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Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

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  • Nicholas R. Magliocca

    (Department of Geography, University of Alabama, 513 University Blvd., Box 870322, Tuscaloosa, AL 35401, USA)

Abstract

The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain.

Suggested Citation

  • Nicholas R. Magliocca, 2020. "Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus," Land, MDPI, vol. 9(12), pages 1-25, December.
  • Handle: RePEc:gam:jlands:v:9:y:2020:i:12:p:519-:d:462113
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    1. Guus ten Broeke & George van Voorn & Arend Ligtenberg, 2016. "Which Sensitivity Analysis Method Should I Use for My Agent-Based Model?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-5.
    2. Bianca E. Lopez & Nicholas R. Magliocca & Andrew T. Crooks, 2019. "Challenges and Opportunities of Social Media Data for Socio-Environmental Systems Research," Land, MDPI, vol. 8(7), pages 1-18, July.
    3. Onwezen, Marleen C. & Antonides, Gerrit & Bartels, Jos, 2013. "The Norm Activation Model: An exploration of the functions of anticipated pride and guilt in pro-environmental behaviour," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 141-153.
    4. Fernando Miralles-Wilhelm, 2016. "Development and application of integrative modeling tools in support of food-energy-water nexus planning—a research agenda," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 6(1), pages 3-10, March.
    5. Guo, Miao & van Dam, Koen H. & Touhami, Noura Ouazzani & Nguyen, Remy & Delval, Florent & Jamieson, Craig & Shah, Nilay, 2020. "Multi-level system modelling of the resource-food-bioenergy nexus in the global south," Energy, Elsevier, vol. 197(C).
    6. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    7. Weiwei Mo & Zhongming Lu & Bistra Dilkina & Kevin H. Gardner & Ju-Chin Huang & Maria Christina Foreman, 2018. "Sustainable and Resilient Design of Interdependent Water and Energy Systems: A Conceptual Modeling Framework for Tackling Complexities at the Infrastructure-Human-Resource Nexus," Sustainability, MDPI, vol. 10(6), pages 1-10, June.
    8. Fernandez-Mena, Hugo & Gaudou, Benoit & Pellerin, Sylvain & MacDonald, Graham K. & Nesme, Thomas, 2020. "Flows in Agro-food Networks (FAN): An agent-based model to simulate local agricultural material flows," Agricultural Systems, Elsevier, vol. 180(C).
    9. Moncada, J.A. & Lukszo, Z. & Junginger, M. & Faaij, A. & Weijnen, M., 2017. "A conceptual framework for the analysis of the effect of institutions on biofuel supply chains," Applied Energy, Elsevier, vol. 185(P1), pages 895-915.
    10. Kun Cheng & Qiang Fu & Tianxiao Li & Qiuxiang Jiang & Wei Liu, 2015. "Regional food security risk assessment under the coordinated development of water resources," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(1), pages 603-619, August.
    11. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    12. Huang, Shiyang & Hu, Guiping & Chennault, Carrie & Su, Liu & Brandes, Elke & Heaton, Emily & Schulte, Lisa & Wang, Lizhi & Tyndall, John, 2016. "Agent-based modeling of bioenergy crop adoption and farmer decision-making," Energy, Elsevier, vol. 115(P1), pages 1188-1201.
    13. Antoni Perello-Moragues & Pablo Noriega & Manel Poch, 2019. "Modelling Contingent Technology Adoption in Farming Irrigation Communities," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-1.
    14. Guillem, E.E. & Murray-Rust, D. & Robinson, D.T. & Barnes, A. & Rounsevell, M.D.A., 2015. "Modelling farmer decision-making to anticipate tradeoffs between provisioning ecosystem services and biodiversity," Agricultural Systems, Elsevier, vol. 137(C), pages 12-23.
    15. Bieber, Niclas & Ker, Jen Ho & Wang, Xiaonan & Triantafyllidis, Charalampos & van Dam, Koen H. & Koppelaar, Rembrandt H.E.M. & Shah, Nilay, 2018. "Sustainable planning of the energy-water-food nexus using decision making tools," Energy Policy, Elsevier, vol. 113(C), pages 584-607.
    16. Schlüter, Maja & Baeza, Andres & Dressler, Gunnar & Frank, Karin & Groeneveld, Jürgen & Jager, Wander & Janssen, Marco A. & McAllister, Ryan R.J. & Müller, Birgit & Orach, Kirill & Schwarz, Nina & Wij, 2017. "A framework for mapping and comparing behavioural theories in models of social-ecological systems," Ecological Economics, Elsevier, vol. 131(C), pages 21-35.
    17. Bruce Edmonds & Christophe Le Page & Mike Bithell & Edmund Chattoe-Brown & Volker Grimm & Ruth Meyer & Cristina Montañola-Sales & Paul Ormerod & Hilton Root & Flaminio Squazzoni, 2019. "Different Modelling Purposes," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(3), pages 1-6.
    18. Bazilian, Morgan & Rogner, Holger & Howells, Mark & Hermann, Sebastian & Arent, Douglas & Gielen, Dolf & Steduto, Pasquale & Mueller, Alexander & Komor, Paul & Tol, Richard S.J. & Yumkella, Kandeh K., 2011. "Considering the energy, water and food nexus: Towards an integrated modelling approach," Energy Policy, Elsevier, vol. 39(12), pages 7896-7906.
    19. Nicholas R Magliocca & Virginia McConnell & Margaret Walls, 2018. "Integrating Global Sensitivity Approaches to Deconstruct Spatial and Temporal Sensitivities of Complex Spatial Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-12.
    20. Annie Waldherr & Nanda Wijermans, 2013. "Communicating Social Simulation Models to Sceptical Minds," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(4), pages 1-13.
    21. Arika Ligmann-Zielinska & Peer-Olaf Siebers & Nicholas R Magliocca & Dawn C. Parker & Volker Grimm & Jing Du & Martin Cenek & Viktoriia Radchuk & Nazia N. Arbab & Sheng Li & Uta Berger & Rajiv Paudel , 2020. "‘One Size Does Not Fit All’: A Roadmap of Purpose-Driven Mixed-Method Pathways for Sensitivity Analysis of Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-6.
    22. Andrea Saltelli & Gabriele Bammer & Isabelle Bruno & Erica Charters & Monica Di Fiore & Emmanuel Didier & Wendy Nelson Espeland & John Kay & Samuele Lo Piano & Deborah Mayo & Roger Pielke Jr & Tommaso, 2020. "Five ways to ensure that models serve society: a manifesto," Nature, Nature, vol. 582(7813), pages 482-484, June.
    23. Alireza Nouri & Bahram Saghafian & Majid Delavar & Mohammad Reza Bazargan-Lari, 2019. "Agent-Based Modeling for Evaluation of Crop Pattern and Water Management Policies," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3707-3720, September.
    24. Abrahamse, Wokje & Steg, Linda, 2009. "How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings?," Journal of Economic Psychology, Elsevier, vol. 30(5), pages 711-720, October.
    25. Akanle, O.M. & Zhang, D.Z., 2008. "Agent-based model for optimising supply-chain configurations," International Journal of Production Economics, Elsevier, vol. 115(2), pages 444-460, October.
    26. Kesheng Shu & Uwe A. Schneider & Jürgen Scheffran, 2015. "Bioenergy and Food Supply: A Spatial-Agent Dynamic Model of Agricultural Land Use for Jiangsu Province in China," Energies, MDPI, vol. 8(11), pages 1-24, November.
    27. Vladimir Nikolic & Slobodan Simonovic & Dragan Milicevic, 2013. "Analytical Support for Integrated Water Resources Management: A New Method for Addressing Spatial and Temporal Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 401-417, January.
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