2. The Modeling Approach
2.1. Sustainability Modeling and Current Applications
- The process of model framing and construction, where decisions must be made about system boundaries, what to include in the model and how to couple model components;
- The formal articulation of assumptions and uncertainties about the system in question;
- The visualization of the underlying structure and connectivity of the system;
- The exposure, through visualization of model behavior, of system characteristics, such as unexpected outcomes and response thresholds.
2.2. Vision Modeling
- Ensure that the vision is composed of compatible goals, free of conflicts and trade-offs (i.e., coherence);
- Reinforce and serve as checks on the plausibility of the visioning goals, because model components must be based on realistic constraints;
- Make sure the visioning outcomes are tangible, largely through simulation runs of the model and viewing the targets graphically;
- Help to categorize how the specific system components are prioritized and nuanced, through both qualitative and quantitative parameterization of the model that determines which outcomes are sensitive to which assumptions and by how much.
3. Applying Modeling
3.1. A Tiered Approach to Modeling Sustainability Visions
- Conceptual and rapid prototype vision models: Conceptual models (as per Figure 1) are qualitative diagrammatic representations of the connections among the various model components. Conceptual models of future desirable states provide structural representations of vision components and system relationships. They are helpful to structure and organize components of the vision and to check for missing components and inconsistencies (i.e., conflicts and trade-offs) among components. Easiest to construct, conceptual models are the most commonly utilized approach for incorporating systems thinking into participatory settings. Techniques, such as influence matrices and trade-off assessments, explore the interrelations among vision components to identify potential conflicts, trade-offs and synergies . Causal loop conceptual diagrams are used to visualize systemic characteristics, which allows for better identification of potential intervention points associated with highly influential systemic features, such as feedback loops, downstream factors and network structure [23,24]; actor-oriented and sustainability constellation conceptual models incorporate specific actors, rules, norms, needs, wants, resources, technologies and actions in assessing beneficial and adverse effects [9,15,24,25]. Conceptual approaches are typically qualitative representations of a system structure, but relative quantifications may be used to make more nuanced inferences on conflicts, trade-offs and interventions .
- Dynamic vision models (functional, with input-output correspondence): Building on the conceptual modeling approach, dynamic models (as per Figure 1) are built and parameterized, such that the “running” models simulate the dynamics of complex interactions among the system components [23,24]. This increased articulation allows participants to better anticipate non-intuitive outcomes, such as “hidden” conflicts, due to thresholds or non-linearities, which emerge from the inter-relationship of system components.Parameters for dynamic vision models are selected from evidence-based and empirical work, allowing the vision components and interactions to be more relevant to the real world (i.e., grounded by reality). This enhances the coherence and plausibility of simulated outcomes. Techniques, such as sensitivity analysis and cross-impact analysis , inform the selection of indicators, targets and interventions based on how sensitive they are to change and the implications of emergent interactions. An important outcome of dynamic visions models is future projections of systems dynamics and normative trade-offs that emerge from the simulations (Arrow 3 in Figure 2). By simulating potential trajectories of visions, participants can explore the plausibility and viability of diverse envisioned future states. Undesirable or unrealistic trajectories may require a review of certain parameters and underlying assumptions or a return to the conceptualization of the vision (Arrow 3 in Figure 1). Dynamic vision models are constructed to further improve the specificity and coherence of the conceptual vision models, allowing participants to examine the viability and plausibility of the vision.
- Pathways of vision models: More cogent assessments of plausibility require the characterization of the sustainability gap between envisioned goals and initial conditions (see ; Figure 2). Pathways of vision models may be either qualitatively crafted from conceptual models or quantitatively simulated using dynamic vision models (Figure 1). These pathways are distinct from the potential future trajectories simulated by dynamic vision models (Arrows 2 and 4 versus Arrow 3 in Figure 2). Vision model pathways are simulated backwards (i.e., from the vision to the present conditions) through a heuristic process of identifying the components and conditions that need to be in place in order to achieve the vision (e.g., actions, policies, technologies, institutions). Using this procedure, it is likely that some of the pathways that are directed backward from the vision will not intersect with current state conditions, and the difference between the two is what we call the “reality gap” (Figure 2). The models thus become a critical tool in also identifying how disparate (and in what way) future ambitions may be from what is actually plausible (i.e., the “sustainability gap” in Figure 2).This approach is also distinct from, but potentially complementary to, backcast modeling, where pathways starting from the current state intersect with pre-determined envisioned future goals (Arrow 5 in Figure 2). Conducting both scenario pathway approaches (backcast modeling and vision modeling) may increase the number of potential interventions and options that are available. A comparative approach that contrasts vision pathways with those from other scenario approaches may also enhance the understanding of how the balance of deterministic and normative perspectives can shape scenario outcomes. One example of this is enhanced understanding of how starting from current state conditions affects the resulting visions. The emphasis of the pathways approach, however, is not merely to better understand methodological distinctions among scenario approaches, but also to enhance the process of visioning by rigorously describing and scrutinizing the visions using systems modeling. The purpose of the pathways approach is to increase the relevance of the vision by exploring and articulating what is needed to achieve a desirable, plausible and sustainable future.
3.2. Engaging Participants in Sustainability Vision Modeling
4. Real-World Examples of Modeling Sustainability Visions
|Project Name||Phoenix General Plan Visioning Study||ASU Sustainable Ecosystems undergraduate course|
|Project setting:||Urban planning research||Sustainability education|
|Project goal:||Develop rigorous visioning process and product||Teach sustainability education competencies|
|Goal criteria:||Sustainability visioning quality criteria ||Sustainability education competencies |
|Explicit role of vision modeling:||Addressing systemic criterion||Teaching systems thinking competency|
|Engagement setting:||Participatory modeling (fifteen two-day village workshops and one one-day city-level workshop)||Group modeling (in-class) 28–35 students per class Groups of 3–5 students|
|Vision modeling approach (scope: scale):|
|Outcomes:||Systems conflict and trade-off revisions to the vision; participants (self-assessment survey) and practitioners (debriefing) reported enhanced systems perspective||Pre- and post-assessments demonstrated enhanced capacity for systems thinking and anticipatory competency building|
4.1. Urban Planning Example: Phoenix General Plan
- Causal loop diagrams and network analysis were used to analyze the overall system structure and relationships among the vision elements;
- Consistency analysis was performed using influence matrices to identify trade-offs and synergies among vision elements;
- Diversity appraisal was used to identify similarities and differences among the vision models from different stakeholder groups (e.g., heterogeneity among the fifteen village visions).
- Familiarize themselves with their group’s subsystem by providing visualization and narratives of the vision elements, relationships and overall subsystem;
- Appraise the sustainability of the final negotiated vision by responding to open-ended questions based on sustainability principles (informal appraisal).
4.2. Education Example: Sustainable Ecosystems Course
5. Discussion and Synthesis
Conflicts of Interest
- Wiek, A.; Iwaniec, D.M. Quality criteria for visions and visioning in sustainability science. Available online: http://link.springer.com/article/10.1007%2Fs11625-013-0208-6#page-1 (accessed on 30 May 2014).
- Helling, A. Collaborative visioning: Proceed with caution!: Results from evaluating Atlanta’s vision 2020 project. J. Am. Plan. Assoc. 1998, 64, 335–349. [Google Scholar] [CrossRef]
- Shipley, R. Visioning in planning: Is the practice based on sound theory? Environ. Plan. A 2002, 34, 7–22. [Google Scholar] [CrossRef]
- Van der Helm, R. The vision phenomenon: Towards a theoretical underpinning of visions of the future and the process of envisioning. Futures 2009, 41, 96–104. [Google Scholar] [CrossRef]
- Brito, L. Analyzing sustainable development goals. Science 2012, 336, 1396–1396. [Google Scholar] [CrossRef]
- Childers, D.L.; Pickett, S.T.A.; Grove, J.M.; Ogden, L.; Whitmer, A. Advancing urban sustainability theory and action: Challenges and opportunities. Landsc. Urban. Plan. 2014, 125, 320–328. [Google Scholar] [CrossRef]
- Clark, W.C.; Dickson, N.M. Sustainability science: The emerging research program. Proc. Natl. Acad. Sci. USA 2003, 100, 8059–8061. [Google Scholar] [CrossRef]
- Sarewitz, D.; Clapp, R.; Crumbley, C.; Kriebel, D.; Tickner, J. The Sustainability Solutions Agenda. New Solutions. J. Environ. Occup. Health Policy 2012, 22, 139–151. [Google Scholar] [CrossRef]
- Wiek, A.; Farioli, F.; Fukushi, K.; Yarime, M. Sustainability science: Bridging the gap between science and society. Sustain. Sci. 2012, 7, 1–4. [Google Scholar]
- Wiek, A.; Ness, B.; Schweizer-Ries, P.; Brand, F.S.; Farioli, F. From complex systems analysis to transformational change: A comparative appraisal of sustainability science projects. Sustain. Sci. 2012, 7, 5–24. [Google Scholar]
- Cherp, A.; George, C.; Kirkpatrick, C. A methodology for assessing national sustainable development strategies. Environ. Plan. C 2004, 22, 913–926. [Google Scholar] [CrossRef]
- Gibson, R.B. Sustainability assessment: basic components of a practical approach. Impact Assess. Proj. Apprais. 2006, 24, 170–182. [Google Scholar] [CrossRef]
- Jordan, A. The governance of sustainable development: Taking stock and looking forwards. Environ. Plan. C 2008, 26, 17–33. [Google Scholar] [CrossRef]
- Astier, M.; García-Barrios, L.; Galván-Miyoshi, Y.; González-Esquivel, C.E.; Masera, O.R. Assessing the sustainability of small farmer natural resource management systems. A critical analysis of the MESMIS program (1995–2010). Ecol. Soc. 2012, 17, 1–25. [Google Scholar]
- Wiek, A.; Larson, K. Water, people, and sustainability—A systems framework for analyzing and assessing water governance regimes. Water Res. Manag. 2012, 26, 3153–3171. [Google Scholar] [CrossRef]
- Robinson, J.B. Future subjunctive: Backcasting as social learning. Futures 2003, 35, 839–856. [Google Scholar] [CrossRef]
- Quist, J.N. Backcasting for a sustainable future: The impact after 10 years. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands, 11 April 2007. [Google Scholar]
- Mason, D. Tailoring scenario planning to the company culture. Strategy Leadersh. 2003, 31, 25–28. [Google Scholar] [CrossRef]
- Höjer, M.; Mattsson, L. Historical determinism and backcasting in future studies. In Proceedings of the Conference on Urban Transport Stems, Lund, Sweden, 7–8 June 1999.
- Strong, R.; Ryan, J.; McDavid, D.; Leung, Y.; Zhou, R.; Strauss, E.; Bosma, J.; Sabbadini, T.; Jarvis, D.; Sachs, S.; et al. A New Way to Plan for the Future. In Proceedings of the 40th Annual Hawaii International Conference on System Sciences, Waikoloa, HI, USA, 3–6 January 2007.
- De Vries, J. Scenario Building: The Sociovision Approach, “maximizing depth”. Available online: http://www.infinitefutures.com/tools/sbdevries.shtml (accessed on 30 May 2014).
- Iwaniec, D.M.; Wiek, A.; Kay, B. SPARC—A Criteria-based Approach to Visioning in Transformational Sustainability Research. Futures 2014. submitted for publication. [Google Scholar]
- Vester, F. The Art of Interconnected Thinking: Tools and Concepts for a New Approach to Tackling Complexity; MCB Verlag GmbH: Munich, Germany, 2007. [Google Scholar]
- Meadows, D. Thinking in Systems: A Primer; Chelsea Green Publishing: White River Junction, VT, USA, 2008. [Google Scholar]
- Ostrom, E. Understanding Institutional Diversity; Princeton University Press: Princeton, NJ, USA, 2009. [Google Scholar]
- Vennix, J. Group Model Building: Facilitating Team Learning Using System Dynamics; John Wiley & Sons Ltd.: West Sussex, UK, 1996. [Google Scholar]
- Bishop, P.; Hines, A.; Collins, T. The current state of scenario development: An overview of techniques. Foresight 2007, 9, 5–25. [Google Scholar] [CrossRef]
- Gaziulusoy, A.İ.; Boyle, C.; McDowall, R. System innovation for sustainability: A systemic double-flow scenario method for companies. J. Clean. Prod. 2012, 45, 104–116. [Google Scholar] [CrossRef]
- Videira, N.; Antunes, P.; Santos, R.; Lopes, R. A participatory modelling approach to support integrated sustainability assessment processes. Sys. Res. Behav. Sci. 2010, 27, 446–460. [Google Scholar] [CrossRef]
- Vennix, J.A.M. Group model-building: Tackling messy problems. Sys. Dyn. Rev. 1999, 15, 379–401. [Google Scholar] [CrossRef]
- Hovmand, P.S.; Brennan, L.; Chalise, N. Whose Model is it Anyway? In Proceedings of the 29th International Conference of the System Dynamics Society, Washington, DC, USA, 25–29 July 2011.
- Shneiderman, B.; Fischer, G.; Czerwinski, M.; Resnick, M.; Myers, B.; Candy, L.; Edmonds, E.; Eisenberg, M.; Giaccardi, E.; Hewett, T.; et al. Creativity support tools: Report from a US National Science Foundation sponsored workshop. Int. J. Hum.-Comp. Interact. 2006, 20, 61–77. [Google Scholar] [CrossRef]
- Vidal, R.V.V. Creative and Participative Problem Solving: The Art and the Science; Informatics and Mathematical Modelling; Technical University of Denmark: Copenhagen, Denmark, 2006. [Google Scholar]
- Wiek, A.; Withycombe, L.; Redman, C.L. Key competencies in sustainability: A reference framework for academic program development. Sustain. Sci. 2011, 6, 203–218. [Google Scholar] [CrossRef]
- State of Arizona. Growing Smarter Act. Phoenix, AZ, USA, 1998. [Google Scholar]
- State of Arizona. Growing Smarter Plus. Phoenix, AZ, USA, 2000. [Google Scholar]
- Iwaniec, D.M.; Wiek, A. Advancing Sustainability Visioning Practice in Planning—The General Plan Revision in Phoenix, Arizona. Plan. Pract. Res. 2014. submitted for publication. [Google Scholar]
- Hmelo-Silver, C.; Pfeffer, M.G. Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors and functions. Cognit. Sci. 2004, 28, 127–138. [Google Scholar] [CrossRef]
- Dragoon. Available online: http://dragoon.asu.edu (accessed on 14 July 2014).
- VanLehn, K.; Childers, D.L.; van de Sande, B.; Iwaniec, D. Learning by Authoring Intelligent Tutoring Systems: A method for engaging students in modeling ill-defined systems. 2014; to be submitted for publication. [Google Scholar]
- Oreskes, N.; Shrader-Frechette, K.; Belitz, K. Verification, validation, and confirmation of numerical models in the earth sciences. Science 1994, 263, 641–646. [Google Scholar]
- VanLehn, K. Model construction as a learning activity: A design space and review. Interact. Learn. Environ. 2013, 21, 371–413. [Google Scholar] [CrossRef]
- Marshall, N.; Grady, B. Travel demand modeling for regional visioning and scenario analysis. Transp. Res. Rec. 2005, 1921, 44–52. [Google Scholar] [CrossRef]
- Lemp, J.D.; Zhou, B.B.; Kockelman, K.M.; Parmenter, B.M. Visioning versus modeling: Analyzing the land-use-transportation futures of urban regions. J. Urban. Plan. Dev. 2008, 134, 97–109. [Google Scholar] [CrossRef]
- Sheppard, S.; Shaw, A.; Flanders, D.; Burch, S.; Wiek, A.; Carmichael, J.; Robinson, J.; Cohen, S. Future visioning of local climate change: A framework for community engagement and planning with scenarios and visualisation. Futures 2011, 43, 400–412. [Google Scholar] [CrossRef]
- Bousquet, F.; Trébuil, G. Companion Modeling and Multi-Agent Systems for Integrated Natural Resource Management in Asia; International Rice Research Institute: Los Baños, Philippines, 2005. [Google Scholar]
- Chermack, T.J.; Lynham, S.A. A review of scenario planning literature. Futures Res. Q. 2001, 17, 7–32. [Google Scholar]
- Börjeson, L.; Höjer, M.; Dreborg, K.-H.; Ekvall, T.; Finnveden, G. Scenario types and techniques: Towards a user’s guide. Futures 2006, 38, 723–739. [Google Scholar] [CrossRef]
- Bradfield, R.; Wright, G.; Burt, G.; Cairns, G.; van der Heijden, K. The origins and evolution of scenario techniques in long range business planning. Futures 2005, 37, 795–812. [Google Scholar] [CrossRef]
- Schlüter, M.; Müller, B.; Frank, K. How to use models to improve analysis and governance of social-ecological systems—the reference frame MORE. Available online: http://ssrn.com/abstract=2037723 (accessed on 30 May 2014).
- Varum, C.; Melo, C. Directions in scenario planning literature—A review of the past decades. Futures 2010, 42, 355–369. [Google Scholar] [CrossRef]
- Shipley, R. Visioning in Strategic Planning: Theory, Practice and Evaluation. Ph.D. Thesis, University of Waterloo, Waterloo, ON, Canada, 1997. [Google Scholar]
- Shipley, R.; Michela, J. Can vision motivate planning action? Plan. Pract. Res. 2006, 21, 223–244. [Google Scholar] [CrossRef]
- Reed, M.S. Stakeholder participation for environmental management: A literature review. Biol. Conserv. 2008, 141, 2417–2431. [Google Scholar] [CrossRef]
- Voinov, A.; Bousquet, F. Modelling with stakeholder. Environ. Modell. Software 2010, 25, 1268–1281. [Google Scholar] [CrossRef]
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