using novel participatory modeling methods to

MODELS THAT MATTER: USING NOVEL PARTICIPATORY MODELING METHODS TO INTEGRATE MENTAL MODELS INTO AN ADAPTIVE COMANAGEMENT PROCESS

A Dissertation Presented by ALEXANDER E. METZGER

Submitted to the Office of Graduate Studies, University of Massachusetts Boston, in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

May 2018

Environmental Science Program

© 2018 by Alexander E. Metzger All rights reserved


A Dissertation Presented by ALEXANDER E. METZGER Approved as to style and content by:

________________________________________________ Ellen M. Douglas, Associate Professor Chairperson of Committee

________________________________________________ Paul Kirshen, Professor Member

________________________________________________ Nardia Haigh, Assistant Professor Member

________________________________________________ Steven A. Gray, Assistant Professor Michigan State University Member _________________________________________ Ellen M. Douglas, Graduate Program Director School for the Environment

_________________________________________ Robyn Hannigan, Dean School for the Environment

ABSTRACT


May 2018

Alexander E. Metzger, B.S., SUNY College of Environmental Science and Forestry M.S., University of Helsinki M.S., North Carolina State University Ph.D., University of Massachusetts Boston Directed by Dr. Ellen Douglas

Boston, Massachusetts has experienced frequent damage and negative impacts from storms and flooding events and is currently planning for a more hazardous future due climate change. Contemporary, social-ecological system (SES) approaches rely on vulnerability and resilience to describe system capacities and use adaptation as a means of adjusting its trajectory. The adaptive co-management (ACM) framework builds upon these approaches by emphasizing the importance of stakeholder mental models; the diverse array of internally-held understandings and dynamic representations of a system. Stakeholder mental models contain a wealth of information to support adaptation and present an opportunity to understand the diversity of perspectives that define effectiveness and equitability. Fuzzy cognitive mapping (FCM) is a growing approach in the field of participatory modeling that facilitates elicitation of mental models as a web of iv

concepts and weighted relationships. In this dissertation, we explored the utility of participatory FCM to better understand variation in mental models among flood managers in Boston and discover opportunities for social learning and collaboration. We first conducted a literature review of participatory FCM case studies to create a typology of approaches. This typology guided our participatory modeling process with organizations involved in flood mitigation and adaptation at various jurisdictional scales. We then used a novel method of knowledge classification with participatory FCM to study variation in flood manager perspectives. Next, we explored the utility of using FCM's to create boundary objects meant to facilitate shared learning among Boston’s flood managers.

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DEDICATION This work is dedicated to my partner, Monica, for inspiring me to do work that matters, and my children, Leah and Miles, who constantly remind me to live in the present and think creatively.

ACKNOWLEDGEMENTS The interdisciplinary and transdisciplinary nature of this work was largely influenced by my participation in the NSF Integrative Graduate Education and Research Traineeship Coasts and Communities Program at the University of Massachusetts Boston and the NSF Socio-Environmental Synthesis Center’s Student Pursuit Program. My remarkable peers and mentors within these communities challenged and inspired me to evolve intellectually and helped to transform the way I think about science and what it can offer to the world. I would like to thank my committee, Dr. Ellen Douglas, Dr. Nardia Haigh and Dr. Paul Kirshen, for their constant and invaluable support mentor, and Dr. Steven Gray, who played a critical role in my growth and development throughout the process. I would also like to thank the flood management community of Boston, Massachusetts and everyone else who played a part in shaping this work into what it has become. It would not have been possible without their generosity and dedication.

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TABLE OF CONTENTS

DEDICATION...............................................................................................

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ACKNOWLEDGMENTS .............................................................................

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LIST OF TABLES .........................................................................................

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LIST OF FIGURES .......................................................................................

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CHAPTER

Page

1. INTRODUCTION ........................................................................ Adaptation to Hazards in Social-Ecological Systems ............ “Learning and linking” processes of ACM ............................ Participatory FCM for integrating mental models ................. Chapter summaries................................................................. References ..............................................................................

1 1 5 9 11 16

PART I. LITERATURE REVIEW 2. TYPOLOGIES AND TRADE-OFFS IN FCM STUDIES: A GUIDE TO DESIGNING PARTICIPATORY RESEARCH USING FUZZY COGNITIVE MAPS ................................................................................................ 28 Introduction ............................................................................ 28 The 4-P’s Framework ............................................................ 34 Constructing the dataset ......................................................... 35 Methods.................................................................................. 37 Results and Discussion .......................................................... 65 Typologies and Tradeoffs ...................................................... 81 Conclusion ............................................................................. 83 References .............................................................................. 88

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PART II. CASE STUDIES IN BOSTON, MA CHAPTER

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3. UNDERSTANDING MENTAL MODEL DIVERSITY OF FLOOD MANAGERS IN BOSTON, MA THROUGH THEMATIC ANALYSIS OF FUZZY COGNITIVE MAPS ............................................................ 103 Introduction ............................................................................ 103 Study Area ............................................................................. 109 Methods.................................................................................. 111 Results .................................................................................... 117 Discussion .............................................................................. 124 Conclusion ............................................................................. 130 References .............................................................................. 132

4. USING FUZZY COGNITIVE MAPPING TO CREATE BOUNDARY OBJECTS FOR SHARED LEARNING AND COLLABORATION 145 Introduction ............................................................................ 145 Study Area ............................................................................. 150 Methods.................................................................................. 152 Results and Discussion .......................................................... 158 Conclusion ............................................................................. 168 References .............................................................................. 170 5. CONCLUSION ............................................................................ References ..............................................................................

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181 185

LIST OF TABLES

Table

Page

2.1. Description of the 4-P's framework (adapted from Gray et al., n.d.) ..................................................................................

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2.2. Rubric elements organized by the 4-P's framework ...................

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2.3. Journal and brief description of studies included in dataset .......

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2.4. Graph theory indices used as structural metrics from Gray et al. (2014) ..........................................................................

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2.5. Rubric element frequency by number of studies in descending order .................................................................................

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2.6. Summary of participatory FCM typologies ................................

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2.7. Limitations identified by researchers in each study type ............

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3.1. List of all organizations participating in study ...........................

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3.2. Metrics used in FCM analysis. Source: Gray, Zanre, & Gray (2014) ......................................................................

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3.3. Graph theory metrics for aggregated group models ...................

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3.4. Relative centrality of concepts in each theme among group models ..............................................................................

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3.5 Ten concepts with the highest centrality in each jurisdictional group model. Shorthand themes are: G = Governance, En = Environmental, St = Structural, So = Social, En = Economic .........................................................................

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4.1. Participant organizations in the case study of flood management in Boston .....................................................

154

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Table

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4.2. Sum of number of concepts and centrality of each theme among individual FCM’s.............................................................. 161 4.3. Relative centrality of each theme in individual FCM’s ..............

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161

LIST OF FIGURES

Figure

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1.1 Diagram of the adaptive cycle, also known as panarchy. Source: Gunderson and Holling (2002) ...........................

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2.1. Example FCM and adjacency matrix for a managed forest ecosystem .........................................................................

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2.2. Comparison of levels of “domain” and “local” knowledge among different participant types ....................................

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2.3. Comparison of frequency of purpose and product .....................

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3.1. Concept accumulation curve for models in chronological order .................................................................................

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3.2. National-level example of a group FCM ....................................

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4.1. Top 10 most central concepts, themes, and connections to “Policy Agenda” ..............................................................

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4.2. Venn diagram showing dominant perspectives for each participant organization ...................................................

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4.3. Group model of the In-Lieu Fee Program ...................................

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CHAPTER 1 INTRODUCTION

Adaptation to Hazards in Social-Ecological Systems

The social ecological systems (SES’s) paradigm has developed a strong foothold in research on mitigating the effects of environmental hazards. Studying complex systems of human and natural components, which are constantly interacting and modifying one another, offers a number of valuable insights and approaches to mitigation of environmental hazards (Liu, Dietz, Carpenter, Alberti, et al. 2007; Adger et al. 2005). Vulnerability and resilience are two concepts from this field of study that have been extensively adopted by scholars, practitioners, and the general public. Turner et al. (2003) describe vulnerability as a characteristic of the system defined by the presence of a specific hazard and a sensitivity of the system to this hazard. The outcomes environmental hazards are largely determined by the system’s capacity for different types of impacts and overall resilience of human and natural components of the system. Resilience in the context of environmental systems was originally defined by C. S. Holling (1973) as the ability of an ecological system to return to a stable state following an external shock. The concept was expanded in the literature to include elements of the 1

social system and human-facilitated recovery, providing much of the original basis for the field of SES (L Gunderson and Holling 2002; Walker et al. 2004; Folke 2006). Considered in combination, increased resilience is accomplished through decreasing factors that make a system vulnerable to shocks and increasing its capacity for recovery to a state that is considered stable and desirable (Walker et al. 2004; Folke et al. 2010).

Resilience-building is a largely social process that depends on broad inclusion of stakeholders and valuation of diverse knowledge sources. Cutter et al. (2008) emphasizes that the factors determining vulnerability and resilience, which include ecological, social, economic, institutional, infrastructure, and community competence, are locally specific. Since these different facets of a SES are often understood and managed independently from one another, resilience requires broad inclusion of stakeholders and diverse sources of knowledge and understandings (Innes and Booher 2008; Lebel et al. 2006). It is impossible, for example, to predict the outcomes of extreme weather and flooding events without an understanding of stakeholder perspectives on what constitutes a negative impact and the priorities that drive management efforts. Thus, recognition of the importance of local expert knowledge and traditional ecological knowledge in research has been growing in the literature, along with management approaches based on stakeholder inclusion (Manfredo et al. 2014; Berkes, Colding, and Folke 2000; Liu, Dietz, Carpenter, Folke, et al. 2007).

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Adaptation is another key concept that has developed a significant body of literature in the context of environmental hazards. The concept of the adaptive cycle, or panarchy, was applied by Holling (2001) and elaborated upon by others (Gunderson and Holling 2002; Walker et al. 2004) to illustrate the cyclical nature of system disturbance by shocks and the renewal and adaptation process that embody vulnerability and resilience. The adaptive cycle is a model describing the various stages of reorganization that a system undergoes at different scales. A system in a stable “conservation” phase (K) will naturally experience periodic shocks of different types, triggering a collapse, or “release” of resources (Ω), that is followed by a “reorganization” phase (α) in which system components self-organize based on current conditions and trigger an “exploitation” phase (r) where the system uses resources to progress toward a stable attractor state, whether similar or dis-similar than the last (Figure 1.1).

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Figure 1.1. Diagram of the adaptive cycle, also known as panarchy. Source: Gunderson and Holling (2002).

Adaptation is also closely tied to the knowledge and understandings of community stakeholders, particularly in the context of environmental hazards. The high complexity inherent to social and environmental dynamics makes effective decisions a difficult endeavor (Adger 2006; Nelson, Adger, and Brown 2007). An immense diversity of knowledge is required to anticipate how strategies and interventions will impact the system’s trajectory (Berkes, Colding, and Folke 2003, 2000; Folke et al. 2007), and it has been repeatedly demonstrated that centralized, top-down decision-making with narrow objectives and input produces inconsistent outcomes that are often considered by stakeholders to be undesirable and inequitable (Innes and Booher 2008; Kapoor 2001; Funtowicz and Ravetz 1991). Diverse actors and organizations within an SES form 4

unique knowledge and understandings through experience with different aspects of the system at different spatial and temporal scales (Manfredo et al. 2014; Sternlieb et al. 2013). Inclusion of this diverse knowledge into decision-making can aid in navigating the system’s complexities (Berkes, Folke, and Gadgil 1995; Berkes, Colding, and Folke 2000). In contrast to traditional, top-down approaches to decision-making, which treat diversity and complexity as obstacles (Adger, Arnell, and Tompkins 2005; Cash et al. 2006), more recent approaches, such as adaptive co-management (ACM), treat them as a resource to achieve more effective understandings of vulnerability, resilience, and adaptation.

“Learning and linking” processes of ACM

Interest in adaptive co-management grown among SES scholars and management practitioners. ACM arose from the desire to integrate two innovative approaches to management and problem solving: an adaptive management approach, which involves using knowledge of current and future conditions to adjust the system trajectory, and a collaborative management approach, which encourages collaboration among stakeholders as a means of effective and equitable decision-making (Folke et al. 2005; Olsson, Folke, and Berkes 2004; Hahn et al. 2006). This integration is a reaction to several persistent challenges in management of complex SES’s, including:

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1. Cross-scale interactions – actors whose jurisdiction and institutional goals function at different scales (spatial, temporal, qualitative, or analytical) often find collaboration and communication difficult due to a mismatch in priorities and fundamental understandings (Cash et al. 2006; Gibson, Ostrom, and T.K. Ahn 2000).

2. Knowledge generation and integration – co-management processes, for example governance of a shared resource and problem-solving involving many stakeholders, are often ineffective without social learning and stakeholder participation that integrate diverse knowledge and aid in creation of shared knowledge. As Brown (2003) describes, integrating diverse stakeholder knowledge and interests is an inherent difficulty that central governing bodies face when creating rules that govern behavior and conduct (Berkes, Folke, and Gadgil 1995; Berkes 2009).

3. Institution-Ecosystem fit – gaps between institutional perspectives and ecological realities of SES’s are due to lack of information and insufficient adaptation to new conditions (Ekstrom and Young 2009; K. Brown 2003; Folke et al. 2007). Institutions often struggle to create management structures that account for the complexity of these coupled systems and encourage effective adaptation to their dynamic behavior (Brown 2003). The timescales and spatial scales at which

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human and natural system function is also a source of mismatch (Folke et al. 2007).

The work of C.S. Holling and other early SES scholars created the foundations of adaptive management (Holling 1973, 1986; Timmerman 1981; Folke, Holling, and Perrings 1996), encouraging a more dynamic and pluralistic view of ecosystem states and trajectories. These ideas championed the viewpoint of resilience and stability as a way to better understand the dynamics of SES’s (Folke 2006; Walker et al. 2004). The adaptive cycle, or panarchy, was proposed as a model of the cyclical nature of complex systems, describing their reorganization, recovery and evolution following disturbances and changes occurring simultaneously among interlocking physical and temporal scales (L Gunderson and Holling 2002; C. S. Holling 2001). Applying this new social-ecological paradigm to the practical concerns of managers and decision-makers led to greater focus on monitoring changes in system state, continual learning applying new knowledge to deal with changing conditions (Lance Gunderson 1999; Claudia Pahl-Wostl 2007; Nelson, Adger, and Brown 2007).

Bodin (2017) summarized that collaborative management approaches arose from the need to address three persistent issues: 1. Presence of numerous stakeholders with diverse perspectives and needs 2. Spatial and temporal mismatch of biophysical processes with institutional jurisdictions

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3. Gaps in knowledge about dynamic behavior of different system components

These issues are inseparable, representing intertwined dimensions of that must be addressed by decision-makers. Diverse stakeholder perspectives often cause conflict and inability to work together toward solutions, and can often be related to scale (Biggs et al. 2011). A spatial and temporal mismatch can affect the fit of institutional actions with the biophysical aspects of a SES and perspectives of other stakeholders, as scale greatly influences these understandings (Guerrero et al. 2013; Cash et al. 2006; Roux et al. 2006; Biggs et al. 2011). The mismatch of institutions with the scale of biophysical processes can indicate a lack of sufficient knowledge about those processes, owing to their complex and interlinked nature (Sternlieb et al. 2013; Folke et al. 2007; Guerrero et al. 2013). Broad collaboration has the potential to address these interwoven issues by highlighting the diversity of stakeholder perspectives and attempting to encourage integration and cocreation of knowledge about complex processes (Stern and Coleman 2015; Hahn et al. 2006).

Plummer et al. (2012) coins these two distinct dimensions of ACM “learning”, referring to the constant monitoring and adjustment of system trajectories, and “linking”, referring to the collaboration of stakeholders or institutions at different scales. The learning and linking processes of ACM are intended to create a synergistic effect. For example, linking the knowledge held by diverse groups and generating new shared knowledge through participatory processes increases understanding about the system and the ability

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of the community to adapt to its dynamic behavior. In their extensive review of ACM case studies, Plummer et al. (2012) found that scholars considered participation of diverse stakeholders and knowledge generation, use, and sharing as two of the most important factors contributing to the success of ACM. They also found that many failures of ACM processes were related to a lack of knowledge and an inability to facilitate learning (Plummer et al. 2012). A later review by Plummer, Armitage, & De Loë (2013) on the fit of ACM and environmental governance showed similar findings: knowledge, representation of diverse perspectives and co-learning of actors was considered critically important to effective governance.

Plummer, Armitage, & De Loë (2013) also noted that appropriate tools and techniques are critical to the success of ACM. All of the factors mentioned in this research on the effectiveness of ACM relate in some way to mental models, which are defined as an individual’s cognitive understanding of the external world that are dynamically and iteratively constructed through observation and experience (Jones, Ross, Lynam, Perez, & Leitch, 2011). Thus, as pointed out by many scholars who address elements of knowledge and perspectives in SES, an approach that emphasizes mental models is critical (Biggs et al. 2011; Folke 2006; Robèrt, Daly, and Hawken 1997).

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Participatory FCM for integrating mental models

Since their original conception by Craik (1943), the concept of mental models have been further developed and applied to research on human reasoning and decision-making in many contexts (Johnson-Laird 1983; Jones et al. 2011). Mental models provide a basis for understanding stakeholder values, beliefs, and decision-making strategies and contain local expert and traditional knowledge about complex social-ecological system dynamics (Biggs et al. 2011; Manfredo et al. 2014; Gray et al. 2012; Doyle and Ford 1998). A great deal of research has focused on externalizing this valuable information and identifying tools for structuring and utilizing them (Carley, 1997; Carley & Palmquist, 1992; Doyle & Ford, 1998; Jones et al., 2011). Fuzzy cognitive mapping (FCM) is a technique that is gaining increased attention for its ability to represent mental models in a manner theoretically consistent with the neurological architecture of the brain (Zhang and Chen 1988; Kosko 1988).

FCM is a semi-quantitative modeling technique that represents complex systems as causal networks composed of concepts and signed, weighted relationships (Kosko 1986). The complex network structure of FCM allows study of the dynamic behavior of systems. One common approach is to run scenarios in which input nodes are activated to simulate a particular event or set of conditions, and observe the resulting change in system state as this activation causes a cascading effect on other concepts in the model (Stylios and Groumpos 2004; S. A. Gray, Gray, et al. 2015b). While FCM provides a rigorous and

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complex modeling environment for applications such as scenario analysis and outcome prediction (A. Jetter 2006; Papageorgiou and Salmeron 2013; Aguilar 2005), there is an increasing interest in its use as a participatory modeling tool to better understand and utilize mental models (Gray, Gray, et al. 2015b; Gray, Zanre, et al. 2014). There are an increasing number of participatory FCM case studies in the literature due to the utility of these methods in eliciting stakeholder mental models and critical knowledge that they contain about the human and natural components of complex systems (Özesmi and Özesmi 2004; Nyaki et al. 2014; E. Papageorgiou and Kontogianni 2012; Jetter and Kok 2014).

Based on the importance of mental models to the learning and linking processes that make up ACM, participatory FCM is a tool that could enhance its capacity for problemsolving and management. The purpose of this dissertation is to answer the following question: How can participatory modeling support the use of mental models in adaptive comanagement?

To explore this question, I conducted an analysis of common methods of participatory FCM and applied two innovative applications of this tool to a case study of flood management community in Boston, MA.

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Chapter summaries

Chapter 2: Typologies and Trade-offs in FCM Studies: A guide to designing participatory research using Fuzzy Cognitive Maps

Participatory modeling is part of a growing practice in transdisciplinary science that demonstrates the value of diverse knowledge beyond that of traditional scientific experts (Voinov and Bousquet 2010; Voinov et al. 2016; Htun, Gray, Lepczyk, Titmus, Adams, et al. 2016). This reframing of the value of local and traditional knowledge, the role of participants and non-experts in research, and the role of science in society opens a vast world of possibilities for better understanding and influencing the complex, interconnected world in which we live (Berkes, Colding, and Folke 2000; Roux et al. 2006; Innes and Booher 2008).

Fuzzy cognitive mapping (FCM) is a semi-quantitative modeling technique that is gaining traction in participatory modeling and environmental planning contexts to better understand complex systems and their human and environmental components (Gray, Gray, et al. 2015a; Papageorgiou and Salmeron 2013; Stylios and Groumpos 2004). This modeling technique allows the creation of robust dynamic system models through integration of diverse sources and types of information (Kosko 1986; Aguilar 2005). It has been successfully used to model and integrate layperson, practitioner and scientific knowledge in a variety of contexts, and has been more recently used to facilitate

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collaborative learning about complex systems, optimize decision-making and support prioritization of management alternatives (Gray, Zanre, et al. 2014; Jetter and Kok 2014; Özesmi and Özesmi 2004).

In this chapter, I review the participatory FCM literature to create a typology of approaches for representing expert and non-scientific mental models and integrating diverse stakeholder knowledge. I then discuss analytical and conceptual trade-offs among approaches and make recommendations on how to use this information in designing participatory research. The guiding question in this chapter is: What standards and norms are emerging in the design of participatory FCM research?

Chapter 3: Understanding mental model diversity of flood managers in Boston, MA through thematic analysis of fuzzy cognitive maps

Global climate change presents a worldwide challenge for coupled human and natural systems worldwide, as it is expected to drastically alter climatic norms and weather patterns across the globe (IPCC 2014). The northeastern United States is expected to receive a great deal more precipitation and impacts from extreme weather than in the past (Barlow 2011; Trenberth 2011). The greater urbanized area of Boston, Massachusetts has experienced many costly and damaging impacts from extreme weather and flooding in the past, and global climate change, coupled with continued urban expansion, allude to

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the possibility of even greater vulnerability in the future (Kirshen, Knee, and Ruth 2008; Douglas et al. 2013; Douglas et al. 2012).

The flood management of Boston, MA is very progressive in discussing these issues and involving a wide range of organizations and stakeholder perspectives into decisionmaking processes aimed at managing current threats and adapting to future conditions. However, there is an expressed need to better understand the diversity of perspectives and priorities within the community in order to better structure knowledge sharing and collaboration, especially among organizations at different jurisdictional scales. This chapter uses participatory FCM and a novel application of knowledge classification to answer the questions: 1. What can participatory FCM reveal about variation in perspectives among experts at different jurisdictional scales? 2. How can a better understanding of mental model diversity inform social learning and collaboration efforts?

Chapter 4: Clustering and connecting: Using fuzzy cognitive mapping to create boundary objects for shared learning and collaboration

Social learning and collaboration among diverse stakeholders are essential processes in management of complex social-ecological systems. While several case studies of participatory FCM have often succeeded in learning about variation in participant

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perspectives as a precursor, they often have difficulty in achieving clear outcomes in the way of social learning and collaboration. One conceptual tool that may address this gap between diverse perspectives and shared learning and collaboration is boundary objects. A boundary object is defined as anything that serves as a focal point for dialogue and cooperation with or without consensus and are used for the purposes of exploring mental models and facilitating knowledge sharing and co-creation of knowledge. Boundary objects can take many forms, such as models, visual representations, organizations, specific concepts, physical places.

In this chapter, I use the flood management community of Boston, MA as a case study to explore the use of participatory FCM to create boundary objects in two ways: identifying concepts that enable the sharing and learning from diverse mental models, and as a process of collaborative modeling designed to build shared knowledge and understandings about flood management and adaptation. The guiding research question is: How can participatory FCM's be used to create boundary objects for social learning?

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Pahl-Wostl, Claudia. 2007. “Transitions towards Adaptive Management of Water Facing Climate and Global Change.” Integrated Assessment of Water Resources and Global Change: A North-South Analysis, 49–62. https://doi.org/10.1007/978-1-4020-55911-4. Papageorgiou, E., and a Kontogianni. 2012. “Using Fuzzy Cognitive Mapping in Environmental Decision Making and Management : A Methodological Primer and an Application.” International Perspectives on Global Environmental Change, 427– 50. https://doi.org/10.5772/29375. Papageorgiou, Elpiniki I., and Jose L. Salmeron. 2013. “A Review of Fuzzy Cognitive Maps Research during the Last Decade.” IEEE Transactions on Fuzzy Systems 21 (1):66–79. https://doi.org/10.1109/TFUZZ.2012.2201727. Plummer, Ryan, Derek R Armitage, and R C De Loë. 2013. “Adaptive Comanagement and Its Relationship to Environmental Governance.” Ecology and Society 18 (1):21. http://dx.doi.org/10.5751/ES-05383-180121. https://doi.org/10.5751/ES-05383180121. Plummer, Ryan, Beatrice Crona, Derek R Armitage, Per Olsson, Maria Tengö, and Olga Yudina. 2012. “Adaptive Comanagement: A Systematic Review and Analysis.” Ecology & Society 17 (3):290–306. https://doi.org/10.5751/ES-04952-170311. Robèrt, Karl-Hennk, Herman Daly, and Paul Hawken. 1997. “A Compass for Sustainable Development.” International Journal of Sustainable Development & World Ecology 4 (2):79–92. https://doi.org/10.1080/13504509709469945.

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CHAPTER 2 TYPOLOGIES AND TRADE-OFFS IN FCM STUDIES: A GUIDE TO DESIGNING PARTICIPATORY RESEARCH USING FUZZY COGNITIVE MAPS

This chapter is adapted from a book chapter currently in press, citation: Metzger, A. E., Gray, S. A., Jetter, A. J., & Papageorgiou, E. I. (2018). Typologies and tradeoffs in FCM Studies: A guide to designing participatory research using fuzzy cognitive maps. In M. McNall (Ed.), Innovations in Collaborative Modeling. East Lansing, MI: Michigan State University Press.

Introduction

During the 1960’s and 1970’s, scholars and practitioners began to challenge the traditional top-down approaches that had previously defined social development and environmental policy with the development of collaborative approaches to planning and management (Healey 2003, 1997; Kapoor 2001). Concern for stakeholders and their involvement in environmental management and decision-making became increasingly widespread among practitioners, and legal mandates established during this time called for participation of stakeholders and the general public in various management situations (Voinov and Bousquet 2010). This movement toward collaboration and participation

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eventually found its way into academia, countering some of the ‘ivory tower’ mentality of researchers and becoming an approach meant to incorporate knowledge diversity, local context, and increased legitimacy into the research process (Voinov and Bousquet 2010).

As a result, recent decades have yielded case-studies and new methods that link public participation to the success of conservation and resource management projects. These cases have engaged local stakeholders of diverse interests, and suggest that many previous resource management failures to lack of participation (Gleason et al. 2010; Persha, Agrawal, and Chhatre 2011). However, the individual context of each of these participatory projects is often unique and complex in its interconnected social and ecological characteristics. Thus it is unlikely that there is a universally ideal level or type of participation, process or tool for engagement (C. Klein et al. 2015). Additionally, some reoccurring fundamental challenges to participatory approaches have been identified including: (1) mismatches in understandings between managers, researchers and stakeholders about complex systems and planning outcomes; (2) power dynamics that lead to inequitable agency and benefit among stakeholders, and (3) difficulty of institutionalizing and sustaining inclusive and adaptive approaches (Barrett and Arcese 1995; Alpert 1996; Newmark and Hough 2000; K. Brown 2003).

Participatory modeling has developed alongside this movement away from top-down, expert-only management and decision-making to provide tools that enhance participatory approaches to decision-making and planning (Voinov and Bousquet 2010; Voinov and

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Gaddis 2008). Traditional approaches to scientific modeling that rely on scientific information alone can be rejected by decision-makers and stakeholders due to lack of local buy-in and accounting for local priorities, values, and knowledge (Voinov and Gaddis 2008). Additionally, complex problems within scientific disciplines often require more than traditional expert knowledge alone can offer (Funtowicz and Ravetz 1991). Participatory modeling can be used to expand and diversify the knowledge used in understanding complex, interlinked social and environmental problems, and contribute to decision-making via collective model-based reasoning.

While a great variety of participatory modeling tools exist (Voinov and Bousquet 2010), those that emphasize explicit mental models elicitation are particularly well suited to address the need for integration of diverse stakeholder knowledge and perspectives identified by many authors (Biggs et al. 2011; Manfredo et al. 2014; Doyle and Ford 1998). The emphasis on diversification of mental models used in decision-making contexts is in itself an acknowledgement that model building is inherently subjective and its legitimacy depends upon the assumptions made and perspectives represented (Jones et al., 2009). The term mental model refers to an individual’s internal cognitive representation of the external world, which is dynamically and iteratively constructed over time and relies largely on construction of cause-and-effect relationships (Jones, Ross, Lynam, Perez, & Leitch, 2011). Since their original conception by Craik (1943), the concept of mental models has been further developed and applied to research on

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human reasoning and decision-making in many contexts (Johnson-Laird, 1983) and environmental management and planning (Jones et al., 2009).

In this chapter I focus on Fuzzy cognitive mapping (FCM) as one specific approach that has been used in participatory planning and modeling. This modeling technique is commonly used to translate informal mental models of stakeholders and decision-makers into networked, cause-and-effect models that capture knowledge and understandings about environmental dynamics ( Gray, Zanre, & Gray, 2014; Jetter & Kok, 2014; Jones et al., 2011). Building upon earlier work that established cognitive mapping as a means of understanding and representing cognitive structure (see Axelrod, 1976 and Tolman, 1948), Kosko (1986, 1988) pioneered FCM by combining ideas related to neural networks consisting of nodes and signed, directional relationships, with fuzzy logic, which provides a -1 to 1 weighing to those relationships. While viewed graphically as a network model of a system, FCM’s mathematical foundation is an ‘adjacency matrix’, or a grid of numerical pairwise relationships among all components (Figure 2.1). When one or more nodes are ‘activated’ by inputting an initial, non-zero value, this activation spreads through the matrix following the weighted relationships. Feedback loops cause repeated activation of a concept, introducing interconnectivity to the model. The activation of nodes is iterated, using a ‘squashing function’ to rescale node values to a 0 to 1 scale, until the model reaches a stable state. The resulting node values for all concepts can be used to interpret scenario outcomes and study the dynamic behavior of modeled systems.

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Figure 2.1. Example FCM and adjacency matrix for a managed forest ecosystem

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The strengths of FCM for participatory applications focusing on mental model elicitation vary by approach, but generally include: ● Ability to represent, integrate and compare very diverse types of knowledge (Gray et al., 2013; Özesmi & Özesmi, 2004) ● Speed and ease of model building (Özesmi and Özesmi 2004) ● Simple enough for stakeholder involvement in model building (A. J. Jetter and Kok 2014; S. A. Gray, Zanre, and Gray 2014b) ● Allows interactive scenario analysis (Gray et al., 2015; Jetter & Kok, 2014) ● Free software packages with helpful user interfaces (Gray, Gray, Cox, & HenlyShepard, 2013)

The weaknesses of this method, also vary by approach, include: •

Potential simplification and misrepresentation of some relationships for which advanced knowledge exists (Özesmi and Özesmi 2004; E. I. Papageorgiou and Salmeron 2013)

•

Lack of spatial and temporal representation

•

No formalized and structured way to represent relationship strength

•

Possible susceptibility to group power dynamics and subsequent over-reliance on a few individuals’ knowledge in a group model-building setting (Jetter & Kok, 2014)

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Klein and Cooper (1982) in their study of operational decision-making by army officers, were the first, to my knowledge, to reference a participatory mental modeling case study. The great majority of participatory FCM studies are related to environmental planning have been published since Özesmi and Özesmi, (2004) popularized the approach originally developed by Kosko (1986). While participatory FCM is still in its early stages but standard practices and descriptions of methods and approaches are beginning to emerge. To support the continued growth of the participatory FCM method and aid in the design of future participatory FCM studies, this chapter reviews applications, creates a typology, and discusses common approaches to participatory FCM and their benefits and tradeoffs. This paper is guided by the question: What standards and norms are emerging in the design of participatory FCM research?

The 4-P’s Framework

Framework and rubric creation

To organize this review of the participatory FCM literature and create a typology of approaches, I employ a framework created by (Gray et al., 2017), known as the “four P’s” (4 P’s) framework for participatory environmental modeling. This framework is intended as a way for researchers involved in participatory modeling of socialenvironmental systems to structure research initiatives and identify and test new hypotheses in this field by standardizing case study applications. The components of the

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framework can be seen in Table 2.1. I used the 4 P’s framework to create a rubric that guided development of the typology (Table 2.2).

Constructing the dataset

Construction of the dataset began with a general search for articles that included topics or keywords including “fuzzy cognitive map(ping)” and “participatory modeling” on Web of Science (21 results) and ScienceDirect (22 results) databases. I then reduced this selection to those that included case studies and in which the system included social and ecological components, and those in which the definition of participatory included the following criteria: 1. The model(s) directly represent knowledge or perspectives of people other than the researcher 2. The holders of this knowledge are consulted directly and in some way engaged in the modeling process While individual definitions of ‘participatory’ can be variable depending on the priorities of the researcher and objectives of the research, I chose to focus on studies in which participants were engaged directly in some part of the actual modeling process. Citations from the resulting set of studies were used as a resource to locate more studies fitting these criteria, as were review articles by Aguilar (2005), Jetter and Kok (2014) and Papageorgiou and Salmeron (2013).

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The resulting dataset included 33 studies published in scientific journals, books, or dissertation databases (Table 2.3). Case studies covered a wide variety of topics, including natural resource management, agriculture, ecology, policy and economics, and strategic decision-making. While several of the studies in the dataset were published in journals with an explicit focus on modeling techniques (ex: Ecological Modeling and Environmental Modeling and Software), the majority of studies were published in journals covering topics related to conservation and management of coupled human and natural systems (ex: Ecology and Society and Ocean and Coastal Management). The following section will identify and explain the various elements of the rubric, developed according to the 4 P’s framework, that I used to review these case studies and create a dataset.

Methods

Purpose

Research Purpose

With guidance from the participatory modeling literature (see: Jones et al. 2009 and Voinov and Bousquet 2010), I identified five main types of research purpose that applied to the studies in the dataset: (a) enhancing system knowledge, (b) understanding variation among participants, (c) shared learning, (d) consensus-building and (e) increasing

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participation. All studies in the dataset included at least one of these purpose types, and many included multiple since they were not exclusive categories.

Table 2.1. Description of the 4-P's framework (adapted from Gray et al., n.d.) Question to be addressed

Dimension Reported

Purpose

Why was the PM approach selected?

Partnership

Who participated and why?

Processes

How were stakeholders involved?

Product

What was produced by the modeling process?

1. Providing justification for why PM is used 2. Defining the issue and the purpose of the model 1. Defining model, data, and process ownership 2. Describing the criteria for inclusion of participants 3. Describing the steps participants are involved in 1. Defining the characteristics of the interaction between the participants and the model 2. Describing the level of participation 3. Defining relationship between the PM and a decision-making process 1. Defining characteristics of the PM tool produced 2. Defining the social outcomes of the process 3. Defining the policy, management or scientific insights

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Table 2.2. Rubric elements organized by the 4-P's framework 4-P’s Framework Element Purpose Partnership

Process

Product

Rubric Element 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Research Purpose Sample population Participant Involvement Interactions with Participants Knowledge Capture Standardization and Condensation Aggregation Structural analyses Comparative analyses Testing and Simulation Outcomes Recommendations for future research Limitations

Table 2.3. Journal and brief description of studies included in dataset Citation

Journal/Source

(Berbés-blázquez 2015)

Dissertation

(Çelik, Özesmi, and Akdoğan 2006)

Ecological Modeling

Lake management, livelihoods

(Douglas et al. 2016)

Journal of Hydrology: Regional Studies

(Fairweather 2010)

Ecological Modeling

Agriculture, irrigation management Dairy farming, ecosystem function Agriculture, system management Water resources, collaborative management Fisheries, ecosystem function Coastal management, climate change

(Fairweather and Hunt 2011) (Giordano et al. 2005) (Gray et al. 2012) (Gray et al. 2014)

Agriculture and Human Values Physical and Chemistry of the Earth Ecological Modeling Ocean and Coastal Management

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System and Topic of Focus Agriculture, ecosystem services

(Henly-Shepard, Gray, and Cox 2015)

Journal of Outdoor Recreation and Tourism Global Environmental Change Environmental Science and Policy

(Hobbs et al. 2002)

Ecological Applications

(Isaac, Dawoe, and Sieciechowicz 2009) (Jetter and Schweinfort 2011)

Environmental Management

(Gray et al. 2015) (Halbrendt et al. 2014)

(Kafetzis, McRoberts, and Mouratiadou 2010)

(Klein & Cooper, 1982) (Kontogianni et al. 2012) (Kontogianni, Tourkolias, and Papageorgiou 2013) (Mendoza and Prabhu 2006) (Mouratiadou and Moran 2007) (Murungweni, Wijk, et al. 2011) (Nyaki et al. 2014) (Özesmi and Özesmi 2003) (E. I. Papageorgiou, Markinos, and Gemptos 2009)

Futures Book: Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications Journal of the Operational Research Society Ocean and Coastal Management International Journal of Hydrogen Energy Forest Policy and Economics Ecological Economics Ecology and Society Conservation Biology Environmental Management Expert Systems with Applications

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Fisheries, ecological function Agriculture, adoption of practices Coastal hazards, disaster planning Lake ecosystem, ecological rehabilitation Agroforestry, farm management Solar photovoltaic, adoption factors (2 studies) River basin, use and policy (2 studies)

Military wargames, decisionmaking Marine system, ecosystem resilience Hydrogen transport technology, market behavior Forest, sustainable management River basin, water resource management Conservation areas, livelihoods Bushmeat hunting, policy Lake system, ecosystem management Agriculture, yield prediction

(Rajaram and Das 2010)

Expert Systems with Applications

(Ramsey and Norbury 2009)

Austral Ecology

(Samarasinghe and Strickert 2013)

Environmental Natural hazards, public policy Modeling and Software Lake ecosystem, ecosystem Hydrobiologia function Water resources, system Futures function Grassland agriculture, Ecological Modeling management Journal of Bay and estuary ecosystem, Environmental ecosystem function Management

(Tan and Özesmi 2006) (van Vliet, Kok, and Veldkamp 2010) (Vanwindekens, Stilmant, and Baret 2013) (Vasslides and Jensen 2016)

a.

Agro-ecosystem, sustainability factors Dryland ecosystem, pest management

Enhancing system knowledge

Researchers increasingly value involving local stakeholders in the research process in order to take advantage of the wealth of non-scientific knowledge they can provide, which is often referred to as ‘traditional’ or ‘local’ knowledge (Biggs et al. 2011; Folke et al. 2005; Manfredo et al. 2014). It has been suggested that in many cases, scientific knowledge alone is not sufficient for creating a comprehensive understanding of a complex system (Funtowicz and Ravetz 1991). Integrating local stakeholders is often thought to provide a more thorough understanding of local conditions, fine-scale dynamics, and decision-making context, which are increasingly being considered valuable to research and management of these systems (Berkes, Folke, and Gadgil 1995; Berkes and Folke 1998). Many studies have attempted to use FCM to integrate diverse sources of knowledge from scientists and non-scientists into a model that better describes

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the functioning of a complex system (see Gray et al., 2012; Tan and Özesmi, 2006; Rajaram and Das, 2008).

b.

Understanding variation among participants

The mental models of stakeholder groups that are expressed in environmental planning environments can potentially be very different, causing misunderstandings and difficulty pursuing coordinated or collective action (Biggs et al. 2011). An understanding of the variation of mental models among stakeholders can be used to facilitate more productive discussion and negotiation about complex systems and decisions that involve multiple stakeholders. Using FCM as a tool to compare the knowledge, understandings, and priorities contained in mental models can make areas of mismatch and agreement explicit (Nyaki et al. 2014; Areti Kontogianni, Tourkolias, and Papageorgiou 2013; Giordano et al. 2005)

c.

Shared learning

Another commonly mentioned use of FCM’s is to stimulate shared learning within a community (see Gray et al., 2016; Henly-Shepard, Gray, & Cox, 2015). The aim of this objective is to make explicit and distribute the knowledge each stakeholder holds about a system using FCM’s, and use them to stimulate a learning process in which stakeholder mental models are expanded or modified. Jones et al. (2009) identify that the participation process may involve a transfer or sharing of knowledge, or it may be co-

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created among participants and researchers. The process of eliciting and integrating this “inside knowledge” into the individual and collective understanding of a particular community is a means of initiating social learning, and group processes are often used to achieve this (Pahl-Wostl, 2007). Some researchers involved in learning-oriented processes have explicitly used the concept of ‘learning loops’, in which learning can be stimulated at a variety of levels including individual, social network and institutional (Henly-Shepard et al., 2015; Pahl-Wostl, 2006). Gray et al. (2016) explored the use of citizen science to engage participants in a science-related process to create social learning outcomes. It has also been suggested that social learning within decision-making communities is an essential adaptive process for creating collective action in attempts to solve complex, multifaceted issues such as climate change (Tschakert and Dietrich 2010).

d.

Consensus-building

Consensus-building involves the negotiation of a shared vision or course of action in an attempt to deal with uncertainty, complexity, or controversy (Innes and Booher 1999). This negotiation process does not necessarily require convergence of mental models held by different stakeholders within a consensus-building process. Instead, consensusbuilding among a group is a process of sharing elements of individually-held mental models and reconciling differences in order to develop a collective set of assumptions or a shared basis for decision-making (Mohammed & Dumville, 2001). In participatory FCM, the process of consensus-building can take the form of group model building, in which discussions and negotiations take place during the model building, or as an

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aggregation of models to simulate a community consensus (see Douglas et al. 2016; Hobbs et al. 2002; Klein and Cooper 1982).

e.

Increasing participation

Lastly, proponents of stakeholder participation in research and management suggest that increasing the involvement of stakeholders in research and decision-making can have many benefits, but also poses significant challenges (Dietz and Stern 2008). Beyond increasing the depth and diversity of knowledge, participation is often championed as a means of encouraging broader stakeholder buy-in, greater social capital, and greater equity in governance processes and outcomes (Berkes, Colding, and Folke 2000; Walker et al. 2002; Folke et al. 2005). The assumption is that participants with find more value in a process that includes their perspectives and will increase its overall legitimacy and tractability by endorsing it as members of their local community. In this way, participation can increase access to local capacity and social capital that to support success and persistence of a project, possibly creating longer-term initiatives (Kapoor 2001). Partnership

To describe the different ways in which partnership was accomplished in these participatory FCM studies, I examined the following rubric items: 1.

Sample population

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2.

Participant Involvement

3.

Interactions with Participants

Sample population

FCM modeling studies frequently sample respondents from a larger population through a process of purposeful selection that aims to include all perspectives relevant to the study. The type of populations that were engaged in the reviewed studies included local experts, domain experts, practitioners, and stakeholder groups.

These participant classifications can be considered in terms of two different knowledge scales: one representing the level of domain knowledge about a system from professional and scientific literature, and the other representing the level of local knowledge about a specific system from direct experience with the system (Figure 2.2). In an agricultural system, for example, local knowledge may include an understanding of place-specific environmental dynamics, such as the local effects of low rainfall levels on crop growth and water resources for irrigation, and the co-influence of water resources with local farmer’s irrigation strategies. Domain knowledge refers to specialized scientific and professional knowledge about topics and processes that operate according to identical principles across landscapes, such as hydrology and plant physiology.

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Figure 2.2. Comparison of levels of “domain” and “local” knowledge among different participant types.

Local experts are expected to have a great deal of local knowledge regarding the system with varying amounts of domain knowledge. Some examples of local experts found in the case studies included groups specializing in coastal resilience within a particular community (Gray et al., 2014) and lay people who live within the system contributing their experientially developed, place-based knowledge (Kontogianni, Papageorgiou, Salomatina, Skourtos, & Zanou, 2012). Domain experts included ecologists, which were

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included in a study about water resource management (Mouratiadou and Moran 2007), and fisheries scientists engaged in creating FCM’s about the mid-Atlantic summer flounder fishery (Gray et al., 2012).

We define practitioners as participants that were engaged in the research specifically to represent the knowledge of those managing or working within the system being studied. Practitioners may possess a widely variable combination of local knowledge, through experience directly with the system being studied, and domain knowledge, though education in the general scientific and professional disciplines involved in their occupation. Vanwindekens, Stilmant, and Baret (2013), for example, interviewed a large number of farmers to create FCM’s of the agricultural system and its management practices. Nyaki et al. (2014) engaged bushmeat hunters in Tanzania to study their practices and perspectives on the bushmeat trade.

Groups of participants referred to as stakeholder groups in these case studies are a category comprised of those that affect or are affected by the system and can be distinguished from other groups based on a particular activity, interest, or type of interaction with the system. As this definition is not strictly based on different levels of knowledge, members of diverse stakeholder groups can fall nearly anywhere on the graph of local and domain knowledge. Obviously, this means that members of the other groups, such as lay people, experts and practitioners, can be considered members of a diverse stakeholder group. Criteria for whether a study included stakeholder groups was whether

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the study specifically identified them as such for the reasons of differentiating multiple groups for comparison, potentially including groups that did not fall within the other three categories. For example, Samarasinghe and Strickert (2013) attempted to integrate the knowledge of managers, workers, local experts and recreational users on the effects of natural disasters in an alpine ski field. In this case, recreational users do not fit into a defined category, and since multiple groups are being differentiated and compared, I consider the study to focus on diverse stakeholder groups in addition to local experts and practitioners. In another example, Henly-Shepard, Gray, and Cox (2015) studied social learning outcomes of a modeling process that engaged a pre-existing committee of residents, businesses, local organizations, law enforcement, and government representatives to work closely together on elucidating and integrating their diversity of knowledge regarding the outcomes of tsunami scenarios. I also considered these groups as diverse stakeholders because the purpose of the committee was to form a collection of diverse interests, and none of the groups involved were composed explicitly of experts or practitioners focused on specifically on the system of tsunamis.

As noted, these types are not meant to be mutually exclusive, but to describe how researchers classified the sources of knowledge for modeling. Reality is not as clear cut, considering that an individual participant may play multiple roles within a system, for example a local flood manager who is a hydrologist and lives and works within the system being studied. Researchers must take this into account, and structure their participant selection and knowledge gathering methods to capture and integrate

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knowledge that represents the appropriate perspective. Since it is likely difficult or impossible to separate the individual influences that multiple roles will play on an individual’s overall perspectives, a researcher attempting to draw distinctions between groups should be aware of these complexities and discuss the limitations and opportunities they provide. In my analysis, researchers made their classifications of participant roles very clear by how they referred to participant groups linguistically and how they chose to structure the analysis, but did not often discuss the issue of knowledge classification.

A second challenge in purposeful sampling is to determine the appropriate sample size. This is typically done by monitoring if additional mapping studies continue to add new insights until interviews or models yield little new information. When only minimal or no new insights are contributed by newly added participants, the point of saturation is reached and the data collection is completed - in many FCM (and other qualitative studies) this occurs at approx. 30 maps (Jetter & Kok, 2014). When respondents are permitted to freely define concepts, saturation can be formally measured by tracking the number of new concepts introduced in subsequent interviews and estimating an accumulation curve of concept. No new respondents are added to the sample when the estimated number of new concepts introduced per interview falls below a minimum threshold (see Özesmi and Özesmi 2004). In many FCM studies, however, researchers are constrained by practical limitations, such as time, budget, respondent availability, and

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the need to include particular individuals in their study. Sample selection based on formally estimated saturation is therefore rarely reported in the literature. Participant Involvement

The ways in which participants are involved in a study can also vary based on methods for achieving the research objectives of a study. To properly frame a study, researchers may begin with initial interviews meant to determine the appropriate sample population (such as in Nyaki et al. 2014) or define some of the major concepts used in the FCM’s (Murungweni, Wijk, et al. 2011). The information from these initial interviews can play a very essential role in properly structuring the next stages of the research.

Model building is a phase that can take different forms for the participants involved in the research. Depending on the research objectives and other constraints of the project, the researcher may choose to involve the participants in building individual or group FCM’s. For example, as pointed out by Gray, Zanre, and Gray (2014), individual models may serve to provide a more equitable and diverse representation of knowledge within a community than group models due to issues with power dynamics within a group modelbuilding process. Thus, if the goal of the study is to study knowledge diversity, an individual modeling approach would be preferable. If time and resources are limited, a group model-building exercises in which multiple, distinct groups co-create FCMs may provide a less time-intensive and lower-cost method. However, this group modeling

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approach may come at the cost of understanding individual perspectives that contribute to a group model.

If the objectives of the study include shared learning and consensus, a group modelbuilding process may be a very useful tool for stimulating discussion regarding differences in knowledge and perspectives, and allow negotiation of a shared perspective (Gray et al., 2014). It should be noted that although some authors have deemed the process of combining individually-constructed FCM’s as the “formation of a consensus social map” (Özesmi and Özesmi 2004), this is only a simulated consensus rather than a true, socially-constructed consensus. A socially-constructed consensus can only be reached through a discussion and negotiation process among participants, as described in Innes and Booher (1999).

Scenario analysis is a common practice in FCM. One of the strengths of FCM is its ability to run various scenarios on a system model that has integrated diverse knowledge and gain insight into system dynamics, sensitivity of components, and system states (E. I. Papageorgiou and Salmeron 2013; Aguilar 2005). In a participatory context, running scenarios on models of participant knowledge can be used to simulate the dynamics of mental models, since a model of an individual’s knowledge will represent their unique perspectives, priorities, and functional understandings of the system. Van Vliet, Kok, and Veldkamp (2010) engaged participants in developing and running a variety of scenarios

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on their FCM’s to discuss likely future outcomes and stimulate social learning about participants’ diverse mental models.

Interactions with participants

Since participatory modeling as a practice is largely concerned with creating connection and collaboration between scientists and non-scientists, an important aspect of the process is contact and communication between the researcher and participants (Voinov and Bousquet 2010; Röckmann et al. 2012). In analyzing the interactions between researchers and participants in participatory FCM case studies, it is difficult to create a comprehensive metric for connection and collaboration. Quantitatively, it could be measured by the number of times that researchers engaged with the participant community during the process. The more interactions that take place suggests that communication and collaboration are present in the relationship between researchers and participants. The quality of these interactions, while also essential in strengthening relationships and trust, is not as straightforward to measure, and would likely require additional data collection from interviews with participants. While I limit the dataset to quantity of interactions for practical reasons, description of the quality of interactions is a valuable topic for future studies.

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Process

The process of conducting participatory research using FCM includes of a variety of important design decisions that derive from the study purpose and intended outcomes, and can help to characterize a researcher’s approach. Özesmi and Özesmi (2004) describes a comprehensive, nine-step process that is very model-focused in its description of how FCM’s are built, validated, modified, analyzed and applied to research. Jetter and Kok (2014) expand upon the considerations of project design and participant involvement in their six-step approach, broadening the contextual basis of FCM in the research process. Because the previous sections on purpose and partnership covered some of the processual steps described in this literature (e.g. determining objectives, sample population, and sample size) I focused more on the technical aspects and design process that determine how FCM’s are built. The rubric elements in this category include: 1.

Knowledge Capture

2.

Standardization and Condensation

3.

Aggregation

4.

Structural analyses

5.

Comparative analyses

6.

Simulation and Testing

During knowledge capture, participants and researchers define the concepts and relationships of the system under study as a weighted cognitive map. During modeling,

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these participant inputs are subsequently translated into a FCM model. This requires that cognitive maps from different participants are aggregated, concepts are standardized, and cognitive maps are modified to represent the intended scope of the model. Moreover, the resulting FCMs are analyzed with regard to their structure and tested with regard to their dynamic behavior to ensure that they reflect system knowledge in a way that matches with researchers and/or participant’s overall conceptions of the system. Once the FCM model is created and fully calibrated, it is used for simulation in order to analyze the impacts of different input scenarios (e.g. decision alternatives, policies) on the state of the represented system and thus support decision making.

Knowledge Capture

How the concepts and relationships are determined in a participatory FCM study is critical, as these reflect the factors included in the model of a functioning system and the information that models contain and convey in a participatory process. In some modeling processes, concepts are determined entirely by participants and unrestricted, which is known as open concept design (see Çelik, Özesmi, and Akdoğan 2006 and Douglas et al. 2016). While the researcher will determine the context of the model by specifying the system being modeled, and sometimes the boundaries of this system, participants are allowed to determine what concepts are included. This approach provides very little restriction in the capture of participant knowledge and definition of knowledge domains, and can be especially beneficial if little is known about the system being modeled.

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However, the time-intensive nature of this approach can be a concern especially if large sample sizes are being used (S. A. Gray, Zanre, and Gray 2014a).

In another approach, all or some of the concepts pre-determined. In this approach, the researcher has a greater degree of control over how the system is defined and multiple models can be aggregated to a meta FCM that represents all individual perspectives. This could be viewed as a spectrum; in which on one end all components are predefined and participants only determine relationships, to the other end, in which participants are presented with one or a small number of concepts that can then built upon in an unrestricted manner. However, there is more nuance involved when the source of these predetermined concepts is considered. While it may be the researcher determining predefined components, they can also be determined by participants in an earlier stage of research, before the intent of building an FCM is introduced. Rajaram and Das (2010) incorporated expert and practitioner perspectives on sustainability, restricting the concepts in model-building to those pre-determined by the same community in a previous study that did not involve FCM-building. Pre-defining concepts can be more efficient than an open-concept design with regard to time input for model building and modification (described in the following sections), and useful in research that wishes to focus on very specific system elements, or in which a sufficient knowledge of the system already exists. However, it restricts the diversity of knowledge being captured from participants, and can more heavily influence how this knowledge is contextualized based on researcher input and interpretation (S. A. Gray, Zanre, and Gray 2014a).

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Standardization and Condensation

There are many instances in which FCM’s will require direct modification by the researcher, for instance to standardize the components among models or to condense the model into a less complex or more useful form. If models are to be combined to form a larger FCM (see item 7. Aggregation below) or compared to assess the differences among models, it may be necessary to normalize the language used to describe components. Linguistic representation of concepts is tied to mental models in a relatable but complex manner (Miller and Johnson-Laird 1976), and thus participants may use different language to describe the identical or similar concepts (Özesmi and Özesmi 2004). Normalizing language requires some degree of interpretation by the researcher, which has been cited as a possible source of inaccuracy in how mental models of participants are represented (see Giordano et al., 2005). Alternatively, to ensure that concepts are correctly interpreted according to the perspectives they represent, participants can be engaged in the standardization of concept language.

It may also be necessary to standardize relationships between components in order to combine models or present a more concise and meaningful model. In combining models, it may be desirable that the direction of causal relationships between two components is identical in all models (Özesmi and Özesmi 2004). This can be accomplished by inverting both the relationship directions and sign of relationship. In causal concept mapping using signed relationships, a positive relationships in one direction is considered

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identical to a negative relationship in the opposite direction, and mathematically preserves the dynamic behavior of a model (Kosko 1986). While this modification is shown to preserve model functionality, it requires some consideration of how to preserve the logical structure of the model, for example the meaning conveyed in direction and sign of a relationship (Zhang and Chen 1988; Özesmi and Özesmi 2004), or the rational sequence of events.

FCM’s may also require standardization in terms of non-structural, contextual aspects of the components. One example of this is the timescale at which the components operate. Although FCM is a non-temporal modeling technique, there should be some attempts to understand the logical and functional implications of casual relationships between components that operate on different timescales. They may introduce temporal inconsistency within the model that renders simulation meaningless. If simulation using the FCM is meant to portray system behavior in a reasonably accurate manner, the model may require normalization of concepts.

Condensation of models can fulfill different purposes, for example grouping or reducing concepts down to variables that are directly relevant to a specific problem or decisionmaking context that the FCM will be used to solve. Nakamura, Iwai, and Sawarag (1982), demonstrated the benefits of condensing a very complex cognitive map of domain knowledge into a targeted causal structure of components that were directly relevant to a decision-making context. Some researchers in my dataset chose to condense their model

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on the basis of creating comprehensible visual representations of a system that could serve as effective discussion and learning tools (see Tan and Özesmi 2006 and Hobbs et al. 2002). While it is widely recognized that this requires a degree of interpretation by the researcher, and may introduce bias in terms of FCM structure, it is also found to be necessary to achieving research goals, such as in the cases above (Özesmi and Özesmi 2004).

Both of these methods may apply in attempts to address issues of functionality and concept relevance within the simulation process (described in 10. Simulation and Testing). It is possible that simulation of system outcomes using FCM, depending on the matrix structure of the model, leads to chaotic behavior in which the model does not reach a consistent steady state. This negates the usefulness of the FCM for studying system behavior or system state outcomes in participatory applications (Jetter & Kok, 2014; Özesmi & Özesmi, 2004). In these cases, changes to the FCM must be made through analysis and modification of the model.

Aggregation Depending on the objectives of the study, many researchers combine multiple FCM’s into aggregate models. One common purpose for combining FCM’s is to create a more complete system representation of a system that integrates the specialized knowledge of a diverse group of participants (see Rajaram and Das 2010 and Vanwindekens, Stilmant, and Baret 2013). They may also be combined into multiple group or community models 57

for the purpose of generalizing and comparing the mental models of these groups (see Jetter and Schweinfort 2011 and Halbrendt et al. 2014).

Jetter and Kok (2014) describe two different approaches to aggregating models as quantitative or qualitative. Quantitative aggregation of FCM’s involves translating FCM’s into adjacency matrices, summing the strength of relationships among each pair of components, and then re-scaling these relationships back to a -1 to +1 scale. This provides a simple method of averaging the relationship between each pair of components defined by each member of the sample population. However, an issue with this technique lies in combining opposing perspectives, for example if one respondent defines a positive relationship + 1 between two components while another respondent defines a negative relationship of -1. The resulting numerical values after aggregation will reflect no causal relationship, or if unbalanced will cancel out one perspective and lessen the stronger signed relationship. While this can create logical inconsistencies in a model with a small number of participants, it would not be an issue in an aggregate model that includes a large sample population that demonstrates a ‘wisdom-of-crowds’ outcome, in which the prevailing perspective is more often true (Jetter & Kok, 2014; Surowiecki, 2004).

Qualitative aggregation, as described by Özesmi and Özesmi (2004) and Jetter and Kok (2014), can be accomplished by grouping similar concepts together into encompassing variables, and creating a new aggregated map that summarizes the relationships between these variables. Both quantitative and qualitative methods often make use of

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standardization and condensation techniques, discussed in the previous section. In my rubric, I characterized options with regard to FCM aggregation as a) aggregation into multiple group models, b) aggregation into single, “whole system” or meta models, or c) not aggregated. I also assessed whether aggregations were made either quantitatively or qualitatively.

Structural analysis

A common way for researchers to assess and compare FCM’s at the structural level is to use a set of graph theory indices commonly used in many types of network analysis (Gray et al., 2014). A summary of these metrics from Gray et al. (2014) can be seen in Table 2.4, and more information about these metrics is available from the aforementioned paper and Özesmi and Özesmi (2004).

Comparative analysis

Comparing FCM’s created by groups or individuals can illuminate key differences in knowledge and perspectives that represent the degree of similarity of mental models. Structural comparisons can be more qualitative in nature, based on general observations related to types of concepts included and relationships present (see Douglas et al., 2016), or they may focus on the structural metrics previously discussed and use statistical

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analysis to more quantitatively describe differences among models (see Mouratiadou and Moran, 2007).

Testing and Simulation

One of the main benefits that sets FCM apart from other modeling techniques is its ability to integrate simple, qualitative representations of diverse mental models into quantitative models that can be used to simulate the system’s dynamic behavior. This allows researchers and participants to learn about the overall system and dynamics (e.g. the different stable states it can reach and the variables that drive the system behavior), to test hypotheses about the impact of concept changes on other system elements, and to improve their decision-making by selecting alternatives that result in desired system states. To achieve these objectives, the FCM model first needs to be verified and tested.

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Table 2.4. Graph theory indices used as structural metrics from Gray et al. (2014)

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This step ensures that it provides a good representation of the system knowledge it encodes and exposes the same dynamic behavior as the real-world system it represents. To this end, a variety of testing strategies, including the testing of so-called dynamic hypotheses, comparison between model and real-world data, and sensitivity analyses are available to modelers (Jetter & Kok, 2014; Papageorgiou, Markinos, & Gemptos, 2009).

Sensitivity analysis of the components included in an FCM can be used to provide additional information about system behavior and simulation outcomes (Htun, Gray, Lepczyk, Titmus, and Adams 2016). By running many iterations using various beginning state vectors, similar to the process in scenario analysis, researchers are able to determine how the dynamics of the system affect particular components, and enables them to monitor the degree to which concept values change under different conditions (Jetter & Kok, 2014). This process is used to assess the validity of the assumptions of the model, and may also be used to evaluate the cascading dynamics that changes to the system have upon concepts that are of importance to the researchers or participants (see Ramsey and Norbury, 2009 and Tan and Özesmi, 2006).

The tested model can then be used to answer ‘what-if” questions by calculating the new stable system states that result from changes to the system that are represented by the initial state vector. Groups can thus test how variables that they are particularly interested (e.g., because they represent environmental trends, different policies, or decision

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alternatives) impact other variables in the system. Naturally, not all system states are equally desirable, which is why participants are often asked to describe which components of the model should increase, decrease, or stay the same, based on their priorities. This information helps the group to interpret simulation results and support decision making. For example, the simulation may show that two alternative interventions (represented by two different initial state vectors) both lead to equal improvements with regard to a stated objective. However, they may have different impacts on other desirable variables and the team would be well advised to select the intervention that improves the variables it aims to improve without deteriorating other aspects of the system (Gray et al., 2015).

Simulations can be used to predict the future state of a system For example, (E. I. Papageorgiou, Markinos, and Gemptos 2009) predict future cotton yields, based on an FCM model of an socio-ecological system. In many cases, however, FCM studies do not aim to predict the future but explore a range of equally plausible, alternative futures thay may occur as trends unfold (Jetter & Schweinfort, 2011; Salmeron, Vidal, & Mena, 2012; van Vliet, Kok, & Veldkamp, 2010). In this context, FCM are not used to explore the impact of the modeling team’s decisions on the system, but to provide the backdrop for planning discussions by showing how uncertain external factors could impact the system under study, referred to in this study as scenario analysis. In this context FCM provide a valuable connection between the rich narratives that are required in scenario studies, and the quantitative rigor and complexity that is provided by model calculations (Jetter &

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Kok, 2014). FCM-based scenario techniques for future studies vary between authors (e.g. Jetter & Kok, 2014; Jetter & Schweinfort, 2011; Kok, 2009; Kok & van Delden, 2009; Papageorgiou et al., 2009; Salmeron et al., 2012). The basic steps are described in Jetter & Kok 2012.

Simulations are thus used in varied contexts. They can define the dynamic behavior of the system and comparison of outcomes that result from policy, management or environmental change scenarios in a way that supports decision-making and prioritization of management strategies (Berbés-blázquez, 2015; Çelik et al., 2006; Gray et al., 2012; Papageorgiou et al., 2009)

Products

For my review and creation of a typology, I assessed the products of the participatory modeling process by using the same basic categories as the purposes: a) More complete model of system b) Perspectives/differences between groups identified c) Shared learning occurred d) Better group understanding/consensus e) Others We also sought further detail on the outcomes from the text to illustrate the broader implications of these outcomes, such as how this outcome was utilized or leveraged by participants. For example, in Çelik, Özesmi, and Akdoğan's (2006) study of a lake system 64

under different management scenarios, the knowledge outcomes were incorporated into a management plan for the lake.

Results and Discussion

Using the rubric developed from the 4-P’s framework, as described above, I were able to observe trends in the use of participatory FCM for research. These data allowed us to better understand the current state and norms of the practice, identify needs and areas of future research, and create a typology of approaches and summary of their benefits and tradeoffs.

Purpose

The most common research purpose within my dataset could be described as enhancing system knowledge, accounting for 20 of the 33 case studies evaluated. Understanding participant variation was the second most common outcome, with 13 studies stating this as a main purpose. All other research purposes (shared learning, consensus and increased participation) were only present in nine or fewer of the studies. This finding suggests that direct outcomes for participants have been less common in participatory FCM than those related to a better understanding of the system and its physical and social components, which appears to be more useful in an academic sense and not surprising given the

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dataset reviewed. An explicit purpose of demonstrating or advancing specific methodologies was mentioned in 11 of the studies (Table 2.5).

Voinov and Bousquet (2010) summarize that the main drivers of participatory modeling research as a combination of the need to represent and share knowledge and understandings and a need to clarify and support decision-making for a particular issue. Over half of the studies whose purpose was to increase system knowledge also identified one of the other purposes (shared learning, participant variation, consensus-building, or increased participation) as an important driver of the research. Nearly all studies expressed an intention to develop this enhanced knowledge for the purposes of management, policy, or decision-making process, regardless of whether the research was meant to directly play a role. These findings imply that the broader motivations of participatory FCM, in terms of Voinov and Bousquet's (2010) two objectives of participatory modeling, are to use an enhanced system knowledge to support applied processes that aid in management, policy, and decision-making.

Similarly, Jones et al. (2009) suggests that the three main functions for involving stakeholders in participatory processes can be summed up as normative, substantive, and instrumental. Normative functions focus on the legitimacy of modeling process. By including a range of stakeholders, researchers hope to create social learning and greater acceptance of outcomes. Substantive functions refer to the idea that integrating diverse stakeholder knowledge will improve overall understanding of the system and its social

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and ecological components, resulting in better problem-solving capacity. Instrumental functions attempt to increase collaboration and reduce conflict among modelers, stakeholders, the public and other involved parties. Regarding FCM’s specifically, Codara (1998) describes the main purposes as explanatory when dealing with model of behavior and underlying motivations, predictive when applied to understanding future outcomes and actions, reflexive when used to examine the assumptions behind representations of a system, and strategic when used to better represent a complex system (E. I. Papageorgiou and Salmeron 2013).

Based on these defined functions of participatory modeling, it is clear that the practice of participatory FCM has greatly favored substantive functions meant to clarify system function and mental models present in the social components in a way that incorporates elements of each of Codara's (1998) purposes. Enhancing system knowledge and understanding stakeholder variation are both related to better understanding the system and its components. Social learning (a normative function), consensus-building, and increasing participation (both instrumental functions) are repeatedly highlighted in the literature as processes that are essential to addressing complex issues faced globally (Adger, 2010; Biggs et al., 2011; Jones et al., 2009). These ideas are commonly cited by researchers involved in using FCMs to study social-ecological systems, and provide much of the rationale for their use of participatory methods. It then follows that a main concern of this field of science should be to better inform stakeholders and decisionmakers through the use of these normative and instrumental processes.

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Partnership

Initial interviews were only conducted in 11 of the 33 studies. These interviews fulfilled a variety of purposes, including determination of problem context or initial model concepts and identifying or validating the target population. Regarding sample populations, diverse stakeholder groups were the most common with slightly over half of the studies including this type of participant. Local experts were utilized the least (seven of the studies) and domain experts and practitioners were both engaged in 12 studies (Table 2.5).

Individual model building was the most common participant activity, used in 26 of the studies, while group model building was only used in 11. There were several studies incorporating both individual and group model building in different points of the research (see Papageorgiou, Markinos, and Gemptos 2009 and Rajaram and Das 2010). Involving participants directly in the scenario analysis process was uncommon, occurring in only three studies in my dataset (see Gray et al., 2015, Murungweni et al., 2011 and van Vliet, Kok, and Veldkamp, 2010) (Table 2.5). In 25 of the studies there was only one specified interaction with the participants, and no study specified more than three interactions in the course of research.

Our findings indicate that participants were most often involved in only one aspect of the modeling process: the knowledge gathering phase. This was most often the only

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interaction with participants in the vast majority of studies. While some utilized initial interviews with participants to define the sample population, domain, or parameters of the model, these aspects of research scoping were most often decided only by the researchers. Expanding the role of the participant in this manner could be a step forward in increasing the participatory nature of the research; allowing identification of the appropriate sample population to be participant-driven and creating a knowledge space that best captures what participants consider the most important aspects of the system being studied.

The finding that participant involvement in scenario analysis was uncommon within the dataset further illustrates the potential for expanding the role of participants in the research. An additional interaction in which participants could be involved in scenario analysis could potentially allow trouble-shooting to ensure that the model outcomes are realistic based on local knowledge of the system, encourage greater learning about system dynamics and system state outcomes, and stimulate discussion of system function that creates social learning and development of a shared knowledge-base (Jetter and Kok, 2014; Gray et al., 2012; Papageorgiou and Salmeron, 2013). These attempts to further integrate participants into the modeling processes could, in other words, provide a more robust approach to research for any of the study purposes identified.

The case studies in my dataset favored engagement of diverse stakeholder groups in the overall dataset, and for many of the types. The knowledge and perspectives among

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diverse stakeholder groups is expected to vary and potentially disagree, since each distinct group is functioning based on mental models that reflect their unique experiential and social influences (Johnson-Laird, 1983; Jones et al., 2011). Therefore, this design decision fits well with the intent of eliciting a great diversity of knowledge about a system in which many diverse interests exist. The types that were an exception, or those in which fewer studies focused on diverse stakeholder groups, were: •

Group modeling with domain experts to increase system knowledge

•

Modeling with practitioners for consensus-building

In group model building for system knowledge, which engaged domain experts, the studies typically focused on management and policy dimensions of food production (Nyaki et al. 2014; E. I. Papageorgiou, Markinos, and Gemptos 2009; Rajaram and Das 2010) or ecological food webs (Ramsey and Norbury 2009). In these applications, the desire to accurately model the complex processes of agricultural management and food web dynamics required use of specific knowledge domains. With the concerns expressed by some authors and participants regarding the accuracy of models constructed with stakeholder knowledge, domain experts could be considered a source of knowledge that can most accurately and cohesively depict specific detailed system functions.

Studies that included modeling with practitioners for consensus-building, similar to the previously discussed group of studies, focused mostly on some aspect of agricultural management (Douglas et al. 2016; Fairweather 2010; Fairweather and Hunt 2011). For consensus-building in a system-management context, focusing on those that have the

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decision-making capacity to manage the system is the logical choice for sample population. In a system as specialized as an agricultural system, many stakeholders may not be more than tangentially aware of how the system functions, or have any agency to influence its management. Therefore, it may be that practitioners represent the most important participants in an effort to build consensus on management and policy approaches.

It is also important to note that in many FCM studies researchers are constrained by practical limitations, such as time, budget, respondent availability, and the need to include particular individuals in their study. Therefore, sample selection based on a formal estimate saturation, which is considered important in gathering the knowledge to fully represent a system, is rarely reported in the literature.

Process

In 21 of the studies, the concepts were unrestricted and determined entirely by participants, which is known as open concept design (see Çelik, Özesmi, and Akdoğan, 2006 and Douglas et al., 2016) (Table 2.5). Rajaram and Das (2010) conducted the only study in my dataset in which the concepts were pre-determined going into the FCMbuilding process. In this study, concepts were determined by participants from the same community in a previous study, and then the relationships were later mapped. In this way,

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the map was a pre-determined concept design, but was entirely constructed by participants.

Eleven of the studies used a partially predetermined concept design. In many of these cases, the researcher allowed participants to add concepts to the FCM, however the researchers prompted the participants or started the mapping process with a set of concepts (see Fairweather, 2010 and Mouratiadou and Moran, 2007). In some cases, this was meant to stimulate ideas in a group FCM process (see Nyaki et al. 2014), and sometimes to ensure that FCM’s were constructed around a particular central theme (see Isaac, Dawoe, and Sieciechowicz 2009).

Aggregating FCM’s into groups for comparison was common, with 12 studies taking this approach exclusively. Seven studies aggregated FCM’s into a whole-system map, and two of these studies (Gray, Chan, Clark, & Jordan, 2012 and Özesmi & Özesmi, 2003) used both methods for different sections of their study. Quantitative and qualitative aggregation methods were used equally in the aggregation process. Only six of the studies in my dataset did not use structural metrics to describe or compare the FCM’s created by participants. The most commonly used metrics were the number of concepts and connections and density of the FCM’s, which were used in over half of the studies. Centrality of components within the FCM’s was also common, used in just under half of the studies. Hierarchy index was the least-used metric, found in only six of the studies.

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Comparing FCM’s among participant groups or individuals was a very common practice in my dataset, with 20 of the studies conducting some form of descriptive or quantitative analysis. Only one study compared only the relationships, as the concepts were predetermined from a previous study with the same stakeholders (van Vliet, Kok, and Veldkamp 2010). Scenario analysis was by far the most common type of model simulation conducted, with 22 of the studies using this procedure. Three studies in this dataset analyzed sensitivity of FCM components as part of their analysis. A total of five studies in my dataset included system state or desired state calculations into their analysis (see Kontogianni et al. 2012 and Özesmi and Özesmi 2003).

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Table 2.5. Rubric element frequency by number of studies in descending order

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With open concept design as a standard practice for most studies within the field of participatory FCM, it is clear that there is a strong concern for removing much of the influence that the researcher has over the data being collected. Those studies that used a partially pre-determined design, as mentioned, did so to ensure that the domain of the knowledge being captured was specifically defined, and usually did not go beyond introducing start concepts for participants to build upon. The partially predetermined concept design was used most often in studies that used group modeling to increase system knowledge. Facilitation of group model-building approaches can be a challenge, and pre-determination of some concepts was used to streamline the process and ensure that the model-building process stayed on topic in a situation where diverse perspectives and opinions could create tangential discussion (Gray et al., 2015; Murungweni et al., 2011; Nyaki et al., 2014).

Structural metrics in the form of graph theory indices was the most common method used to compare individual or group FCM’s, as these methods are an established method of comparing network data in many different disciplines, for example artificial neural networks, concept mapping and social network analysis (Özesmi and Özesmi, 2004; Gray, Zanre, and Gray, 2014). The use of graph theory was very useful in, for example, illustrating the change in mental models due to social learning (Henly-Shepard, Gray, and Cox 2015), illustrating differences perspectives and robustness of system knowledge among groups (Özesmi and Özesmi 2003), or identifying key concepts and relationships (Kontogianni et al. 2012).

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The trends in use of scenario analysis suggest that some distinction in process exists between studies leaning more to the applied, instrumental purposes and those that are more substantive in nature, focusing on representing knowledge. Scenario analysis was an important component of the participatory FCM process in all study types except consensus building. A greater knowledge about system function also was not typically a purpose (or outcome) that co-occurred with consensus building. As the basis for consensus has been described as social negotiation of stakeholder knowledge and understandings (Innes and Booher 1999) rather than a process for building upon knowledge, it stands to reason that these processes could be exclusive. These findings could suggest that the studies in my dataset that intended to create consensus, often in managing critical social-ecological systems (see Douglas et al., 2016; Hobbs et al., 2002; Fairweather and Hunt, 2011), did not always strive to integrate a greater system knowledge into their consensus. If a consensus is intended to underlie decision-making in a complex system, it may be advisable to also take advantage of the knowledge diversity in a participatory FCM process to base this consensus around an expanded knowledge of the system.

Products

The most common outcomes for studies in my dataset were identifying and comparing participant knowledge and understandings, found in 22 studies, and creating a more

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complete model of the system studied, found in 13 studies. All other objectives (shared learning, consensus, methodological, and others) were found in seven or fewer studies. Five of the seven studies with consensus-related outcomes also highlighted shared learning outcomes.

There were discrepancies in the frequency of most purposes and outcomes in the dataset (Figure 2.3). Characterization of participant knowledge, for example, was identified as a purpose for only 13 studies, but was identified as an outcome of 22 studies. Building complete system knowledge, conversely, was a purpose for 20 studies, but only 13 showed the outcome of a complete system model.

Our analysis indicates that participatory FCM is a particularly effective way of eliciting and learning about mental models. The data show that learning about stakeholder participant knowledge even occurred in many studies that did not state this outcome as a purpose. While the data provided some evidence that mental models could enhance system knowledge and functional understanding, it appeared that this outcome was more difficult to achieve. Studies that failed to produce what they considered a complete system model suggested various ways in which the process could be improved in future studies. Nyaki et al. (2014), for example, suggested that participant knowledge should be empirically validated in order to ensure that it is based in a correct understanding of the system. Van Vliet, Kok, and Veldkamp (2010) suggested that in cases where an aggregated, whole-system model does not fit with stakeholder perceptions once

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presented, an additional workshop should be conducted. Gray et al. (2012) suggested that new techniques for standardizing stakeholder knowledge would be beneficial, allowing for more legitimate integration and comparison.

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Figure 2.3. Comparison of frequency of purpose and product

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Typologies and Tradeoffs

With the goal of creating a typology that helps to establish standards and practices for those interested in using participatory FCM techniques, I focused on the study purpose and grouped together rubric elements that commonly occurred with each study purpose. Table 2.6 details, for each study purpose, the frequency with which different partnership, process, and product rubric elements occurred. Each study purpose was frequently associated with a particular set of study design practices related to partnership and process, as well as similar outcomes related to products. Of the 13 studies on understanding participant variation, for example, 10 used individual model building, 11 focused on diverse stakeholders, 11 compared model structure, and 13 resulted in greater understanding of the variation among participant knowledge. I found that studies categorized under the purpose of increasing system knowledge consisted of two subtypes: those engaging participants in individual model-building or group model-building. This encouraged us to identify two separate types of studies of this purpose.

Identifying the limitations of each approach to participatory FCM was an equally important exercise. Identification of the types allowed us to summarize the various challenges encountered in each approach, which is important when analyzing the benefits and tradeoffs (Table 2.7). I found suggestions from the FCM literature regarding the limitations of various approaches to be accurate (see: Gray et al., 2012; Jetter & Kok, 2014; Özesmi & Özesmi, 2004; Papageorgiou & Kontogianni, 2012). For example,

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studies involving individual model-building often mention the process being expensive and time-consuming when compared to group approaches. Group model-building approaches, on the other hand, can be challenging due to the need for effective facilitation to avoid influence of power dynamics among the participants.

Our typology and table of limitations are most immediately useful in guiding researchers through the benefits and drawbacks of different approaches to participatory FCM and identifying the appropriate methods for their purpose. In a general example, the impetus for the project may be to use an FCM co-created by a diverse group of experts and stakeholders to quickly produce a set of guidelines for decision-makers addressing a complex socio-environmental issue. Although individual modeling with open concept design may robustly represent the knowledge and perspectives of the diverse group, it may be too time intensive as it would require building each model, standardizing for comparability, and additional group work to merge models. It may be more appropriate to use an approach similar to the Complete System Model – Group Approach, in which initial concepts could be defined through pre-interviews and a facilitated group meeting could be used to construct the FCM, run basic scenarios, and discuss outcomes and recommendations. While this example may fit well within a single approach, it is important to note that circumstances may require a mixed approach in order to address the needs and limitations of a pursuit.

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Beyond establishing standards and practices for participatory FCM, this typology will also be useful in identifying ways in which the science and the practice can be further explored and improved. Shared Learning approaches, for example, represent only a small portion of the studies, and only one of these studies (van Vliet, Kok, and Veldkamp 2010) engaged participants in scenario exercises. Consensus Building relied on individual model building in most cases, when group modeling may be more appropriate since it encourages direct participant interaction. It was also shown to be successful in just over half of the cases, perhaps due to the lack of follow-up and sufficient engagement with and among participants. These cases highlight areas research in participatory FCM that are critical in advancing the field.

Conclusion

The field of participatory FCM has made a great deal of progress in developing an understanding of effective methods for knowledge capture and integration of knowledge into functional system models. Although relatively few case studies have emerged that focus on consensus-building, social learning, and other more applied appropriations of FCM, the small number available suggest that standards and norms are beginning to emerge in these areas as well. Through analysis of a comprehensive dataset of participatory FCM studies and identification of several common types, this paper has contributed to clarifying the standards and norms that are emerging in the field of participatory FCM. My intent is that this knowledge will serve as guidance for current

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and future users of participatory FCM, and make explicit many of the benefits and tradeoffs of tested approaches.

Table 2.6. Summary of participatory FCM typologies Purpose

Partnership

Process

Products

Understanding Participant Variation (n=13)

Individual interviews (n=7) and model building (n=10) with diverse groups of stakeholders (n=11)

Open concept determination (n=8), often aggregated into group models

Very effective in studying variation in participants’ knowledge and understandings (n=13)

More Complete System Model Individual Approach (n=15)

Individual modeling with Domain Experts (n=7), Local Experts (n=6), and/or Diverse Stakeholders (n=7)

Open concept determination (n=10)

More Complete System Model Group Approach (n=8)

Group modeling with Domain Experts (n=5)

Partially Predetermined concepts (n=4) or Open (n=3)

Robust comparisons among models (n=11) and scenario analysis (n=8) to study functional differences

Robust comparisons among models (n=9) and scenario analysis (n=14) for prediction or system behavior

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More complete system model (n=10) and describing participant variation (n=10)

More complete system model (n=5)

Scenario analysis for prediction or system behavior (n=6) Consensus Building Individual model (n=9) building (n=7) with no clear trend in participant types

Open concept determination (n=5) or Partially predetermined (n=4), models sometimes aggregated into consensus map (n=4) Scenario analysis (n=4) or other calculations (n=5) (ex: PCA, Resilience Metrics) conducted

Shared Learning (n=5)

Group Modeling (n=4) with Small Groups (4-13)

Open concept determination (n=5) and Scenario Analysis (n=4)

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Consensus outcomes were often achieved (n=5), but limited follow-up with groups to define next steps Participant Variation outcomes were common (n=5)

Shared Learning (n=3), Consensus (n=3), and Participant Variation (n=4)

Table 2.7. Limitations identified by researchers in each study type Purpose

Trade-offs and Limitations

Examples

Understanding Participant Variation

Can be expensive and time consuming to conduct

Berbés-blázquez, 2015; Giordano, Passarella, Uricchio, & Vurro, 2005; Kafetzis, McRoberts, & Mouratiadou, 2010; Klein & Cooper, 1982; Kontogianni, Tourkolias, & Papageorgiou, 2013; Mouratiadou & Moran, 2007; Özesmi & Özesmi, 2003

Translation and ambiguity from linguistic terms introduces researcher interpretation

More Complete System Model Individual Approach

Time consuming and many participants needed

More Complete System Model Group Approach

Power dynamics in group situations may bias model

Disparities in participant perspectives may introduce uncertainty in model

Group knowledge alone did not produce a satisfactory model in some cases

Consensus Building

Merged models do not necessarily represent a real consensus achieved through group discussion/negotiation 86

Douglas et al., 2016; S. Gray, Hilsberg, McFall, & Arlinghaus, 2015; Murungweni, Wijk, Andersson, Smaling, & Giller, 2011; Nyaki, Gray, Lepczyk, Skibins, & Rentsch, 2014; Elpiniki Papageorgiou, Stylios, & Groumpos, 2006; Rajaram & Das, 2010; Ramsey & Norbury, 2009; van Vliet, Kok, & Veldkamp, 2010 Çelik, Özesmi, & Akdoğan, 2006; S. A. S. R. J. Gray et al., 2014; S. Gray, Chan, Clark, & Jordan, 2012; Halbrendt et al., 2014; Isaac, Dawoe, & Sieciechowicz, 2009; Jetter & Schweinfort, 2011; E. Papageorgiou & Kontogianni, 2012; Samarasinghe & Strickert, 2013; Tan & Özesmi, 2006; Vasslides & Jensen, 2016 J. R. Fairweather & Hunt, 2011; J. Fairweather, 2010; Hobbs, Ludsin, Knight, Ryan, &

Doubts over accuracy of model for decision-making Shared Learning

Need good facilitation to avoid group power dynamics Need to engage and communicate among diverse groups

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Ciborowski, 2002; Kafetzis et al., 2010; Klein & Cooper, 1982 Douglas et al., 2016; Henlyshepard, Gray, & Cox, 2015; Elpiniki Papageorgiou et al., 2006; van Vliet et al., 2010; Vasslides & Jensen, 2016

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CHAPTER 3 UNDERSTANDING MENTAL MODEL DIVERSITY OF FLOOD MANAGERS IN BOSTON, MA THROUGH THEMATIC ANALYSIS OF FUZZY COGNITIVE MAPS

Introduction

Global climate change is expected to drastically alter patterns of precipitation worldwide, causing many issues related to storm hazards and flooding (IPCC 2014). Advanced climate models have predicted more frequent and intense extreme precipitation and hurricane events in the Northeastern United States (Barlow 2011; Trenberth 2011). These projections are particularly concerning for coastal cities in this region that are already vulnerable to flooding and other storm-related issues (Hallegatte et al. 2013; Hunt and Watkiss 2011), such as Boston, Massachusetts. Boston has experienced many damaging and costly storm events that have compromised infrastructure, critical services, and environmental health. For example, the Blizzard of 1978 was estimated to have caused $550 million in property and infrastructural damage and $95 million in emergency costs along the coast of Massachusetts (Kirshen, Knee, and Ruth 2008). In March of 2010, an extreme precipitation event caused flooding in Boston and many other areas of Massachusetts and the Northeastern United States, resulting in deaths, $10.7 million 103

dollars in property in South Boston and sewage treatment overflows in nearby municipalities (National Oceanic and Atmospheric Administration 2010).

Superstorm Sandy represented a turning-point in the dialogue about mitigating the impacts of these types of extreme weather events. While the storm reached metropolitan Boston during a low tide, and thus had considerably few impacts, it’s catastrophic effects in New York City served as a warning of the risk involved with more frequent, intense storms (Powell, Hanfling, and Gostin 2012; Douglas et al. 2013). Initiatives intended to enhance resilience and adaptation to future conditions found increasing support within the community of decision-makers working on storm and flood-related issues. Recent interviews with many key flood managers in Boston focusing on management and adaptation to extreme weather impacts revealed that although many advances in management of extreme weather and flooding have occurred over time (Mertz 2016), there are still many gaps that need to be addressed in the adaptation process, including: a. alignment of perspectives and priorities across scales b. collaboration across projects and sharing of resources c. greater shared understanding of complex interactions between social and environmental processes

Together, these issues involve social and cognitive processes, or the ‘soft’ dimensions of management. These interviews themselves have revealed that addressing these gaps will involve collaborative processes that emphasize the knowledge and understandings of

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diverse stakeholders. Participatory modeling is an approach that offers many tools and techniques with potential to address these aspects of the management process.

The 1960’s and 1970’s marked a conceptual shift in many areas of environmental governance and decision-making away from top-down, centralized approaches and toward broader stakeholder participation (Healey 2003, 1997; Kapoor 2001). Practitioners and scholars have observed that top-down decision making often fails to account for the high complexity of systems with interlinked social and environmental dimensions, resulting in ineffective and inequitable approaches to problem-solving (Innes and Booher 2008; Kapoor 2001; Funtowicz and Ravetz 1991). These shortcomings have led to interest in more democratic and inclusive approaches with an emphasis on including diverse stakeholder knowledge and perspectives (Berkes and Berkes 2009; Berkes, Folke, and Gadgil 1995; Bodin 2017). With these realizations as a guiding framework, participatory modeling was conceived as a stakeholder-driven approach to model building focused on development of modeling tools that aid in collecting and utilizing diverse knowledge and understandings (Voinov and Bousquet 2010; Voinov et al. 2016; Van den Belt 2004; Pfaff et al. 2016).

Participatory modeling approaches are often focus on accurately representing and operationalizing perspectives and understandings (Voinov and Bousquet 2010; D’Aquino and Bah 2013), and thus provide a means of engaging stakeholder mental models, which are defined as each individual’s internal representation of the world that is iteratively

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constructed through observation and experience (Doyle and Ford 1998; Jones et al. 2011). While there are many different avenues for understandings mental models and their applicability to research in human and natural systems, one widely accepted and utilized framework describes mental models as a mechanism of reasoning that employs networks of functional associations among mental objects, or complex webs of causeand-effect (N. a. Jones et al. 2011; P N Johnson-Laird 1980; Bower and Morrow 1989; Carley 1997). This framework originates from cognitive psychology. Tolman's (1948) experiments in rats supported the presence of an internal cause-and-effect ‘map’ that influenced decision-making. Later, Axelrod (1976) used this cognitive map concept to understand and map logical structures underlying human behavior.

FCM, a form of neural network modeling, is a modeling technique that is gaining increased attention for its ability to represent mental models in a manner theoretically consistent with the neurological architecture of the brain (Zhang and Chen 1988; Kosko 1988). Mathematician Bart Kosko (1986) created FCM through integrating cognitive maps with fuzzy set theory, providing a robust mathematical platform to simulate complex causal reasoning. Underlying the network model or causal web structure of FCM is an adjacency matrix, which links pairs of concepts with numerical relationships of -1 to 1 that can be used to calculate system states and study dynamic system behavior. For an in-depth technical description, see Aguilar (2005), Gray et al. (2015), and Stylios & Groumpos (2004).

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There are an increasing number of studies in which participatory FCM is used to elicit stakeholder mental models and critical knowledge about human and natural components of complex systems (see: Özesmi and Özesmi 2004; Nyaki et al. 2014; Jetter, Gray, and Ellsworth, n.d.; Papageorgiou and Kontogianni 2012). A review of the literature by Metzger et al. (2018), presented in Chapter 2, shows five distinct purposes that participatory FCM typically addresses: 1. Enhancing system knowledge - FCM can be used to integrate diverse sources of knowledge from scientists and non-scientists into a model that better describes the functioning of a complex system (Gray et al., 2012; Tan and Özesmi, 2006; Rajaram and Das, 2008) 2. Understanding participant variation – mental models elicited from participants can be used to compare knowledge, understandings, and priorities to identify areas of mismatch and agreement explicit (Nyaki et al. 2014; Areti Kontogianni, Tourkolias, and Papageorgiou 2013; Giordano et al. 2005) 3. Shared learning – stakeholder knowledge structured into FCM’s can be highlighted in a process of social learning to transmit information or build shared knowledge (Gray et al., 2016; Henly-Shepard, Gray, & Cox, 2015) 4. Consensus-building - knowledge contained in FCM’s can aid in the negotiation of consensus through group model building or discussion and negotiation, or can be aggregated into models that simulate a community consensus (Douglas et al. 2016; Hobbs et al. 2002; Klein and Cooper 1982)

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5. Increased participation - an overarching theme in participatory FCM and the larger discipline of participatory modeling is increasing the degree and diversity of participation in research, planning and decision-making processes (van Vliet, Kok, and Veldkamp 2010; Samarasinghe and Strickert 2013), with the idea that this will provide the benefits listed above and also benefit other normative aspects, such legitimacy and acceptance of the outcomes (N. A. Jones et al. 2009)

These purposes are often utilized in combination. For example, researchers commonly use FCM to gain an understanding of variation in participant knowledge and perspectives, which can then guide consensus-building or inform construction of a more complete system model. Social learning processes using FCM are often used as a means of sharing complex system information among participants, or creating a shared understanding that underlies consensus (Metzger et al., 2018). Douglas et al. (2016), for example, conducted a case study of irrigators sharing a common water resource in which FCM was used to better understand the dynamics of human-influence on the hydrological system, and this knowledge used to identify learning opportunities and simulate the possible structure of a consensus on management of the resource. Nyaki et al., (2014) conducted group model-building workshops to understand variation in stakeholder perspectives on the bushmeat trade in Tanzania, and suggested gaps among stakeholders that could be hindering cooperation.

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These examples and others suggest that participatory FCM could be an effective means of addressing the identified needs of the flood management community in Boston, MA. Participatory FCM could help to inform a better understanding of the perspectives and priorities of various actors involved in flood management and serve as a means of discovering important opportunities for social learning and collaboration. This exploration will be guided by two distinct questions: 1. What can participatory FCM reveal about variation in perspectives among experts at different jurisdictional scales? 2. How can a better understanding of mental model diversity inform social learning and collaboration efforts?

Study Area

Boston is a highly urbanized coastal city with an estimated population of 673,184 people in 2016 (United States Census Bureau 2017). The metropolitan boundaries of Boston cover approximately 48 square miles and contain the terminus of three watersheds, the Mystic, Charles, and Neponset River watersheds, which comprise 514 square miles. These rivers currently provide drinking water to tens of thousands of people and serve as a source for water for several municipalities, while providing urban recreation to hikers, cyclists, fishermen, and paddlers and supporting considerable habitat for native fish and wildlife. (Frashure, Bowen, and Chen 2012; Charles River Watershed Association 2015; Mystic River Watershed Association 2015; Neponset River Watershed Association 2015).

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Rapid development and urbanization over time have also caused massive re-engineering of the greater Boston area’s hydrology by creating obstructions and anthropogenic controls over river levels and flow. The first dam on the Neponset River was constructed in 1634 to provide power to mills and other manufacturing, followed by over 100 more that still exist today (Neponset River Watershed Association 2015). Damming and channelization along the populated areas of all three rivers has massively transformed the hydrology of the region. Boston’s aging combined sewer and storm water systems, established before 1700, also affects the area’s hydrology (Boston Water and Sewer Comission 2015).

Boston’s extensive water resource infrastructure is overseen, operated, and modified by a range of governmental organizations at different scales (national, state and municipal), largely to preventing flooding issues, which are increasingly common and costly in the metropolitan area. A wide variety of other government organizations and NGO’s work at a wide variety of scales to mitigate the impacts of flooding through outreach, planning, and emergency response. This complex, interconnected web decision-makers at variety of scales are currently responsible for a variety of tasks related to flooding including urban planning for land use and infrastructure, communication among flood managers and the public, education and outreach for storm and flood preparedness, emergency response and resource allocation in flood situations, and adaptation research and planning.

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Individual organizations often fulfill more than one of these functions and work with others on specific projects or longer-term initiatives.

Sea level rise and the increase of storm intensity and frequency are becoming a more urgent consideration for organizations in Boston involved in urban planning, extreme event response and outreach and education. There have been a great deal of local research conducted to better understand the threat that these impacts pose (Douglas et al. 2013; Kirshen, Knee, and Ruth 2008; Mertz 2016; Cheng 2013). While some research into climate change and hazard mitigation in Boston, MA has involved stakeholder input (Douglas et al., 2013), there is a lack of comprehensive research on the state of knowledge and perspectives within the management community.

Methods

To elicit stakeholder perspectives and explore variation in mental models among flood managers in Boston, I used guidelines from Metzger et al.'s (2018) review of participatory FCM. I first conducted individual model-building sessions with individuals from various organizations involved in flood impact mitigation in greater Boston. I selected participant organizations strategically to represent a diversity of jurisdictional scales, including local organizations that operate only within the metropolitan area, watershed organizations that focus on the three watersheds that feed into Boston, state organizations responsible for flood-related activities across Massachusetts, and national

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agencies that provide resources and influence policy for coastal areas across the nation. Individuals interviewed from these organizations were involved in planning, outreach and communication, or emergency response and held a position in their organization that involved both decision-making and implementation of flood-related management activities. This ensured that participants represented a broad and informed perspective on their organization’s role in flood management.

We selected participants to contact through inquiry with known flood management professionals, internet search, and participant suggestions. The resulting set of participants included 13 organizations, three at each jurisdictional scale from national to watershed and four from local metropolitan organizations (see Table 3.1). To ensure a sufficient sample size, I constructed an accumulation curve to assess when concept saturation was reached. The number of new concepts per interview declined to only one by the eleventh interview and remained at this level for the remaining two (Figure 3.1).

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Table 3.1. List of all organizations participating in study Name A Better City Boston Harbor Now Cambridge Department of Public Works Metropolitan Area Planning Commission Charles River Watershed Association Mystic River Watershed Association Neponset River Watershed Association Massachusetts Office of Coastal Zone Management Massachusetts Department of Fish and Game, Division of Ecological Restoration Massachusetts Emergency Management Authority National Weather Service (River Forecast Center) The Nature Conservancy Army Corps of Engineers (Flood Risk Management Program)

Jurisdiction Local Local Local Local Watershed Watershed Watershed State State State National National National

While I left the determination of concepts and relationships open to the participant, I introduced one initial central concept (“Flooding”) that provided a common starting place and guided the model building process by introducing the following five categories to stimulate the addition of concepts and relationships: 1. Causes of extreme flooding a. Ex: Rain rate, antecedent conditions, ocean storm swell 2. Immediate impacts of flooding a. Ex: infrastructure damage, displacement of residents, sewage contamination 3. Long-term effects of flooding a. Ex: local economy, public health and safety, water quality 4. Management actions taken to mitigate flooding and its impacts 113

a. Ex: dam regulation, early warning system, flood modeling 5. Adaptation strategies to increase resilience a. Ex: better predictive models, resilient infrastructure, project financing

Concept Accumulation 20

Number of New Concepts

18 16 14 12 10 8 6 4 2 0 1

2

3

4

5

6

7

8

9

10

11

12

13

Chronological Order of Models

Figure 3.1. Concept accumulation curve for models in chronological order

While building the FCM’s I also encouraged participants to provide additional notes on components and relationships that they felt needed explanation and context. This allowed us a more qualitatively robust set of FCM’s to analyze and assisted in the later steps of standardization and knowledge classification.

I analyzed the basic structural characteristics of FCM’s using graph theory metrics that are commonly in analysis of FCM and other network models. These metrics, described in

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Table 3.2, describe various properties of the network structure that are useful in learning about the knowledge of participants (Özesmi and Özesmi 2004; Eden, Ackermann, and Cropper 1992).

Due to their inherent complexity of FCM’s, comparing their content often necessitates standardizing the concepts among models and reducing the number of unique concepts while preserving their meaning. This standardization process left us with a set of 105 concepts beyond the starting concept: “Flooding”. To compare the general perspectives and priorities of the participants, I classified each concept into one of five themes (See Appendix II for examples): 1. Governance – rule and policy-making for the purposes of influencing social, economic, or environmental systems 2. Environmental – natural components and environmental systems not directly controlled by institutions, such as ecological function, weather, and climate 3. Structural – physical elements of the system, such as built infrastructure and physical landform and landscape features 4. Social – processes or qualities related to institutions or populations, such as communication among organizations and health and safety 5. Economic – related to money, finance and monetary resources

These five themes allowed for a generalized comparison that could then guide more detailed analyses at the level of specific concepts and relationships. The metrics used the

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thematic analysis of mental model diversity were the frequency of concepts from each theme that participants added to their model and the centrality of those concepts, as this indicated their relative importance to the management and adaptation efforts of participants.

Participatory FCM studies attempting to compare mental models among participants often aggregate individual models into groups models by identifying characteristics, qualities, or memberships that could be associated with important variations in perspective (Gray, Zanre, et al. 2014). Since the possible mismatch of perspectives among jurisdictional scales was mentioned as a potential issue by several participants, and is identified as a persistent issue in the adaptive management literature (Adger, Arnell, and Tompkins 2005; Cash et al. 2006), I chose to aggregate individual models into groups based on the jurisdictional scale of organizations (i.e. national, state, watershed and metropolitan) (Figure 3.2). Additionally, I am not aware of any participatory FCM study has yet addressed the topic of mental model variation among spatial or jurisdictional scales. Past studies have typically addressed differences among groups and individuals based on their role in the system of study (Nyaki et al. 2014; Vasslides and Jensen 2016; Areti Kontogianni, Tourkolias, and Papageorgiou 2013), their community or geographic location (Berbés-blázquez 2015; Halbrendt et al. 2014), or other characteristics such as livelihood (Murungweni, van Wijk, et al. 2011).

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The final step of data collection was to present initial findings to a group of participants involved in model-building to observe reactions, involve them in interpretation of the results and explore whether this method of participatory modeling supports social learning. The focus group we held included representatives of four organizations; one at the watershed level, two at the state level and one at the national level. In this focus group, I refreshed the participants on the participatory modeling process, discussed steps used in model standardization and aggregation, presented initial findings from the thematic analysis of group FCM’s, discussed their reactions to the findings and conducted an exit survey with a set of open-ended questions in which they described reactions, lessons learned, and suggestions for improving the process.

Results

Graph Theory Metrics

The graph theory metrics show some distinct patterns among aggregated group models (Table 3.3). As the size of jurisdictional scale increases, there appears to be an increase in density and hierarchy index and a decrease in number of components, with the largest occurring between the watershed and state-level model. The complexity score followed this trend to a lesser degree, with the metropolitan-level model being the lowest, followed by watershed-level and then national-level, however the state model was the highest.

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Table 3.2. Metrics used in FCM analysis. Source: Gray, Zanre, & Gray (2014)

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According to Özesmi & Özesmi (2004), a higher density indicates more perceived opportunities for creating change in a system due to the higher presence of causal connections among components. According to this interpretation, watershed and metropolitan scale organizations may perceive many fewer possibilities for altering the system that leads to flooding and its impacts compared to state and national organizations.

In participatory FCM applications, high complexity describes a mental model with robust causal connections that can help identify opportunities for change in the system, indicating a perspective that is less constrained by the influence of driving forces (Eden, Ackermann, and Cropper 1992). This interpretation suggests that the state-level organizations are knowledgeable about, or have agency to utilize, a greater variety of tools and techniques for managing and adapting to flooding.

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Figure 3.2. National-level example of a group FCM

Hierarchy index indicates the degree to which the participants’ thinking relies on a formalized top-down structure, with more democratic thinking (scores closer to 0) being less constrained by a formal structure. More democratic models are thought to be associated with stakeholders that are more flexible in perspective and perceive a greater likelihood that the system can be changed (Özesmi and Özesmi 2004; Sandell 1996). Thus, the stakeholders with a lower hierarchy score, such as the metropolitan and watershed organizations, are likely to play a key role in developing effective implementation of adaptation measures.

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Table 3.3. Graph theory metrics for aggregated group models

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Thematic Comparisons

The metropolitan-scale model had a much higher relative centrality in the economic theme than other group models (Table 3.4). Many of its most central concepts were also classified as economic (Table 3.5). Of the ten most central concepts, four were economic, four were social, and two were structural. This suggests that the metropolitan-scale organizations focused more strongly on economic and social aspects of flooding in their management and adaptation efforts.

The watershed-level model showed a stronger representation of environmental and structural aspects of flooding. This model had a much higher relative centrality in environmental and structural themes and the lowest in all of other themes (Table 3.4). The ten most central concepts were composed of five environmental concepts, four structural concepts, and only one social concept (Table 3.5).

The state-level group model appeared to be more balanced in representation of themes but showed the greatest emphasis on social and governance. Compared to the metropolitan and federal models, the state model had similar levels of centrality in environmental, structural, and social themes, but a comparatively high centrality in governance (Table 3.4). While four of the ten most central concepts were sociallythemed, it included concepts from all five themes, which metropolitan and watershed models did not (Table 3.5). It is worth noting that “policy agenda”, a concept within the

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theme of governance, was by far the most central concept included by state-level organizations. The centrality score of this one concept was more than double that of any other concept in the state model.

The national group model appeared to be the most balanced compared to all other levels. It’s centrality for each theme was neither the highest nor the lowest compared to other jurisdictional scales (Table 3.4). Similar to the state group model, there were four social concepts included among the ten most central and all themes were represented (Table 3.5).

Table 3.4. Relative centrality of concepts in each theme among group models. Metropolitan

Watershed

State

National

Governance

6%

4%

16%

10%

Environmental

17%

40%

18%

22%

Structural

24%

36%

22%

26%

Social

31%

14%

32%

31%

Economic

22%

5%

12%

12%

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Table 3.5. Ten concepts with the highest centrality in each jurisdictional group model. Shorthand themes are: G = Governance, En = Environmental, St = Structural, So = Social, En = Economic. Metropolitan Education, Outreach and Communication

Watershed

State

National

En

Weather forecasts

G

Policy Agenda

St

Historical Records of Impact

En

Water Quality

En

Ecosystem Damage

G

Local Economy

En

Natural Habitat

St

Infrastructure Damage

So

St

Property Damage

So

So

Operations center

So

Loss of Life

St

Flood/Stormwater infrastructure

En

Natural/Green Infrastructure

1

So

2

So

3

Ec

4

St

5

St

6

So

Risk Analysis

St

Flood controls

So

7

So

Residence impacts

En

Contamination

So

8

Ec

9

Ec

10

Ec

Transportation disruption Infrastructure Damage

Capacity and Funding Regulations and market incentives Investment

So En

St St St

Public Health/Safety Sanitary Sewer Overflow

Infrastructure Failure Flood Resilient Infrastructure Local Predictive Models

Preparation and Situational Awareness Education, Outreach and Communication

Property Damage Land Use/Planning Decisions Displacement of People Recreation/Public Value

Ec

Local Economy

Ec

Business closures

St

Transportation disruption

So

Public Awareness

So

Local Reports

En

Riverine ecosystem

Discussion

Our comparison of mental models across spatial scales of jurisdiction revealed differences in structure and content. According to the graph theory metrics used to analyze mental model structure, state and national-scale organizations perceive a greater number of opportunities for changing and influencing the system and convey a more robust systemic knowledge of system function, especially at the state level. With state and national organizations occupying roles within the flood management community that afford greater access to resources and agency in decision-making and agenda-setting, 124

they may naturally play a role in advancing a greater range of mitigation and adaptation options.

The thematic analysis supports this interpretation in multiple ways. Local and watershedlevel organizations reflect this trend in the centrality of concepts in different themes. Both models at these scales are clearly dominant in two themes. State and national organizations, however, showed representation of all themes in their most central concepts, and had very similar relative centrality in all themes except for governance. Findings suggesting that all jurisdictional scales but national seem to uniquely specialize in different themes could have important implications for how to structure social learning and collaboration across scales. It appears that organizations at each scale have different knowledge or capacity in different areas to contribute, and therefore might may be positioned to lead projects on different types of issues in accordance with their specialization.

Local organizations spanned a relatively broad range of activities, including emergency response, long-term planning of business, community resilience and the built structure, and addressing diverse social and environmental objectives through community partnerships. The greater focus on economic aspects of the system seems to relate quite appropriately, however, to the direct and immediate impact that flooding has on businesses and the local economy, which each organization represented in some way. The other emphasis on social aspects may relate to the closer connection that their smaller

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spatial focus gives these organizations with specific neighborhoods, townships, and community groups that reside within the metropolitan boundaries.

The watershed scale was composed only of NGO’s that collaborate broadly and are often involved in long-term planning of social and structural resilience, but whose mission statements are largely geared toward local environmental issues. All three of these watershed associations were formed at different times to address water quality, environmental, or natural resource issues in their respective watersheds. State-level organizations were all governmental entities that performed a wide variety of functions. While all are involved in long-term planning and policy formation for coastal resilience and outreach and education of the public, two are involved with emergency response. The Massachusetts Emergency Management Agency, for example, operates a control center that distributes real-time information and hazard alerts and coordinates emergency resources, and the Massachusetts Office of Coastal Zone Management, among other tasks, works to ensure that emergency shoreline stabilization projects proceed in a manner that creates the least environmental impact.

The broad representation of themes seen in the national model reflects the diverse nature of the three national organizations included in the study, and their tendency to collaborate broadly across scales and types of stakeholders. The National Weather Service provides real-time information on the environmental conditions, such as precipitation rate and antecedent conditions, which contribute to the ability of organizations and the public to

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physically prepare and anticipate the need for resource allocation. The Army Corps of Engineers’ Flood Risk Management Program focuses on forming strategic partnerships in the public, private, and non-profit sectors to address a wide range of flood resilience issues. The Natural Conservancy, as a large international NGO concerned largely with environmental issues in both developed and undeveloped areas, uses a wide range of approaches such as research, education and advocacy, and works with a broad diversity of other stakeholders across the country.

It is evident that to some degree, the type of delineation that a jurisdictional boundary represents determines the type of organization that will function at that scale. Watersheds, for example, are natural features of the landscape that determine flows of water: a critical natural resource. It stands to reason that this scale would consist mainly of organizations that are concerned mainly with the natural and environmental aspects of the land. States, however, are a political delineation, and thus tend to consist of political entities. The inclusion of more government agencies in the state and national scale may explain the finding that hierarchy index is much higher at these scales, as these government organizations tend to be directed in a “top-down” manner, while the local and watershed organizations describe a more “grassroots” approach to their work. Participants suggested broadening the study by including organizations at each level that constituted a broader array of functions and organizational types. These could include NGO’s at the state-level or advocacy groups at different scales that are concerned with environmental hazards and issues of coastal resilience.

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In the focus group, participants generally agreed that these findings provided valuable insights into the differences among spatial scales. There was agreement that the five themes identified were appropriate and useful in classifying the knowledge and priorities of organizations across scales. In fact, the themes identified were nearly identical to those being used in a state-level report about hazard management that is still being drafted at the time of this writing. This shows that my thematic approach was robust and consistent with participants’ perspectives on the critical topics and factors influencing management of environmental hazards.

Participants agreed that these findings helped to identify gaps in knowledge and perspectives among stakeholders, and that discussing them with the focus group resulted in learning about how priorities and approaches differed among organizations. One example of social learning was the stronger economic focus in the local-scale model. This finding that was a surprise to some initially, but further discussion among participants led to a general acceptance and curiosity regarding the implications. Another participant articulated that the findings revealed a bias in their thinking about the priorities of different actors in the flood management community that was not previously apparent, and which they deemed important to better understand when forming collaborations. Overall, the participants expressed that the findings revealed and challenged preconceptions about the overall perspectives of organizations at different scales.

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Other outcomes of the focus group workshop included an enhanced understanding of participant mental models, which led some participants to express an increased motivation to collaborate with organizations at other scales. The exit surveys also showed that the modeling process improved participants’ ability to visualize the dynamic interactions within the flood management system, and factors that could benefit management practices.

One important consideration identified by participants was the high degree of influence that selection of participants may have had in these results. On one hand, focus group participants saw this as evidence that the participatory model-building process created an accurate representation of the participants’ mental models and could be used to understand their perspectives. However, in the context of the practical application of this research it was agreed that the 13 organizations selected may not have adequately represented a broad enough spectrum of flood management roles to provide data that could broadly characterize the entire community. Conversations with the focus group and with individual participants revealed that a better understanding could be gained by conducting more interviews and focus groups.

One shortcoming of this research was that the theme of governance was not well represented in these models. The model-building procedure itself may have focused more on the physical aspects of flooding, rather than the policies and institutional practices that greatly influence aspects of flooding such as environmental protection efforts, the built structure and infrastructure, and social resilience to flooding. The complexity of 129

modeling the governance and policy in a straightforward, cause-and-effect structure may also explain this finding.

Conclusion

Participatory FCM, as both the literature and this research demonstrates, is an effective means for extracting mental models and representing complex perspectives of stakeholders in a way that allows both researchers and the participants themselves to learn about the understandings that underlie choices and behavior (Halbrendt et al. 2014; S. A. Gray, Zanre, and Gray 2014b; E. M. Douglas et al. 2016). Stakeholder FCM’s are complex, however, and require rigorous attention to interpreting the subtle meanings of concepts and their relationships in a complex causal structure. One contribution this research makes to the practice of participatory FCM is a method of coding and classifying the diverse knowledge elicited from stakeholders to better structure the analysis and interpretation of mental models. Thematic analysis methods proved effective in forming a general interpretation of complex FCM’s and could be used to further explore and compare specific aspects of mental models. This resulted in valuable discussion by stimulating learning among stakeholders, validating some intuitive beliefs about perspectives and priorities, and challenging others.

Another benefit of this research to the field of SES is the addition of case study and exploratory methods that support participation and integration of mental models into management processes. The calls for integration of mental models into management of 130

SES is often accompanied by an acknowledgement that further development of tools for doing so are needed (Biggs et al. 2011; Folke 2006; Berkes and Berkes 2009; Plummer, Armitage, and De Loë 2013). We hope that future research will continue explore the potential of participatory FCM and thematic analysis to explore knowledge diversity in management and adaptation processes and equip the managers of social-ecological systems with tools that improve their ability to navigate the many complexities that they face in dealing with climate change and other wicked problems.

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CHAPTER 4 USING FUZZY COGNITIVE MAPPING TO CREATE BOUNDARY OBJECTS FOR SHARED LEARNING AND COLLABORATION

Introduction

Management of complex social-ecological systems (SES’s) is inherently complex, as it requires a detailed understanding of interactions among numerous social and ecological actors, organized structures to intervene at many points in the system and feedback processes that constantly provide new insights and information on system trajectory (Turner et al. 2016; Folke et al. 2010; Lebel et al. 2006). These challenges necessitate broad participation of stakeholders for generating and integrating knowledge, contextualizing and framing management approaches and building capacity for effective collaboration (Walker et al. 2004; Berkes and Turner 2006; Wyborn and Bixler 2013). It is known that centralized, top-down decision-making is often restrictive and ineffective in problem-solving (Funtowicz and Ravetz 1991; Berkes and Folke 1998; Voinov and Gaddis 2008), as it can overlook important sources of diverse knowledge, (Berkes, Folke, and Gadgil 1995; Biggs et al. 2011) hinder participation that may lead to greater problem-solving capacity and adaptability (Berkes, Colding, and Folke 2000; Innes and 145

Booher 1999; Folke et al. 2005) and create undesirable power dynamics (Kapoor 2001, 2004; Innes and Booher 2008).

Weber (2008) summarized the emergence of a “new environmental governance” paradigm in which approaches that embrace participation and collaboration are causing researchers and practitioners to challenge widely-held notions about the nature of solving socio-environmental problems. Stakeholders and institutions acting within an SES contain a wealth of knowledge pertaining to management and adaptation of that system to changing conditions at the various scales (Cash et al. 2006; Berkes, Colding, and Folke 2000; Hahn et al. 2006), a sentiment exemplified by the recent call for greater inclusion of mental models in environmental governance and management (Biggs et al. 2011; Folke 2006).

Craik (1943) coined the concept mental models as each individual’s unique internal representation of external reality that underlies comprehension and interaction with their environment. This concept has been used to study and better understand human reasoning, perception, and decision-making (Johnson-Laird 1983; Jones et al. 2011). In the context of socio-environmental problem-solving, mental model elicitation provides a basis for understanding stakeholder values, beliefs, and decision-making strategies and local expert and traditional knowledge about complex social-ecological system dynamics (Biggs et al. 2011; Manfredo et al. 2014; S. A. Gray et al. 2012; Doyle and Ford 1998). Thus, a great deal of literature has focused on inclusion of diverse mental models in

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decision making and management of complex SES’s in order to create a robust base of knowledge and problem-solving capacity (Olsson et al. 2006; Folke et al. 2007). Much of the focus of this research is identifying tools for eliciting and externalizing mental models and methods for structuring them for better utilization (Carley, 1997; Carley & Palmquist, 1992; Doyle & Ford, 1998; Jones et al., 2011)..

Fuzzy cognitive mapping (FCM) is a tool that is gaining popularity in participatory research and problem-solving for its ability to directly elicit mental models to study complex system structure and understandings of stakeholders (S. A. Gray, Zanre, and Gray 2014b; Özesmi and Özesmi 2004). In many participatory applications, fuzzy cognitive maps (FCM’s) are used to compare stakeholder perspectives, build collective knowledge about complex systems, and support collaboration in problem-solving (Metzger et al. 2018; A. J. Jetter and Kok 2014; Henly-Shepard, Gray, and Cox 2015). FCM has found a place in participatory modeling as a tool for engaging stakeholders and non-traditional experts in the processes of model-building and problem-solving (Voinov and Bousquet 2010). Researchers commonly approach mental model elicitation through interview or model-building session with groups or individuals, and utilize the resulting FCM’s to better understand complex systems and decision-making, describing variation among participants, create shared learning, or encourage collaboration and consensus building processes (Metzger et al. 2018; S. A. Gray, Zanre, and Gray 2014b).

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Jones et al. (2009), when describing the functions of participatory processes, differentiates among substantive, in which integrating diverse stakeholder knowledge will improve overall understanding and problem-solving capacity; normative, which is meant to improve legitimacy by creating social learning and achieving greater acceptance of outcomes; and instrumental, which focuses on improving collaboration and reducing conflict among those involved. A comprehensive review of participatory FCM case studies, found that substantive purposes, such as enhancing system knowledge and understanding variation among participant perspectives, were the most common (Metzger et al. 2018). Normative and instrumental purposes, including shared learning, consensusbuilding, and increased participation were less common, and were often difficult to achieve. Therefore, additional tools and concepts are needed to better connect the practice of participatory FCM to address these shortcomings. One tool that could potentially improve these normative and instrumental functions of participatory FCM is boundary objects.

Boundary objects are a discursive, meaning-making tool which is beginning to cross into SES (see: Brand and Jax 2007; Becker 2012) and participatory modeling literature (see: Huang et al. 2017) . A boundary object is defined as anything that serves as a focal point for dialogue and cooperation with or without consensus, flexibly determined by the needs and context of the process in which it is used (Star and Griesemer 1989). The concept of boundary objects has been used in scientific studies to describe “things that exist at the junctures where varied social worlds meet in an arena of mutual concern” (Clarke and

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Star 2008 p. 121). These objects support collaboration, conflict resolution, discussion, and understanding across disciplinary, social, cultural, and organizational boundaries (Black and Andersen 2012). They are meant to enrich discussion and decision-making by providing a common basis for the interpretive and reasoning processes of individuals’ social worlds, where social worlds are understood as shared discursive spaces in which meaning-making, including that around boundary objects, occurs (Clarke and Star 2008). In other words, boundary objects serve as a shared focal point for discussion of alternate understandings and perspectives.

Boundary objects can be nearly anything that serves these functions, including models, words and concepts, visual representations, organizations, specific concepts, physical places, and many more (Star 2010). A recent article by Huang et al. (2017) explores the very process of collaborative modeling, in which stakeholders share their knowledge and understandings through collectively constructing an FCM as a boundary object. Due to the flexibility of the concept, participatory FCM could help in identifying boundary objects that enhance shared learning on multiple levels, for example: the processes, the finished model, chains of causation, individual relationships, or individual concepts.

Better integration of mental models into environmental governance and problem solving is a pursuit that requires flexible tools such as participatory FCM, which can support a better understanding of stakeholder diversity and creation of social learning outcomes. To enhance participatory FCM’s contribution to in these areas, I suggest a boundary object-

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oriented approach. In this paper, I present a case study of flood management in Boston, Massachusetts to explore the question: How can participatory FCM's be used to create boundary objects for social learning?

Study Area

Background on Boston, MA

Boston, Massachusetts is a highly urbanized coastal city with a population of 673,184 people in 2016 (United States Census Bureau 2017), located at the terminus of three watersheds that provide drinking water to hundreds of thousands of people while supporting urban recreation for hikers, cyclists, fishermen, and paddlers and considerable habitat for native fish and wildlife. (Frashure, Bowen, and Chen 2012; Charles River Watershed Association 2015; Mystic River Watershed Association 2015; Neponset River Watershed Association 2015). The urbanized area of Boston, has periodically experienced severe

impacts from extreme weather and flooding. An extreme rain event in March of 2010 caused flooding to occur in many areas of New England, including Boston. The severe impacts of this storm included loss of life, $10.7 million dollars in damage to property in South Boston, and sewage treatment overflows causing contamination in nearby municipalities (National Oceanic and Atmospheric Administration 2010). Superstorm Sandy in 2012 had minimal impacts to metropolitan Boston since it hit land during a low tide, but it’s catastrophic effects in New York City served as a warning to city officials

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and the extreme weather management community in the area. (Powell, Hanfling, and Gostin 2012; E. Douglas et al. 2013). Resilience and adaptation to future conditions and future storms became topics of increased urgency among decision-makers at all levels.

The extensive infrastructure that manages inland and coastal flooding, including dams, the combined sewer and stormwater systems, and above and below-ground storage structures, are overseen by a range of governmental organizations at different scales (federal, state and municipal). Emergency response and alerts to the public prior to and during storm events are dependent upon communication, information sharing, and collaboration among various local, regional, state, and federal organizations. Recent interviews with many key flood

managers in Boston, MA on the topic of how to manage and adapt to extreme weather impacts (Metzger et al. 2015) revealed that many in the flood management community emphasized the importance of social processes, such as: a. alignment of perspectives and priorities across scales b. collaboration across projects and sharing of resources c. greater shared understanding of complex interactions between social and environmental processes

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Methods

Our research design attempted to create boundary objects in two different ways and assess their utility in social learning. One approach used participant FCM’s to identify individual concepts with high potential to stimulate discussion and sharing of critical information and perspectives among participants. The second approach utilized a collaborative modeling process as a boundary object.

Metzger et al.'s (2018) review of participatory FCM, presented in Chapter 2, was used as a starting point to design the appropriate methods. My goal was to use a base of knowledge about participant variation to identify boundary objects that would be useful for social learning. According to this review, participant variation is often studied through comparing FCM’s created by individuals that represent the diverse knowledge and understandings of the community being studied. Thus, I first conducted model-building with individual representing various flood management organizations that operate in Boston, MA (See Table 4.1). Participants included organizations involved in planning, outreach and communication, or emergency response. I also ensured that participants represented various jurisdictional scales, including local organizations that operate within the metropolitan boundaries, organizations that focus on each of the three watersheds that terminate in Boston Harbor, Massachusetts state organizations, and national agencies dealing with flooding and extreme weather issues in regions that include Boston, MA. I allowed participants to determine all concepts included in the model, starting with a

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single central concept, “flooding”, and guided the addition of concepts within five broad categories: 1. Causes of extreme flooding a. Ex: Rain rate, antecedent conditions, ocean storm swell 2. Immediate impacts of flooding a. Ex: infrastructure damage, displacement of residents, sewage contamination 3. Long-term effects of flooding a. Ex: local economy, public health and safety, water quality 4. Management actions taken to mitigate flooding and its impacts a. Ex: dam regulation, early warning system, flood modeling 5. Adaptation strategies to increase resilience a. Ex: better predictive models, resilient infrastructure, project financing

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Table 4.1. Participant organizations in the case study of flood management in Boston Name Army Corps of Engineers (Flood Risk Management Program) A Better City Boston Harbor Now Cambridge Department of Public Works Charles River Watershed Association Coastal Zone Management Department of Fish and Game (Division of Ecological Restoration) Metropolitan Area Planning Commission Massachusetts Emergency Management Authority Mystic River Watershed Association Neponset River Watershed Association National Weather Service (River Forecast Center) The Nature Conservancy

Jurisdiction

Abbreviation

Federal Local Local Local Regional State

ACE ABC BHN CAM DPW CRWA CZM

State Local State Regional Regional Federal Federal

DFG MAPC MEMA MyRWA NepRWA NWS TNC

I conducted a knowledge classification process (Further detailed in chapter 3) to describe the landscape of perspectives within the flood management community. I identified five main themes that could be used to classify all concepts mentioned in the individual FCM’s: 1. Governance – rule and policy-making for the purposes of influencing social, economic, or environmental systems 2. Environmental – natural components and environmental systems not directly controlled by institutions, such as ecological function, weather, and climate 3. Structural – physical elements of the system, such as built infrastructure and physical landform and landscape features 4. Social – processes or qualities related to institutions or populations, such as communication among organizations and health and safety 154

5. Economic – related to money, finance and monetary resources

Using the linguistic terms and supplementary notes from model-building sessions with participants, I applied a theme to each concept that composed the individual FCM’s. These categorized FCM’s were used to calculate the total centrality of each theme. Centrality is the sum of the absolute values of relationships leading into and out of a component, and generally determines its overall importance and influence that a component has on the system as a whole (Kosko 1986). By aggregating the centrality of components that fell within each theme, I calculated the emphasis that individual participants placed on each theme in their model. The thematic centrality values were represented as relative values: the sum of centrality in each particular theme divided by the combined centrality of all concepts. This metric provided a basis of comparison by which to understand variation in participant perspectives.

One criteria for choosing a concept with high potential as a boundary object was a high centrality, as this indicates importance and influence on the overall system. The boundary object literature provided additional concepts used for selection. Three criteria of an effective boundary objects described in the literature are (Star 2010; Star and Griesemer 1989): Interpretive flexibility - enables discourse across diverse social worlds and ways of understanding Informational need – facilitates the sharing of well-structured, beneficial information

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Tacking ability – represents both broad, transferrable concepts and locally specific, contextual information

To achieve interpretive flexibility in our selection of a boundary object, we searched for a concept connected broadly to concepts in all themes. This suggested its ability to bridge many different dimensions of flood management and provide a conceptual connection among diverse ways of thinking. To aid in applying this criterion, I merged all individual models into a single meta-model that represented all mental models combined, standardizing and reducing the number of concepts to 106. This meta-model could then be searched for a concept that connected concepts of different themes with high centrality.

To ensure that the boundary object selected would fulfill an informational need, I narrowed the options to concepts within the least-represented category, as this would ideally encourage discussion and sharing of information about an aspect of flood management that is least developed and for which knowledge gaps are most likely to exist. I identified a concept with tacking ability by searching the for a concept that referred not just to a specific physical structure or process, as with most concepts, but could also be interpreted more broadly as an overarching approach to flood management.

Once I identified a concept that met these criteria for an effective boundary object, I held a focus group session that included a collaborative model-building exercise, fulfilling the

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use of collaborative modeling as a boundary object. To determine the appropriate participants for the collaborative modeling focus group, I included organizations that emphasized diverse types of knowledge. I used thematic analysis to cluster participants based on the themes that were dominant in their models. Themes were considered ‘dominant perspectives’ if the percentage of total centrality exceeded 100%/n, n being the number of themes. Since there were five themes, dominant themes would be greater than 20% of the model’s total centrality. This analysis of dominant themes was used to select a group of participants that spanned multiple knowledge domains.

This focus group included four participants whose dominant themes covered all five knowledge domains. In this focus group, I presented findings from our thematic analysis of participant variation and engaged the group in two different boundary object-related exercises. One involved them discussing the concept that I selected as a potential boundary object based to assess its potential for social learning. The second was a group modeling exercise in which the modeling process served as a boundary object. Findings from this workshop were collected through observer notes, audio recording, and a short open-ended exit survey.

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Results and Discussion

Identifying Boundary Objects and Focus Group Participants

The most well-represented theme in the individual models was structural with 109 concepts and a centrality of 257.03 across all individual models. The Social theme had the next highest centrality (190.18), but the environmental theme accounted for more total concepts (85 vs. 61). Governance had the least concepts (20) and centrality (77.87) (Table 4.2). Analyzing meta-model, which was a merged model of all participant FCM’s, I found that the concept “Policy Agenda” had a much higher centrality than any other concept. It was connected to a wide array of concepts from each of the five themes and was connected directly or closely to the most central concepts in each theme (Figure 4.1).

Using the dominant themes in participant FCM’s (Table 4.3), I constructed a Venn diagram showing the overlap of perspectives by participants (Figure 4.2). The structural theme was dominant in all but one participant FCM, while governance was dominant in only two. All themes were emphasized by at least two organizations and there was a great deal of overlap in which all organizations either shared a theme or could be connected to that theme through overlap with another organization.

The dispersed and overlapping nature of dominant themes demonstrates that while there exists a robust diversity in mental models among flood managers, there are also many

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opportunities for bridging perspectives among stakeholders, which would be leveraged for learning and collaboration. For example: many of the immediate impacts of flooding tended to be structural aspects of the system such as “infrastructure damage”, “transportation disruption”, and “combined sewer overflow”. These structural impacts of flooding appear to be well understood throughout the community and could serve as a common basis of understanding that underlies collaborative projects.

Figure 4.2 also suggests that there are many organizations within the community that could serve as “bridging organizations”. Bridging organizations are particularly important in multi-stakeholder collaborations where there are potential gaps between social institutions in such ways as values, vision, and capacity (Brown 1991; Lawrence and Hardy 1999). They play an essential role in the collaborative process of adaptive management and resource governance, as they help to define a conceptual common ground and social link between diverse stakeholder understandings (Folke et al. 2005; Berkes 2009). Organizations with three dominant themes could potentially be ideal organizations to include in discussion about collaborative strategies. The Massachusetts Office of Coastal Zone Management, for example, could potentially serve as a bridge between organizations interested in economic and social aspects of the system, while The Nature Conservancy could serve as a bridge between stakeholders more concerned with Social and Environmental aspects. The Massachusetts Department of Fish and Game could serve as a bridging organization between environmental and governance sectors.

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Figure 4.1. Top 10 most central concepts, themes, and connections to “Policy Agenda” 160

Table 4.2. Sum of number of concepts and centrality of each theme among individual FCM’s

No. Concepts Total Centrality

Economic 35 109.84

Environmental Governance 85 20 166.73 77.87

Social 61 190.18

Structural 109 257.03

Table 4.3. Relative centrality of each theme in individual FCM’s

ACE ABC BHN CAM DPW CRWA CZM DFG MAPC MEMA MyRWA NepRWA NWS TNC

Governance Structural Economic Environmental 13% 32% 9% 6% 17% 26% 19% 7% 0% 32% 42% 15% 0% 36% 10% 38% 0% 55% 4% 35% 11% 32% 21% 16% 21% 27% 14% 32% 6% 23% 17% 10% 22% 18% 4% 9% 4% 48% 21% 16% 6% 34% 0% 46% 14% 33% 17% 17% 0% 35% 6% 39%

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Social 40% 31% 11% 17% 6% 20% 6% 44% 47% 11% 14% 19% 21%

Figure 4.2. Venn diagram showing dominant perspectives for each participant organization 162

Focus group workshop

Representatives of four organizations took part in the focus group; the Massachusetts Office of Coastal Zone Management, Massachusetts Emergency Management Agency, The Nature Conservancy, and Charles River Watershed Association. Together, they collectively represented all five themes, and thus could be expected to provide a diverse range of knowledge and perspectives. The concept chosen to serve as a boundary object, “Policy Agenda”, was discussed among participants. The discussion covered the following topics: •

What policies exist that have a positive or negative influence on flooding or impacts?

•

What knowledge, data, and resources are needed to support beneficial policies?

•

How can managers and stakeholders at different scales collaborate to support these policies?

The discussion began by highlighting the dual nature of the topic. One aspect of policy discussed was the process of collectively defining the policy agenda and crafting laws that advance the agenda. The other aspect discussed was the localized implementation of these policies and laws, which result in social and environmental impacts. Several examples of specific policies and adaptation projects were brought up and briefly discussed in the context of their role in the larger process of policy agendas related to adaptation. The other two questions, which covered resources needed to support policy

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and how to structure collaboration across scales, were then addressed as the group transitioned into an open-ended group modeling exercise in which participants were asked to build an FCM that represented their collective knowledge and perspectives on the topic.

The participants decided that the best approach to modeling the topic of policy would be to model a specific implementation of a policy that they felt had potential to increase the flood resilience of Boston and other coastal areas in the region. The topic selected was a collaboratively implemented funding mechanism known as the In Lieu Fee Program (ILFP). Under this program, development projects that affect federally-regulated wetlands and aquatic habitat in Massachusetts are given the option of paying a mitigation fee, which is added to a fund used to finance coastal and aquatic conservation projects. This program is collaborative in structure, as it involves government and NGO’s at different levels. The fee structure is determined by the Army Corps of Engineers (national level), collected and managed by the Massachusetts Department of Fish and Game (state level), and administered to projects with the help of organizations such as The Nature Conservancy (an international NGO) and appropriate local partners. The model produced by the group represented the basic structure of the program and improvements proposed by the group to make the project more impactful (Figure 4.3).

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Figure 4.3. Group model of the In-Lieu Fee Program 165

The model depicts the basic mechanics of this program, it’s funding sources and monitoring, and the range of intended environmental (i.e. fish habitat and water quality) and structural (i.e. floodplain protection and un-development) outcomes of projects funded through this program. Thus, it expands upon the knowledge contained in the individual models by depicting a specific policy in detail and how it connects to other aspects of the system. This discussion of the program structure and function led to identification of an important strategic adjustment that some participants were not aware of and could inspire broader collaboration among flood managers. According to participants at the state and national level, most of the projects conducted under the ILFP have been small-scale habitat restoration projects for shoreline buffering, most commonly establishment of eelgrass habitat. While these projects have provided the benefit of enhancing fish habitat, the participants elaborated that they do not often provide sufficient flood-reduction, storm buffering, or other benefits. The discussion turned to an alternative strategy that the group agreed may provide a much greater range of benefits: aggregation of funds to target larger projects, such as land protection in critical shoreline areas. This strategy would require a increased participation of other organizations to identify project criteria, locate and develop projects with many potential benefits and leverage additional financial resources and organizational capacity.

The model of this policy also depicted many unknown or poorly understood relationships and elicited discussion of those unknowns. Some examples of these are the magnitude of other funding sources and matching capital, the influence that 5-year monitoring of

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existing projects on how the fund is managed and the effects that a changing climate and resource use assessments will have on the fee structure of the program. This information is as useful as known relationships, as they indicate to the participants and researchers what concepts and relationships should be further explored, either through targeted research or inclusion of other stakeholders with this specialized knowledge.

Feedback from participants was very positive and offered insights into the function of this policy and elicited suggestions for improving the process. Notes from the focus group dialogue and exit surveys suggest that this discussion and model-building also revealed important generalizable characteristics of the flood management community and transferrable lessons for improvement. When asked about what they learned from the individual process of building individual FCM’s, the participants discussed the accuracy of this method in representing their knowledge and perspective. Further, the participants found these models to demonstrate the diverse but interconnected nature of the community, which was enlightening to some and verified the previous notions of others. Some brought up the importance of having a common understanding of flood management and working across scales and boundaries, and an increased desire to better understand and collaborate with other organizations.

Regarding the group modeling exercise, there were many learning outcomes related specifically to the policy that was modeled. Some of the outcomes were a better functional understanding of the ILFP due to the group modeling exercise, increased

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awareness of opportunities for collaboration among stakeholders and a greater knowledge of the benefits that this policy could provide. Additional points, such as the importance of including input from vulnerable populations in the decision-making process, added to the discussion about how to improve this policy.

One suggestion for what could have been improved in the process of participatory modeling was expansion of the analysis to include a larger set of participants. While the 13 perspectives represented by FCM’s seemed to cover the different thematic categories related to flood management, the inclusion of more stakeholder perspectives could have revealed additional opportunities for social learning and collaboration. Addition of participants focusing specifically on policy and rule-making was suggested by one participant. It was also mentioned that participants in the individual modeling process could have been more deliberately chosen to represent a greater diversity of jurisdictional scales and organizational roles in flood management.

Conclusion

The methods discussed in this paper of thematically analyzing FCM’s, identifying boundary concepts, and using group participatory modeling as a boundary object explores the use of these innovative tools for enhancing participatory processes and creating opportunities for social learning. This research contributes to important conversations taking place in multiple literatures and popular discussions. The participatory modeling

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literature related to FCM, for example, has a lack of case studies that successfully demonstrate highly structured, broadly transferrable methods for achieving social learning and consensus-building (Metzger et al. 2018). These processes play a large role in what the larger field of participatory modeling strives to accomplish (Voinov and Bousquet 2010; Jones et al. 2009), and are thus critical to the continued development of the field.

This research also adds practical case study to the literature on boundary objects in the management of social-ecological systems. Research into the intentional use of boundary objects in this context is still in its early stages, despite being a topic of interest in the field for some time. Some advocate using boundary objects in SES (Hinkel, Bots, and Schlüter 2014; Sternlieb et al. 2013; Cash et al. 2006) while others explore the use of SES and its conceptual components, as boundary objects (Brand and Jax 2015; Becker 2012; Steger et al. 2018). The use of participatory modeling in this and other research (see: Huang et al. 2017) demonstrates multiple forms that boundary objects can take in scientific research: for example, a participatory process, a whole system, or components of that system. Overall, my intent is that this research be used to demonstrate the great potential and flexibility of participatory modeling and boundary objects, and to encourage their continued development for collaborative management of social-ecological systems.

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Walker, Brian, C. S. Holling, Stephen R. Carpenter, and Ann Kinzig. 2004. “Resilience, Adaptability and Transformability in Social – Ecological Systems.” Ecology And Society 9 (2):5. https://doi.org/10.1103/PhysRevLett.95.258101. Weber, Edward P. 2008. “Note: Reality and Better Mousetraps: A Research Agenda for New Environmental Governance Institutions.” Society & Natural Resources 21 (2):91–93. Wyborn, Carina, and R. Patrick Bixler. 2013. “Collaboration and Nested Environmental Governance: Scale Dependency, Scale Framing, and Cross-Scale Interactions in Collaborative Conservation.” Journal of Environmental Management 123. Elsevier Ltd:58–67. https://doi.org/10.1016/j.jenvman.2013.03.014.

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CHAPTER 5 CONCLUSION

Mental models play an integral role in the learning and linking processes of adaptive comanagement (ACM). Thus, tools that increase the ability of practitioners and researchers to understand and utilize the mental models of local stakeholders are critical. Participatory approaches to fuzzy cognitive mapping (FCM) have proven to be an effective method for eliciting, comparing and utilizing mental models in social-ecological applications. This dissertation explored the potential of FCM to improve the use of mental models in ACM by addressing the question: How can participatory modeling support the use of mental models in adaptive co-management?

In Chapter 2, I summarized the literature on participatory FCM by reviewing case studies and creating a typology of research design to answer the question: What standards and norms are emerging in the design of participatory FCM research? This typology and the knowledge gain in this review contribute to the literature many important insights into the design of participatory FCM research and methodological tradeoffs. They also guided us 181

in designing two case studies in Boston, MA through which I explored the utility of participatory FCM approaches in an ACM process.

Chapter 3 employed a novel method of knowledge classification to participatory modeling with FCM to answer two questions: 1. What can participatory FCM reveal about variation in perspectives among experts at different jurisdictional scales? 2. How can a better understanding of mental model diversity inform social learning and collaboration efforts? An innovative research approach helped to illuminate the variation in dominant priorities and domains of knowledge through which perspectives of organizations at different jurisdictional scales could be compared. Positive feedback from participants in postanalysis surveys and informal interviews strengthened the legitimacy of these results and their implications for collaboration and shared learning.

Chapter 4 addressed the topic of social learning with participatory FCM through the identification of boundary objects, answering the question: How can participatory FCM's be used to create boundary objects for social learning? Using the aggregated model of all flood manager FCM’s and the novel knowledge categorization process from Chapter 3, I identified one concept (“Policy”) that met all criteria of an effective boundary object. I then categorized individual FCM’s to select a diverse group of participants for a group

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modeling exercise that was successful in stimulating shared learning and developing an artifact of shared knowledge.

This research has demonstrated multiple ways in which participatory modeling can serve as a tool to better integrate mental models into an ACM process. While this research focused on stakeholders that have a great deal of agency in managing environmental hazards, a valuable extension of this research could focus on applying the same methods to elicit mental models of other stakeholders, especially vulnerable populations that are severely impacted. In addition to expanding the opportunities for learning and linking and increasing the breadth of participation in defining the context and solution space, this expansion could be used to explore the use of participatory modeling for addressing problematic power dynamics.

For example Gondo (2009) found that while successful ACM case studies in southern Africa circumvented harmful competition between groups and the other issues typical of conventional top-down approaches, cases in which power dynamics were not effectively managed resulted in manipulation of the participatory process to advance narrow, preexisting agendas. While the sharing of management power is often mentioned in the foundational ACM literature (Hahn et al. 2006; Folke et al. 2005; Olsson, Folke, and Berkes 2004), the discussion of the role that power plays, and identification of tools and processes for avoid the harmful influence of power structures on the proceces, are not well established. The issue goes much deeper, as described by Denney et al. (2018) when

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even well-intentioned and carefully-designed processes of inclusion, collaboration and knowledge integration end up repeating undesirable patterns of power dynamics that have been long ingrained into western approaches to participation. The use of modeling and boundary objects as tools for participation and mediation of the framing and problemsolving process is an open area of research with a great deal of potential.

Overall, I hope that this work contributes substantially to the various literatures in which it is situated and to the flood management community in Boston, MA. Additionally, my hope is to inspire further development of tools and techniques that support innovative solutions to wicked problems in the present and future.

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References

Denney, J Michael, Paul Case, Alexander Metzger, Maria Ivanova, and Araya Asfaw. 2018. “Power in Participatory Processes : Reflections from Multi-Stakeholder Workshops in the Horn of Africa.” Sustainability Science 0 (0). Springer Japan:0. https://doi.org/10.1007/s11625-018-0533-x. Folke, Carl, Thomas Hahn, Per Olsson, and Jon Norberg. 2005. “Adaptive Governance of Social-Ecological Systems.” Annual Review of Environment and Resources 30 (1):441–73. https://doi.org/10.1146/annurev.energy.30.050504.144511. Gondo, T. 2009. “Adaptive Co-Management of Natural Resources: A Solution or Part of the Problem?” In 2009 Amsterdam Conference on the Human Dimensions of Global Environmental Change, 1–19. Amsterdam, The Netherlands. Hahn, Thomas, Per Olsson, Carl Folke, and Kristin Johansson. 2006. “Trust-Building, Knowledge Generation and Organizational Innovations: The Role of a Bridging Organization for Adaptive Comanagement of a Wetland Landscape around Kristianstad, Sweden.” Human Ecology 34 (4):573–92. https://doi.org/10.1007/s10745-006-9035-z. Olsson, Per, Carl Folke, and Fikret Berkes. 2004. “Adaptive Comanagement for Building Resilience in Social-Ecological Systems.” Environmental Management 34 (1):75– 90. https://doi.org/10.1007/s00267-003-0101-7.

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