBN (RYC-2013-13628) acknowledge co-funding from the Spanish Ministry of Economy, Industry and Competitiveness (MINECO)

Tools and methods in participatory modeling: Selecting the right tool for the job

© 2018 Elsevier Ltd Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process

Soc Netw Anal Min

Suicide is the second leading cause of death among youth ages 10-19 in the USA. While suicide has long been recognized as a multifactorial issue, there is limited understanding regarding the complexities linking adverse childhood experiences (ACEs) to suicide ideation, attempt, and fatality among youth. In this paper, we develop a map of these complex linkages to provide a decision support tool regarding key issues in policymaking and intervention design, such as identifying multiple feedback loops (e.g., involving intergenerational effects) or comprehensively examining the rippling effects of an intervention. We use the methodology of systems mapping to structure the complex interrelationships of suicide and ACEs based on the perceptions of fifteen subject matter experts. Specifically, systems mapping allows us to gain insight into the feedback loops and potential emergent properties of ACEs and youth suicide. We describe our methodology and the results of fifteen one-on-one interviews, which are transformed into individual maps that are then aggregated and simplified to produce our final causal map. Our map is the largest to date on ACEs and suicide among youth, totaling 361 concepts and 946 interrelationships. Using a previously developed open-source software to navigate the map, we are able to explore how trauma may be perpetuated through familial, social, and historical concepts. In particular, we identify connections and pathways between ACEs and youth suicide that have not been identified in prior research, and which are of particular interest for youth suicide prevention efforts.CC999999/ImCDC/Intramural CDC HHSUnited States

A Pedagogy of Hope: Levers of Change in Transformative Place-based Learning Systems

In response to mounting wicked environmental problems and an outdated U.S. educational system, this dissertation investigates transformative place-based education as an adaptive boundary system that connects individual learning to positive social-ecological change. The research approaches learning through a systems lens through a novel framework of Social-ecological Systems, Place-based Education, and Transformative Learning Theories. A three-part mixed methods approach, including content analysis of the literature, phenomenological interviews with experts in place-based education, and fuzzy-logic cognitive mapping with educators at three schools practicing the principles of place-based education, is used to examine transformative place-based education from the perspectives of current U.S. place-based educators. Twenty-four components of a transformative place-based learning system are identified in the research and used in constructing fuzzy-logic cognitive maps. Four essential themes emerge from the data that are discussed as levers that link individual learning to positive social-ecological change: active engagement, context and connection to place, collaborative real-world problem solving, and courageous leadership. The research indicates that most central to transformative place-based learning experiences is real-world problem solving. The causal relationships between real-world problem solving and almost every other learning system component, including agency, change, and place, creates the strongest link in this study between individual learning and positive social-ecological change

Exploring the Drivers of, and Potential Interventions to Reduce, Antimicrobial Resistance in the European Food System Context

Antimicrobial resistance (AMR) is a growing One Health problem that has become one of the leading causes of death worldwide. AMR emerges from a complex system characterized by multiple interacting factors across the human-animal-environment spectrum, all of which have the potential to be impacted by the effects of climate change. This thesis aimed to explore the drivers of AMR and assess potential interventions to reduce AMR in the Swedish food system context under potential climate change conditions. This thesis had four main objectives, to: 1) identify the quantitative and qualitative data needed to create and parameterize a simulation model of AMR emergence and transmission within the Swedish food system; 2) create and use a simulation model to test the potential ability of selected interventions to reduce AMR in the food system; 3) assess the sustainability of these interventions under climate change;, and 4) outline a systematic approach for creating mixed methods models for complex public health issues. The structure of the simulation model was based on an expert-derived causal loop diagram (CLD), created by Swedish and European AMR experts during a previously conducted participatory modelling workshop, that contained 91 nodes and 331 relationships deemed important to the development and spread of AMR within the Swedish food system. To determine if there was adequate information to create and parameterize the simulation model of AMR, a scoping review was conducted. This review identified 140 existing models and data from 414 sources to inform 64 of the major nodes within the CLD. The identified models addressed the main parts of the system (e.g., agriculture and farm transmission, antimicrobial use (AMU) and AMR, supply and demand for food); however, there was limited connection between the different areas of the food system. Nodes on the outer edges of the CLD did not have data, nor were they included within the scope of the models identified in the scoping review. Other data gaps included the environmental sector and wildlife. To further refine and parameterize the simulation model, semi-quantitative statements referring to the state of the nodes and relationships in the CLD were extracted from the transcripts from the prior participatory workshop. Transcript analysis identified 83 nodes, 48 of which were included in the CLD, and 35 were new nodes that emerged during the analysis or were existing nodes that were merged or divided. Based on the data requirements of the models identified via the scoping review, and the data currently available, it was not possible to create a fully quantitative model without including many assumptions. Therefore, the CLD was used as the base structure of a fuzzy cognitive map (FCM) of the Swedish food system, which was refined and parameterized by the data from the scoping review and transcript analysis. The final FCM contained 90 nodes, and 491 relationships. The use of FCM allowed for the evaluation of eight interventions under predicted climate change conditions, however, none of them were able to significantly reduce AMR in the system. Finally, the entire processes was reflected upon, including steps taken, challenges and mitigation strategies, and recommendations for future research in systems approaches for modelling complex systems and public health problems. In conclusion, this thesis identified that it was not feasible to create a purely quantitative model of AMR within the Swedish food system due to data limitations. However, by using data from the literature and experts’ tacit knowledge, an FCM of the system provided an innovative way to analyze the complex system, provided invaluable insight into the behaviour of the system, and aided in scenario analysis from a broader systems lens

Exploring the interactions among factors impeding the diffusion of prefabricated building technologies: Fuzzy Cognitive Maps

Purpose – The purpose of this paper is to identify the interactions of factors impacting the widespread adoption of prefabricated building technologies and the intervention strategies to facilitate the development of prefabrication based on fuzzy cognitive maps (FCMs). Design/methodology/approach – Through in-depth interviews with six stakeholder groups, namely, the government, developers, designers, contractors, manufacturers and researchers, 13 critical factors were identified and used to construct stakeholder-grouped FCMs, which were further aggregated into a collective FCM. The complexity and density of the collective FCM and the centrality of factors in the FCM were examined. Subsequently, a series of “what-if” simulations of the collective FCM were conducted to analyze the effectiveness of different interventions in promoting prefabrication. Findings – The results show that three factors including market demand, cost, and policies and regulations have been mentioned by all stakeholder groups. However, these factors were ranked differently by stakeholder groups, implying that different stakeholder groups perceive the barriers to prefabricated building technologies differently. FCM simulations show that strengthening policies and regulations yield the strongest overall effect stimulating prefabrication, alleviating the organizational and environmental barriers more than the technological barriers, while improving the knowledge and expertise alleviate the technological barriers more. These measures need to be accompanied by other approaches, such as reducing cost and improving quality. Research limitations/implications – It is a tough task to promote prefabrication as it is affected by numerous barriers with complex interactions, which have been overlooked by previous studies. This study clearly shows which strategy could tackle which barriers to prefabrication through the FCM simulations. This provides valuable references for the enterprises’ decision making and the governments’ policy making to facilitate the diffusion of prefabricated building technologies. Originality/value – Few studies aim to analyze the interactions among the barriers to prefabrication, while this study specifically investigates this issue by illustrating the complex interactions using FCMs. Few studies also aim to identify the intervention strategies promoting prefabrication based on a quantitative approach, while this study employs FCM simulations to directly simulate the effectiveness of different strategies to facilitate prefabrication in a quantitative manner.Xiao-Long Gan, Rui-Dong Chang, Craig Langston, Tao We

Making sense of changing coastal systems: overcoming barriers to climate change adaptation using fuzzy cognitive mapping

This thesis describes the role and value of Fuzzy Cognitive Mapping (FCM) in undertaking coastal climate change adaptation at the local scale, comparing FCM against existing, scenario-based adaptation methods in overcoming known barriers to adaptation. It describes the attributes and limitations of FCM as a modelling tool, exploring what must be accounted for in considering the use of FCM in mixed stakeholder settings where individual and group knowledge must be integrated to form a view of the system under study, discussing in some detail the facilitation strengths and weaknesses inherent to the method. These issues are then described via reference to case-studies in Ireland and Scotland, drawing inferences regarding the ease with which an FCM-based approach to adaptation might be substituted for orthodox, scenario-based adaptation. This is found to not only be feasible, but preferable, provided there is sufficient facilitation capacity on hand to manage the added complexity that FCM carries over simple narrative scenario development. Adding to the value that FCM offers in adaptation contexts, the thesis also explores its value as both a diagnostic tool for establishing what additional capacity building or data may be required by adaptation decision makers, and also as a tool for gauging the extent to which resilience gains (or losses) might be measured. Although FCM cannot be claimed to provide a robust objective measure of resilience gains or losses, it can nevertheless usefully illustrate to decision makers the strengths and limitations of their own understanding of the systems which they must manage. This is perhaps where the future of FCM-based systems analysis in support of adaptation may ultimately lie