One of the factors for the success of simulation studies is close collaboration with stakeholders in developing a conceptual model. Conceptual models are a useful tool for communicating and understanding how real systems work. However, models or frameworks that are not aligned with the perceptions and understanding of local stakeholders can induce uncertainties in the model outcomes. We focus on two sources of epistemic uncertainty in building conceptual models of food-energy-water systems (FEWS): (1) context and framing; and (2) model structure uncertainty. To address these uncertainties, we co-produced a FEWS conceptual model with key stakeholders using the Actor-Resources-Dynamics-Interaction (ARDI) method. The method was adopted to specifically integrate public (and local) knowledge of stakeholders in the Magic Valley region of Southern Idaho into a FEWS model. We first used the ARDI method with scientists and modellers (from various disciplines) conducting research in the system, and then repeated the process with local stakeholders. We compared results from the two cohorts and refined the conceptual model to align with local stakeholders’ understanding of the FEWS. This co-development of a conceptual model with local stakeholders ensured the incorporation of different perspectives and types of knowledge of key actors within the socio-ecological systems models.
An, L. (2012). Modeling human decisions in coupled human and natural systems: reviews of agent-based model. Ecological Modelling 229:25-36
Argent, R.M., Sojda, R.S., Giupponi, C., McIntosh, B., Voinov, A.A., & Maier, H.R. (2016). Best practices for conceptual modelling in environmental planning and management. Environmental Modelling and Software, 80, 113-121. https://doi.org/10.1016/j.envsoft.2016.02.023.
Ascough II, J., Maier, H., Ravalico, J., & Strudley, M. (2008) Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecological modelling 219, 383-399. https://doi.org/10.1016/j.ecolmodel.2008.07.015.
Balbi, S., Perez, P., & Giupponi, C. (2010). A spatial agent-based model to explore scenarios of adaptation to climate change in an alpine tourism destination. University Ca'Foscari of Venice, Dept. of Economics Research Paper Series.
Barreteau, O., Abrami, G., Daré, W.S., Du Toit, D., Ferrand, N., Garin, P., Souchère, V., Popova, A., Werey, C. (2012). Collaborative modelling as a boundary institution to handle institutional complexities in water management, Restoring lands-coordinating science, politics and action. Springer, pp. 109-127.
Basco-Carrera, L., Warren, A., van Beek, E., Janoski, A., & Giardino, A. (2017). Collaborative modelling or participatory modelling? A framework for water resources management. Environmental Modelling and Software, 91, 95-110. https://doi.org/10.1016/j.envsoft.2017.01.014.
Bazilian, M., Rogner, H., Howells, M., Hermann, S., Arent, D., Gielen, D., Steduto, P., Mueller, A., Komor, P., & Tol, R.S. (2011). Considering the energy, water and food nexus: Towards an integrated modelling approach. Energy Policy, 39(12), 7896-7906. https://doi.org/10.1016/j.enpol.2011.09.039.
Berkes, F., & Folke, C. (1998). Linking social and ecological systems: management practices and social mechanisms for building resilience. Cambridge University Press, Cambridge, UK.
Beven, K.J., Aspinall, W.P., Bates, P.D., Borgomeo, E., Goda, K., Hall, J.W., Page, T., Phillips, J.C., Smith, P.J., Wagener, T., & Watson, M. (2017). Epistemic uncertainties and natural hazard risk assessment-part 2: what should constitute good practice? Natural Hazards and Earth System Sciences, 18(10), 2769-2783. https://doi.org/10.5194/nhess-2017-251.
Brugnach, M., Tagg, A., Keil, F., & de Lange, W.J. (2007) Uncertainty matters: computer models at the science–policy interface. Water Resources Management 21, 1075-1090. https://doi.org/10.1007/s11269-006-9099-y.
Brugnach, M., Dewulf, A., Pahl-Wostl, C., & Taillieu, T. (2008). Toward a relational concept of uncertainty: about knowing too little, knowing too differently, and accepting not to know. Ecology and Society 13(2): 30. http://www.ecologyandsociety.org/vol13/iss2/art30/
Clancey, W.J. The knowledge level reinterpreted: modeling how systems interact. Machine Learning 4:285-291.
Coats, E.R., & Wilson, P.I. (2017). Toward nucleating the concept of the water resource recovery facility (WRRF): perspective from the principal actors. Environmental Science and Technology 51, 4158-4164. doi: 10.1021/acs.est.7b00363.
d'Aquino, P., & Bah, A. (2013). A participatory modeling process to capture indigenous ways of adaptability to uncertainty: outputs from an experiment in West African drylands. Ecology and Society, 18(4): 16. http://dx.doi.org/10.5751/ES-05876-180416.
Daher, B.T., & Mohtar, R.H. (2015). Water–energy–food (WEF) Nexus Tool 2.0: guiding integrative resource planning and decision-making. Water International 40(5-6), 748-771. https://doi.org/10.1080/02508060.2015.1074148.
Dumrongrojwatthana, P., Le Page, C., Gajaseni, N., & Trébuil, G. (2011). Co-constructing an agent-based model to mediate land use conflict between herders and foresters in northern Thailand. Journal of Land Use Science 6(2-3), 101-120. https://doi.org/10.1080/1747423X.2011.558596.
Elsawah, S., Guillaume, J.H., Filatova, T., Rook, J., & Jakeman, A.J. (2015) A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: From cognitive maps to agent-based models. Journal of environmental management 151, 500-516. https://doi.org/10.1016/j.jenvman.2014.11.028.
Endo, A., Tsurita, I., Burnett, K., & Orencio, P.M. (2017). A review of the current state of research on the water, energy, and food nexus. Journal of Hydrology: Regional Studies, 11(Supplement C), 20-30. https://doi.org/10.1016/j.ejrh.2015.11.010.
Étienne, M. (2013). Companion modelling: a participatory approach to support sustainable development. Springer Science & Business Media.
Étienne, M., Du Toit, D.R., & Pollard, S. (2011). ARDI: A Co-construction Method for Participatory Modeling in Natural Resources Management. Ecology & Society 16(1), 1-14.
Filatova, T., Verburb., P.H., Parker, D.C., & Stannard, C.A. (2013). Spatial agent-based modles for socio-ecological systems: challenges and prospects. Environmental Modelling and Software 45, 1-7.
Garcia, D.J., & You, F. (2016). The water-energy-food nexus and process systems engineering: a new focus. Computers & Chemical Engineering, 91, 49-67. https://doi.org/10.1016/j.compchemeng.2016.03.003.
Gibon, A., Sheeren, D., Monteil, C., Ladet, S., & Balent, G. (2010). Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework. Landscape Ecology 25, 267-285.
Gourmelon, F., Chlous-Ducharme, F., Kerbiriou, C., Rouan, M., & Bioret, F. (2013). Role-playing game developed from a modelling process: A relevant participatory tool for sustainable development? A co-construction experiment in an insular biosphere reserve. Land use policy 32, 96-107. https://doi.org/10.1016/j.landusepol.2012.10.015.
Gupta, H.V., Clark, M.P., Vrugt, J.A., Abramowitz, G., & Ye, M. (2012). Towards a comprehensive assessment of model structural adequacy. Water Resources Research, 48(8), W08301. https://doi.org/10.1029/2011WR011044.
Hamilton, S.H., ElSawah, S., Guillaume, J.H., Jakeman, A.J., & Pierce, S.A. (2015) Integrated assessment and modelling: overview and synthesis of salient dimensions. Environmental Modelling & Software 64, 215-229. https://doi.org/10.1016/j.envsoft.2014.12.005.
Klauer, B., & Brown, J. (2004) Conceptualising imperfect knowledge in public decision-making: ignorance, uncertainty, error and risk situations. Environmental Research, Engineering and Management 1(27), 124-128.
Lynam, T., Mathevet, R., Etienne, M., Stone-Jovicich, S., Leitch, A., Jones, N., Ross, H., Du Toit, D., Pollard, S., Biggs, H., & Perez, P. (2012) Waypoints on a Journey of Discovery: Mental Models in Human-Environment Interactions. Ecology and Society 17 (3). http://doi.org/10.5751/es-05118-170323
Morgan M.G., & Henrion M. (1990). Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, Cambridge, 344 pp.
Naivinit, W., Le Page, C, Trebuil, G., & Gajaseni, N. (2010) Participatory agent-based modelling and simulation of rice production and labor migrations in Northeast Thailand. Environmental Modelling and Software 25, 1345-1358.
NASS (2017). National Agricultural Statistics Service, https://www.agcensus.usda.gov.
Pace D.K. (2003) Thoughts about the simulation conceptual model. In Proceedings of the 2003 Spring Simulation Interoperability Workshop.
Pahl-Wostl, C., M. Craps, A. Dewulf, E. Mostert, D. Tabara, & Taillieu, T. (2007). Social learning and water resources management. Ecology and Society, 12(2): 5. http://www.ecologyandsociety.org/vol12/iss2/art5/.
Pedde, S., Kok, K., Onigkeit, J., Brown, C., Holman, I., & Harrison, P.A. (2018). Bridging uncertainty concepts across narratives and simulations in environmental scenarios. Regional Environmental Change 19, 655-666. https://doi.org/10.1007/s10113-018-1338-2.
Refsgaard, J.C., Henriksen, H.J., Harrar, W.G., Scholten, H., & Kassahun, A. (2005). Quality assurance in model based water management–review of existing practice and outline of new approaches. Environmental Modelling and Software, 20(10), 1201-1215. https://doi.org/10.1016/j.envsoft.2004.07.006.
Refsgaard, J.C., van der Sluijs, J.P., Højberg, A.L., & Vanrolleghem, P.A. (2007). Uncertainty in the environmental modelling process–a framework and guidance. Environmetal Modelling and Software, 22(11), 1543-1556. https://doi.org/10.1016/j.envsoft.2007.02.004.
Robinson, S. (2008). Conceptual modelling for simulation part I: definition and requirements. Journal of the Operational Research Society, 59, 278-290.
Rouan, M., Kerbiriou, C., Levrel, H., & Etienne, M. (2010). A co-modelling process of social and natural dynamics on the isle of Ouessant: Sheep, turf and bikes. Environmental Modelling & Software 25, 1399-1412.
Scholten, L., Scheidegger, A., Reichert, P., & Maurer, M. (2013). Combining expert knowledge and local data for improved service life modeling of water supply networks. Environmetal Modelling and Software, 42, 1-16. http://dx.doi.org/10.1016/j.envsoft.2012.11.013.
Smajgl, A., Brown, D.G., Valbuena, D., & Huigen, M.G.A. (2011). Empirical characterization of agent behaviours in socio-ecological systems. Environmetal Modelling and Software, 26, 837-844. https://doi.org/10.1016/j.envsoft.2011.02.011.
Solís, P.B., Belmin, C., Leclerc, G., Antona, M., Morataya, R., & Bommel, P. (2016). Challenges for involving water stakeholders in educational and decision-making participatory processes supported by ABM. International Congress on Environmental Modelling and Software (iEMS), Toulouse, France, July 2016.
Tako, A.A., Kotiadis, K., & Vasilakis, C. (2010). A conceptual modelling framework for stakeholder participation in simulation studies. Processings of the 2010 Operational Research Society Simulation Conference (SW10), pp. 76-85.
United States Census Bureau, (2017). QuickFacts: Twin Falls City, Idaho. Available at https://www.census.gov/quickfacts/twinfallscityidaho. Accessed November 6, 2018.
Villamor, G.B., & van Noordwijk, M. (2016) Gender specific land-use decisions and implications for ecosystem services in semi-matrilineal Sumatra. Global Environmental Change 39, 69-80. https://doi.org/10.1016/j.gloenvcha.2016.04.007.
Villamor, G., Palomo, I., Santiago, C., Oteros-Rozas, E., & Hill, J. (2014). Assessing stakeholders' perceptions and values towards social-ecological systems using participatory methods. Ecological Processes, 3(1), 22. https://doi.org/ 10.1186/s13717-014-0022-9.
Villamor, G.B., Le, Q.B., Djanibekov, U., van Noordwijk, M., & Vlek, P.L.G. (2014). Biodiversity in rubber agroforests, carbon emissions, and rural livelihoods: An agent-based model of land-use dynamics in lowland Sumatra. Environmetal Modelling and Software, 61, 151-165. https://doi.org/10.1016/j.envsoft.2014.07.013.
Villamor, G.B., Van Noordwijk, M., Troitzsch, K.G., & Vlek, P.L., (2012) Human Decision Making In Empirical Agent-Based Models: Pitfalls And Caveats For Land-Use Change Policies, ECMS. Citeseer, pp. 631-637.
Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmetal Modelling and Software, 25, 1268-1281. https://doi.org/10.1016/j.envsoft.2010.03.007.
Voinov, A., Kolagani, N., McCall, M.K., Glynn, P.D., Kragt, M.E., Ostermann, F.O., Pierce, S.A., & Ramu, P. (2016). Modelling with stakeholders – next generation. Environmetal Modelling and Software, 77, 196-220. https://doi.org/10.1016/j.envsoft.2015.11.016.
U.S. Department of Agriculture (USDA), National Agricultural Statistics Service. (2012). Census of Agriculture. Available online: https://www.nass.usda.gov/AgCensus/
Walker, W.E., Harremoës, P., Rotmans, J., van der Sluijs, J.P., van Asselt, M.B., Janssen, P., & Krayer von Krauss, M.P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment 4(1), 5-17. https://doi.org/10.1076/iaij.126.96.36.19966.
Warmink, J.J., Janssen, J., Booij, M.J., & Krol, M.S. (2010) Identification and classification of uncertainties in the application of environmental models. Environmental Modelling & Software 25, 1518-1527. https://doi.org/10.1016/j.envsoft.2010.04.011
Zhang, X., & Vesselinov, V.V. (2017). Integrated modeling approach for optimal management of water, energy and food security nexus. Advances in Water Resources, 101, 1-10. https://doi.org/10.1016/j.advwatres.2016.12.017.