Agents of change—together: Using agent-based models to inspire social capital building for resilient communities

References

• Adger, W.N. (2003). Social capital, collective action, and adaptation to climate change. Economic Geography, 79(4), 387–404. doi:10.1111/j.1944-8287.2003.tb00220.x
   Web of Science ®Google Scholar
• Alcivar, J., Blumstein, A.L., Dostart, K.E., Drahos, J.W., Levinson, L.R., Whitson, J., … Rongerude, J.M. (2014). Capitol East neighborhood charter plan update. Retrieved from http://lib.dr.iastate.edu/resilientneighborhoods_plans/1
   Google Scholar
• Aldrich, D.P., & Meyer, M.A. (2015). Social capital and community resilience. American Behavioral Scientist, 59(2), 254–269. doi:10.1177/0002764214550299
   Web of Science ®Google Scholar
• An, L. (2012). Modelling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 229, 25–36. doi:10.1016/j.ecolmodel.2011.07.010
   Web of Science ®Google Scholar
• Ashwill, M., Flora, C., & Flora, J. (2011). Building community resilience to climate change: Testing the adaptation coalition framework in Latin America. Washington, DC: International Bank for Reconstruction and Development/The World Bank.
   Google Scholar
• Axelrod, R. (2006). Agent-based modeling as a bridge between disciplines. In L. Tesfatsion & K.L. Judd (Eds.), Handbook of computational economics (Vol. 2, pp. 1565–1584). North Holland: Elsevier.
   Google Scholar
• Axelrod, R., & Hamilton, W.D. (1981). The evolution of cooperation. Science, 211(4489), 1390–1396. doi:10.1126/science.7466396
   PubMed Web of Science ®Google Scholar
• Axelrod, R.M. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton: Princeton University Press.
   Google Scholar
• Barabasi, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
   PubMed Web of Science ®Google Scholar
• Basco-Carrera, L., Warren, A., van Beek, E., Jonoski, A., & Giardino, A. (2017). Collaborative modelling or participatory modelling? A framework for water resources management. Environmental Modelling and Software, 91, 95–110. doi:10.1016/j.envsoft.2017.01.014
   Web of Science ®Google Scholar
• BenDor, T., Scheffran, J., & Hannon, B. (2009). Ecological and economic sustainability in fishery management: A multi-agent model for understanding competition and cooperation. Ecological Economics, 68(4), 1061–1073. doi:10.1016/j.ecolecon.2008.07.014
   Web of Science ®Google Scholar
• Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280–7287. doi:10.1073/pnas.082080899
   PubMed Web of Science ®Google Scholar
• Borri, D., Camarda, D., & Pluchinotta, I. (2013). Planning urban microclimate through multiagent modelling: A cognitive mapping approach. In Y. Luo (Ed.), Cooperative design, visualization, and engineering (pp. 169–176). Berlin/Heidelberg, Germany: Springer. doi:10.1007/978-3-642-40840-3_25
   Google Scholar
• Bruen, A.M., Goodwin, M., Liu, S., Christianson, E., Ryan, M.L., Tong, X., … Rongerude, J.M. (2014). Capitol Park neighborhood charter plan update. Retrieved from http://lib.dr.iastate.edu/resilientneighborhoods_plans/2
   Google Scholar
• Cai, J., & Xiong, H. (2017). An agent-based simulation of cooperation in the use of irrigation systems. Complex Adaptive Systems Modeling, 5(1), 9. doi:10.1186/s40294-017-0047-x
   Google Scholar
• Chazdon, S., Emery, M., Hansen, D., Higgins, L., & Sero, R. (2017). A field guide to Ripple Effects Mapping. Minneapolis, MN: University of Minnesota Libraries Publishing.
   Google Scholar
• Emery, M., & Flora, C. (2006). Spiralling up: Mapping community transformation with Community Capitals Framework. Community Development, 37(1), 19–35. doi:10.1080/15575330609490152
   Google Scholar
• Frayret, J.M., D’Amours, S., Rousseau, A., Harvey, S., & Gaudreault, J. (2007). Agent-based supply-chain planning in the forest products industry. International Journal of Flexible Manufacturing Systems, 19(4), 358–391. doi:10.1007/s10696-008-9034-z
   Google Scholar
• Freitag, R.C., Abramson, D.B., Chalana, M., & Dixon, M. (2014). Whole community resilience: An asset-based approach to enhancing adaptive capacity before a disruption. Journal of the American Planning Association, 80(4), 324–335. doi:10.1080/01944363.2014.990480
   Web of Science ®Google Scholar
• Fuller, M.C. (2010). Driving demand for home energy improvements: Motivating residential customers to invest in comprehensive upgrades that eliminate energy waste, avoid high utility bills, and spur the economy (Technical report). Berkeley, CA: Ernest Orlando Lawrence Berkeley National Laboratory.
   Google Scholar
• Gutierrez-Montes, I., Emery, M., & Fernandez-Baca, E. (2009). The Sustainable Livelihoods Approach and the community capitals framework: The importance of system-level approaches to community change efforts. Community Development, 40(2), 106–113. doi:10.1080/15575330903011785
   Google Scholar
• Hailegiorgis, A.B., Atesmachew, B., Kennedy, W.G., Rouleau, M., Bassett, J.K., Coletti, M., … Gulden, T. (2010). An agent based model of climate change and conflict among pastoralists in East Africa. Proceedings of the iEMSs Third Biennial Meeting: Modelling for Environment’s Sake. Ottawa, Canada: International Environmental Modelling and Software Society.
   Google Scholar
• Harris, T.M. (2016). From PGIS to participatory deep mapping and spatial storytelling: An evolving trajectory in community knowledge representation in GIS. The Cartographic Journal, 53(4), 318–325. doi:10.1080/00087041.2016.1243864
   Web of Science ®Google Scholar
• Hashemi, F., Marmur, B., Thompson, J., & Passe, U. (2018). Developing a workflow to integrate tree inventory data into urban energy models. Proceedings of the 2018 Simulation in Architecture and Urban Design Conference, SimAUD 2018, June 05–07. The Netherlands: TU Delft, Society for Modeling & Simulation International.
   Google Scholar
• Holland, J.H. (1995). Hidden order: How adaptation builds complexity. New York, NY: Addison-Wesley.
   Google Scholar
• Huang, W., Krejci, C.C., Dorneich, M.C., Passe, U., Shenk, L., & Stonewall, J. (Forthcoming). Analyzing residential weatherization decisions using hybrid simulation modeling. Building simulation.
   Google Scholar
• Jagani, C., & Passe, U. (2017). Simulation-based sensitivity analysis of future climate scenario impact on residential weatherization initiatives in the US Midwest. In M. Turrin, B. Peters, W. O’Brien, R. Stouffs, & T. Dogan (Eds.) Proceedings of the 2017 Simulation in Architecture and Urban Design (SimAUD 2017), Conference in Toronto, CA.
   Google Scholar
• Janssen, M.A., & Rollins, N.D. (2012). Evolution of cooperation in asymmetric commons dilemmas. Journal of Economic Behavior & Organization, 81(1), 220–229. doi:10.2139/ssrn.1368783
   Web of Science ®Google Scholar
• Kais, S.M., & Islam, M.S. (2016). Community capitals as community resilience to climate change: Conceptual connections. International Journal of Environmental Research and Public Health, 13(1211). doi:10.3390/ijerph13121211
   PubMedGoogle Scholar
• Krejci, C., & Beamon, B. (2015). Impacts of farmer coordination decisions on food supply chain structure. Journal of Artificial Societies and Social Simulation, 18(2), 19. doi:10.18564/jasss.2727
   Web of Science ®Google Scholar
• Magis, K. (2010). Community resilience: An indicator of social sustainability. Society and Natural Resources, 23(5), 401–416. doi:10.1080/08941920903305674
   Web of Science ®Google Scholar
• Martin Luther King Jr. Park Neighborhood Association. et al. (2014). Martin Luther King Jr. Park neighborhood plan. Retrieved from https://www.dmgov.org/Departments/CommunityDevelopment/PDF/MLK%20Jr%20Park%20Plan.pdf
   Google Scholar
• Miller, J.H., & Page, S.E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton: Princeton University Press.
   Google Scholar
• Mittal, A., & Krejci, C.C. (2017). A hybrid simulation modeling framework for regional food hubs. Journal of Simulation, 1–16. doi:10.1109/WSC.2015.7408276
   Web of Science ®Google Scholar
• Passe, U., Anderson, N., De Brabanter, K., Dorneich, M.C., Krejci, C., Poplin, A., & Shenk, L. (2016). Methodologies to study human-microclimate interaction for resilient, smart city decision-making. Passive Low Energy Architecture Conference, San Francisco, CA.
   Google Scholar
• Pathak, S.D., Day, J.M., Nair, A., Sawaya, W.J., & Kristal, M.M. (2007). Complexity and adaptivity in supply networks: Building supply network theory using a complex adaptive systems perspective. Decision Sciences, 38(4), 547–580. doi:10.1111/j.1540-5915.2007.00170.x
   Web of Science ®Google Scholar
• Polhill, J.G., Filatova, T., Schlüter, M., & Voinov, A. (2016). Modelling systemic change in coupled socio-environmental systems. Environmental Modelling and Software, 75, 318–332. doi:10.1016/j.envsoft.2015.10.017
   Web of Science ®Google Scholar
• Poplin, A., Shenk, L., & Passe, U. (2017). Engaging youth with pervasive technologies for resilient communities: Using the storytelling capabilities of maps and online GIS for coalition-building and action. Interaction Design and Architecture(S) Journal, 35, 182–204.
   Google Scholar
• Ross, H., & Berkes, F. (2014). Research approaches for understanding, enhancing, and monitoring community resilience. Society and Natural Resources, 27, 787–804. doi:10.1080/08941920.2014.905668
   Web of Science ®Google Scholar
• Smith, J.W., Anderson, D.H., & Moore, R.L. (2012). Social capital, place meanings, and perceived resilience to climate change. Rural Sociology, 77(3), 380–407. doi:10.1111/j.1549-0831.2012.00082.x
   Web of Science ®Google Scholar
• Southwell, B.G., & Murphy, J. (2014). Weatherization behavior and social context: The influences of factual knowledge and social interaction. Energy Research and Social Science, 2, 59–65. doi:10.1016/j.erss.2014.03.019
   Google Scholar
• Stonewall, J., Huang, W., Dorneich, M., Krejci, C., Shenk, L., & Passe, U. (2018). Energy use and weatherization practices: Applications for agent-based modeling to support vulnerable populations. Human Factors: the Journal of Human Factor and Ergonomics Society, 62(1), 676–680. doi:10.1177/1541931218621153
   Google Scholar
• Taylor, R., Forrester, J., Pedoth, L., & Matin, N. (2014). Methods for integrative research on community resilience to multiple hazards, with examples from Italy and England. Procedia Economics and Finance, 18, 255–262. doi:10.1016/S2212-5671(14)00938-1
   Google Scholar
• Tesfatsion, L., Rehmann, C.R., Cardoso, D.S., Jie, Y., & Gutowski, W.J. (2017). An agent- based platform for the study of watersheds as coupled natural and human systems. Environmental Modelling and Software, 89, 40–60. doi:10.1016/j.envsoft.2016.11.021
   Web of Science ®Google Scholar
• US Department of Energy. (2016). What is weatherization? Retrieved from http://energy.gov/eere/wipo/what-weatherization/
   Google Scholar
• Viva East Bank! (2018). A neighborhood revitalization initiative. Retrieved from http://www.vivaeastbank.org/
   Google Scholar
• Wang, J., Brown, D.G., Riolo, R.L., Page, S.E., & Agrawal, A. (2013). Exploratory analyses of local institutions for climate change adaptation in the Mongolian grasslands: An agent-based modeling approach. Global Environmental Change, 23(5), 1266–1276. doi:10.1016/j.gloenvcha.2013.07.017
   Web of Science ®Google Scholar
• Wilensky, U. (1999). NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved from https://ccl.northwestern.edu/netlogo/
   Google Scholar
• Wolf, J., Adger, W.N., Lorenzoni, I., Abrahamson, V., & Raine, R. (2010). Social capital, individual responses to heat waves and climate change adaptation: An empirical study of two UK cities. Global Environmental Change, 20, 44–52. doi:10.1016/j.gloenvcha.2009.09.004
   Web of Science ®Google Scholar
• Wooldridge, M., & Jennings, N.R. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2), 115–152. doi:10.1017/S0269888900008122
   Web of Science ®Google Scholar
• Zellner, M.L., Lyons, L.B., Hoch, C.J., Weizeorick, J., Kunda, C., & Milz, D.C. (2012). Modeling, learning, and planning together: An application of participatory agent-based modeling to environmental planning. URISA Journal, 24(1), 77–93.
   Google Scholar