Abstract
Responding to the challenges of societal transformation in the face of climate change, efforts to integrate behaviorally rich models of adaptation decision-making into large-scale macroeconomic and Earth system models are growing and agent-based models (ABMs) are an effective tool for doing so. However, behavioral richness in ABMs has been limited to implementations of single decision models for all agents in a simulated population. The main goals of this study were to: 1) implement the ‘building-block processes’ (BBPs) approach for decision model heterogeneity; 2) demonstrate the application of sensitivity and uncertainty analyses to quantify the scope of structural uncertainty produced by alternative decision models under variable price and climate conditions; and 3) apply the Observing System Simulation Experiment (OSSE) approach to validate such a behaviorally rich BBPs model at the level of individual agent decisions. Using an ABM of agricultural producers’ decision-making, we demonstrated that uncertainty in crop and farm management decisions introduced by heterogeneous decision models was equal to and in some instances greater than that due to variable price or precipitation conditions. Unrealistically rapid or stagnant behavioral dynamics were evident in model versions implementing single decision models for all agents. Moreover, interactions among agents with diverse decision models in the same population produced consistently more accurate outcomes and realistic behavioral dynamics. The BBPs framework and accompanying sensitivity and uncertainty analyses demonstrated here offer a path forward for increasing behavioral richness in ABMs, which is key to understanding processes of adaptation central to societal responses to climate change.
References
Abbaszadeh, P., Gavahi, K., Alipour, A., Deb, P., H. Moradkhani (2022). Bayesian Multi- modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction. Agricultural and Forest Meteorology, 314, 108773, doi:10.1016/j.agrformet.2021.108773.
An, L. (2012). Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 229, 25–36.
An, L., Grimm, V., Sullivan, A., TurnerII, B. L., Malleson, N., Heppenstall, A., Vincenot, C., Robinson, D., Ye, X., Liu, J., Lindkvist, E., & Tang, W. (2021). Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457, 109685. https://doi.org/10.1016/J.ECOLMODEL.2021.109685Arthur, W. B. (1999). Complexity and the Economy. Science, 284(5411), 107–109. https://doi.org/10.1126/science.284.5411.107
Arbuckle, J. G., Hobbs, J., Loy, A., Morton, L. W., Prokopy, L. S., & Tyndall, J. (2014). Understanding Corn Belt farmer perspectives on climate change to inform engagement strategies for adaptation and mitigation. Journal of Soil and Water Conservation, 69(6), 505–516. https://doi.org/10.2489/jswc.69.6.505
Arneth, A., Brown, C., & Rounsevell, M. D. A. (2014). Global models of human decision-making for land-based mitigation and adaptation assessment. Nature Climate Change, 4(7), 550–557. https://doi.org/10.1038/nclimate2250
Arthur, W. B. (2006). Chapter 32 Out-of-Equilibrium Economics and Agent-Based Modeling. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of Computational Economics (Vol. 2, pp. 1551–1564). Elsevier. https://doi.org/10.1016/S1574-0021(05)02032-0
Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M. (2002). Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences of the United States of America, 99 Suppl 3(suppl 3), 7275–7279. https://doi.org/10.1073/pnas.092080799
Balke, T., & Gilbert, N. (2014). How Do Agents Make Decisions? A Survey. Journal of Artificial Societies and Social Simulation, 17(4), 13.
Barrett, C. B., Benton, T. G., Cooper, K. A., Fanzo, J., Gandhi, R., Herrero, M., James, S., Kahn, M., Mason-D’Croz, D., Mathys, A., Nelson, R. J., Shen, J., Thornton, P., Bageant, E., Fan, S., Mude, A. G., Sibanda, L. M., & Wood, S. (2020). Bundling innovations to transform agri-food systems. Nature Sustainability 2020 3:12, 3(12), 974–976. https://doi.org/10.1038/s41893-020-00661-8
Baustert, P., & Benetto, E. (2017). Uncertainty analysis in agent-based modelling and consequential life cycle assessment coupled models: A critical review. Journal of Cleaner Production, 156, 378–394. https://doi.org/10.1016/J.JCLEPRO.2017.03.193
Beckage, B., Gross, L. J., Lacasse, K., Carr, E., Metcalf, S. S., Winter, J. M., Howe, P. D., Fefferman, N., Franck, T., Zia, A., Kinzig, A., & Hoffman, F. M. (2018). Linking models of human behaviour and climate alters projected climate change. Nature Climate Change, 8(1), Article 1. https://doi.org/10.1038/s41558-017-0031-7
Berglund, E. Z. (2015). Using Agent-Based Modeling for Water Resources Planning and Management. Journal of Water Resources Planning and Management, 141(11), 04015025. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000544
Blythe, J., Silver, J., Evans, L., Armitage, D., Bennett, N. J., Moore, M. L., Morrison, T. H., & Brown, K. (2018). The Dark Side of Transformation: Latent Risks in Contemporary Sustainability Discourse. Antipode, 50(5), 1206–1223. https://doi.org/10.1111/ANTI.12405
Brown, C., Seo, B., & Rounsevell, M. (2019). Societal breakdown as an emergent property of large-scale behavioural models of land use change. Earth System Dynamics, 10(4), 809–845. https://doi.org/10.5194/ESD-10-809-2019
Brown, C., Holman, I., & Rounsevell, M. (2021). How modelling paradigms affect simulated future land use change. Earth System Dynamics, 12(1), 211–231. https://doi.org/10.5194/ESD-12-211-2021
Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., & Hartin, C. (2019). GCAM v5. 1: Representing the linkages between energy, water, land, climate, and economic systems. Geoscientific Model Development, 12(2), 677–698.
Constantino, S. M., Schlüter, M., Weber, E. U., & Wijermans, N. (2021). Cognition and behavior in context: a framework and theories to explain natural resource use decisions in social-ecological systems. Sustainability Science, 16(5), 1651-1671.
Davidson, M. R., Filatova, T., Peng, W., Verbeek, L., & Kucuksayacigil, F. (2024). Simulating institutional heterogeneity in sustainability science. Proceedings of the National Academy of Sciences, 121(8), e2215674121. https://doi.org/10.1073/pnas.2215674121
DellaPosta, D., Nee, V., & Opper, S. (2017). Endogenous dynamics of institutional change. Rationality and Society, 29(1), 5–48. https://doi.org/10.1177/1043463116633147
Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Salinas, J. L., & Blöschl, G. (2013). Socio-hydrology: conceptualising human-flood interactions. Hydrology and Earth System Sciences, 17(8), 3295–3303. https://doi.org/10.5194/hess-17-3295-2013
Ellis, E. C., Magliocca, N. R., Stevens, C. J., & Fuller, D. Q. (2018). Evolving the Anthropocene: Linking multi-level selection with long-term social–ecological change. Sustainability Science, 13(1). https://doi.org/10.1007/s11625-017-0513-6
Elsawah, S., Guillaume, J. H. A., 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
Elsawah, S., Filatova, T., Jakeman, A. J., Kettner, A. J., Zellner, M. L., Athanasiadis, I. N., Hamilton, S. H., Axtell, R. L., Brown, D. G., Gilligan, J. M., Janssen, M. A. n, Robinson, D. T., Rozenberg, J., Ullah, I. I. T., & Lade, S. J. (2020). Eight grand challenges in socio-environmental systems modeling. Socio-Environmental Systems Modelling, 2, 16226. https://doi.org/10.18174/sesmo.2020a16226
Epstein, J. M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton University Press.
Epstein, J. M. (2023). Inverse Generative Social Science: Backward to the Future. Journal of Artificial Societies and Social Simulation, 26(2), 9.
Epstein, J. M., Garibay, I., Hatna, E., Koehler, M., & Rand, W. (2023). Special Section on “Inverse Generative Social Science”: Guest Editors’ Statement. Journal of Artificial Societies & Social Simulation, 26(2), 1–2. https://doi.org/10.18564/jasss.5085
Filatova, T., Verburg, P. H., Parker, D. C., & Stannard, C. A. (2013). Spatial agent-based models for socio-ecological systems: Challenges and prospects. Environmental Modelling and Software, 45, 1–7. https://doi.org/10.1016/j.envsoft.2013.03.017
Grêt-Regamey, A., Huber, S. H., & Huber, R. (2019). Actors’ diversity and the resilience of social-ecological systems to global change. Nature Sustainability, 2(4), 290–297. https://doi.org/10.1038/s41893-019-0236-z
Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H.-H., Weiner, J., Wiegand, T., & DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750), 987–991.
Groeneveld, J., Müller, B., Buchmann, C. M., Dressler, G., Guo, C., Hase, N., Hoffmann, F., John, F., Klassert, C., Lauf, T., & others. (2017). Theoretical foundations of human decision-making in agent-based land use models—A review. Environmental Modelling & Software, 87, 39–48.
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, 137, 12–23. https://doi.org/10.1016/j.agsy.2015.03.006
Haider, L. J., Schlüter, M., Folke, C., & Reyers, B. (2021). Rethinking resilience and development: A coevolutionary perspective. Ambio, 50(7), 1304–1312. https://doi.org/10.1007/S13280-020-01485-8/FIGURES/2
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A. (2011). Statistical inference for stochastic simulation models – theory and application. Ecology Letters, 14(8), 816–827. https://doi.org/10.1111/J.1461-0248.2011.01640.X
Hsu, K. L., Moradkhani, H., & Sorooshian, S. (2009). A sequential Bayesian approach for hydrologic model selection and prediction. Water Resources Research, 45(1). https://doi.org/10.1029/2008WR006824
Irwin, E. G., Culligan, P. J., Fischer-Kowalski, M., Law, K. L., Murtugudde, R., & Pfirman, S. (2018). Bridging barriers to advance global sustainability. Nature Sustainability, 1(7), 324–326. https://doi.org/10.1038/s41893-018-0085-1
Jager, W., & Janssen, M. (2012). An updated conceptual framework for integrated modeling of human decision making: The Consumat II. Workshop Complexity in the Real World @ ECCS 2012 - from Policy Intelligence to Intelligent Policy.
Jager, W., Janssen, M. A., De Vries, H. J. M., De Greef, J., & Vlek, C. A. J. (2000). Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model. Ecological Economics, 35(3), 357–379. https://doi.org/10.1016/S0921-8009(00)00220-2
Jakeman, A.J., Elsawah, S., Wang, H.-H., Hamilton, S.H., Melsen, L., and Grimm, V (2024). Towards normalizing good practice across the whole modeling cycle: its instrumentation and future research topics. Socio-Environmental Systems Modelling, vol. 6, 18755, https://doi.org/10.18174/sesmo.18755
Janssen, M. A., & Baggio, J. A. (2017). Using agent-based models to compare behavioral theories on experimental data: Application for irrigation games. Journal of Environmental Psychology, 52, 194–203. https://doi.org/10.1016/J.JENVP.2016.04.018
Juhola, S., Filatova, T., Hochrainer-Stigler, S., Mechler, R., Scheffran, J., & Schweizer, P.-J. (2022). Social tipping points and adaptation limits in the context of systemic risk: Concepts, models and governance. Frontiers in Climate, 4. https://doi.org/10.3389/fclim.2022.1009234
Kaiser, K. E., Flores, A. N., & Hillis, V. (2020). Identifying emergent agent types and effective practices for portability, scalability, and intercomparison in water resource agent-based models. Environmental Modelling & Software, 127, 104671. https://doi.org/https://doi.org/10.1016/j.envsoft.2020.104671
Kuehne, G., Llewellyn, R., Pannell, D. J., Wilkinson, R., Dolling, P., Ouzman, J., & Ewing, M. (2017). Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy. Agricultural Systems, 156, 115–125. https://doi.org/10.1016/j.agsy.2017.06.007
Kumar, S. V., Harrison, K. W., Peters-Lidard, C. D., Santanello, J. A., & Kirschbaum, D. (2014). Assessing the Impact of L-Band Observations on Drought and Flood Risk Estimation: A Decision-Theoretic Approach in an OSSE Environment. Journal of Hydrometeorology, 15(6), 2140–2156. https://doi.org/10.1175/JHM-D-13-0204.1
Krefeld-Schwalb, A., Gabel, S., & Wei, S. (2024). A New Lens on Spillovers: Global Evidence on Overlapping Motives for Sustainable Behaviors [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/syku6
Latombe, G., Parrott, L., & Fortin, D. (2011). Levels of emergence in individual based models: Coping with scarcity of data and pattern redundancy. Ecological Modelling, 222(9), 1557–1568. https://doi.org/10.1016/j.ecolmodel.2011.02.020
Lempert, R. J. (2002). A new decision sciences for complex systems. Proceedings of the National Academy of Sciences of the United States of America, 99(SUPPL. 3), 7309–7313. https://doi.org/10.1073/PNAS.082081699/ASSET/F16E8806-64B4-4568-8022-BAD20A0039D3/ASSETS/GRAPHIC/PQ0820816005.JPEG
Ligmann-Zielinska, A., Siebers, P. O., Magliocca, N., Parker, D., Grimm, V., Du, E. J., Cenek, M., Radchuk, V. T., Arbab, N. N., Li, S., Berger, U., Paudel, R., Robinson, D. T., Jankowski, P., An, L., & Ye, X. (2020). ‘One size does not fit all’: A roadmap of purpose-driven mixed-method pathways for sensitivity analysis of agent-based models. JASSS, 23(1). https://doi.org/10.18564/jasss.4201
Madadgar, S. & H. Moradkhani (2015), Improved Bayesian Multi-modeling: Integration of Copulas and Bayesian Model Averaging. Water Resources Research, 50, 9586–9603, doi: 10.1002/2014WR015965.
Magliocca, N. R. (2020). Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus. Land, 9(12), 519. https://doi.org/10.3390/land9120519
Magliocca, N. R., & Ellis, E. C. (2013). Using Pattern-oriented Modeling (POM) to Cope with Uncertainty in Multi-scale Agent-based Models of Land Change. Transactions in GIS, 17(6), 883–900. https://doi.org/10.1111/tgis.12012
Magliocca, N. R., & Ellis, E. C. (2016). Evolving human landscapes: A virtual laboratory approach. Journal of Land Use Science, 11(6), 642–671.
Magliocca, N., McConnell, V., & Walls, M. (2018). Integrating Global Sensitivity Approaches to Deconstruct Spatial and Temporal Sensitivities of Complex Spatial Agent-Based Models. Journal of Artificial Societies and Social Simulation, 21(1). https://doi.org/10.18564/jasss.3625
Magliocca, N. R., McNamara, D. E., & Murray, A. B. (2011). Long-term, large-scale morphodynamic effects of artificial dune construction along a barrier island coastline. Journal of Coastal Research, 27(5). https://doi.org/10.2112/JCOASTRES-D-10-00088.1
Maier, H. R., Guillaume, J. H. A., van Delden, H., Riddell, G. A., Haasnoot, M., & Kwakkel, J. H. (2016). An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together? Environmental Modelling & Software, 81, 154–164. https://doi.org/10.1016/J.ENVSOFT.2016.03.014
Manson, S. M. (2007). Challenges in evaluating models of geographic complexity. Environment and Planning B: Planning and Design, 34(2), 245–260.
McCulloch, J., Ge, J., Ward, J. A., Heppenstall, A., Polhill, J. G., & Malleson, N. (2022). Calibrating Agent-Based Models Using Uncertainty Quantification Methods. 2021:65:3, 25(2). https://doi.org/10.18564/JASSS.4791
Moradkhani, H. (2008). Hydrologic Remote Sensing and Land Surface Data Assimilation. Sensors, 8(5), Article 5. https://doi.org/10.3390/s8052986
Murray-Rust, D., Brown, C., van Vliet, J., Alam, S. J., Robinson, D. T., Verburg, P. H., & Rounsevell, M. (2014). Combining agent functional types, capitals and services to model land use dynamics. Environmental Modelling & Software, 59, 187–201. https://doi.org/10.1016/j.envsoft.2014.05.019
National Agricultural Statistics Service (NASS). (2022). QuickStats: Census of Agriculture. United States Department of Agriculture. Available at: https://quickstats.nass.usda.gov/.
Niamir, L., Filatova, T., Voinov, A., & Bressers, H. (2018). Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes. Energy Policy, 118, 325–345. https://doi.org/10.1016/J.ENPOL.2018.03.045
Niamir, L., Ivanova, O., & Filatova, T. (2020). Economy-wide impacts of behavioral climate change mitigation: Linking agent-based and computable general equilibrium models. Environmental Modelling & Software, 134, 104839. https://doi.org/10.1016/j.envsoft.2020.104839
Nielsen, J., de Bremond, A., Roy Chowdhury, R., Friis, C., Metternicht, G., Meyfroidt, P., Munroe, D., Pascual, U., & Thomson, A. (2019). Toward a normative land systems science. Current Opinion in Environmental Sustainability, 38, 1–6. https://doi.org/10.1016/J.COSUST.2019.02.003
Noll, B., Filatova, T., & Need, A. (2020). How does private adaptation motivation to climate change vary across cultures? Evidence from a meta-analysis. International Journal of Disaster Risk Reduction, 46, 101615. https://doi.org/10.1016/j.ijdrr.2020.101615
Noll, B., Filatova, T., Need, A., & Taberna, A. (2022). Contextualizing cross-national patterns in household climate change adaptation. Nature Climate Change, 12(1), 30–35. https://doi.org/10.1038/s41558-021-01222-3
Nolte, C. (2020). High-resolution land value maps reveal underestimation of conservation costs in the United States. Proceedings of the National Academy of Sciences, 117(47), 29577–29583. https://doi.org/10.1073/pnas.2012865117
Orach, K., Duit, A., & Schlüter, M. (2020). Sustainable natural resource governance under interest group competition in policy-making. Nature Human Behaviour, 4(9), 898–909. https://doi.org/10.1038/s41562-020-0885-y
Overmars, K. P., & Verburg, P. H. (2007). Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model. Land Use Policy, 24(3), 584–599.
Pacilly, F. C. A., Hofstede, G. J., Lammerts van Bueren, E. T., & Groot, J. C. J. (2019). Analysing social-ecological interactions in disease control: An agent-based model on farmers’ decision making and potato late blight dynamics. Environmental Modelling & Software, 119, 354–373. https://doi.org/10.1016/j.envsoft.2019.06.016
Pathak, R., Magliocca, N.R., Kumar, M., Rathore, L., and Moradkhani, H. (in review). Does the future look irrigated? Evaluating the Likelihood of Irrigation Adoption Within Alabama. Agricultural Systems.
Perello-Moragues, A., Noriega, P., & Poch, M. (2019). Modelling contingent technology adoption in farming irrigation communities. JASSS, 22(4). https://doi.org/10.18564/jasss.4100
Piemontese, L., Kamugisha, R. N., Tukahirwa, J. M. B., Tengberg, A., Pedde, S., & Jaramillo, F. (2021). Barriers to scaling sustainable land and water management in Uganda: A cross-scale archetype approach. Ecology and Society, 26(3), Article 3. https://doi.org/10.5751/ES-12531-260306
Pindyck, R. S. (2013). Climate Change Policy: What Do the Models Tell Us? Journal of Economic Literature, 51(3), 860–872. https://doi.org/10.1257/jel.51.3.860
Price, A. N., Pathak, R., Guthrie, G. M., Kumar, M., Moftakhari, H., Moradkhani, H., Nadolnyak, D., & Magliocca, N. R. (2022). Multi-Level Influences on Center-Pivot Irrigation Adoption in Alabama. Frontiers in Sustainable Food Systems. https://doi.org/10.3389/FSUFS.2022.879161
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. Environmental Modelling & Software, 22(11), 1543–1556. https://doi.org/10.1016/J.ENVSOFT.2007.02.004
Robinson, D. T., van Vliet, J., Brown, C., Dendoncker, N., Holzhauer, S., Moseley, D., Vulturius, G., & Rounsevell, M. D. A. (2022). Identifying data challenges to representing human decision-making in large-scale land-use models. Mapping and Forecasting Land Use, 115–126. https://doi.org/10.1016/B978-0-323-90947-1.00013-2
Rounsevell, M. D. A., Arneth, A., Alexander, P., Brown, D. G., De Noblet-Ducoudré, N., Ellis, E., Finnigan, J., Galvin, K., Grigg, N., Harman, I., Lennox, J., Magliocca, N., Parker, D., O’Neill, B. C., Verburg, P. H., & Young, O. (2014). Towards decision-based global land use models for improved understanding of the Earth system. Earth System Dynamics, 5(1). https://doi.org/10.5194/esd-5-117-2014
Rounsevell, M. D. A., Arneth, A., Brown, C., Cheung, W. W. L., Gimenez, O., Holman, I., Leadley, P., Luján, C., Mahevas, S., Maréchaux, I., Pélissier, R., Verburg, P. H., Vieilledent, G., Wintle, B. A., & Shin, Y. J. (2021). Identifying uncertainties in scenarios and models of socio-ecological systems in support of decision-making. One Earth, 4(7), 967–985. https://doi.org/10.1016/J.ONEEAR.2021.06.003/ATTACHMENT/1D5C5328-5329-4F43-88FC-CDE1521D4244/MMC1.PDF
Rounsevell, M. D. A., Robinson, D. T., & Murray-Rust, D. (2012). From actors to agents in socio-ecological systems models. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1586), 259–269. https://doi.org/10.1098/rstb.2011.0187
Sanga, U., Park, H., Wagner, C. H., Shah, S. H., & Ligmann-Zielinska, A. (2021). How do farmers adapt to agricultural risks in northern India? An agent-based exploration of alternate theories of decision-making. Journal of Environmental Management, 298, 113353. https://doi.org/10.1016/J.JENVMAN.2021.113353
Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M. A., McAllister, R. R. J., Müller, B., Orach, K., Schwarz, N., & Wijermans, N. (2017). A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecological Economics, 131, 21–35. https://doi.org/10.1016/j.ecolecon.2016.08.008
Schulze, J., Müller, B., Groeneveld, J., & Grimm, V. (2017). Agent-based modelling of social-ecological systems: Achievements, challenges, and a way forward. JASSS, 20(2). https://doi.org/10.18564/jasss.3423
Schwarz, N., & Ernst, A. (2009). Agent-based modeling of the diffusion of environmental innovations—An empirical approach. Technological Forecasting and Social Change, 76(4), 497–511. https://doi.org/10.1016/J.TECHFORE.2008.03.024
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., & others. (2019). A description of the advanced research WRF model version 4. National Center for Atmospheric Research: Boulder, CO, USA, 145, 145.
Srikrishnan, V., Lafferty, D. C., Wong, T. E., Lamontagne, J. R., Quinn, J. D., Sharma, S., Molla, N. J., Herman, J. D., Sriver, R. L., Morris, J. F., & Lee, B. S. (2022). Uncertainty Analysis in Multi-Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems. Earth’s Future, 10(8), e2021EF002644. https://doi.org/10.1029/2021EF002644
Sun, Z., Lorscheid, I., Millington, J. D., Lauf, S., Magliocca, N. R., Groeneveld, J., Balbi, S., Nolzen, H., Müller, B., Schulze, J., & Buchmann, C. M. (2016). Simple or complicated agent-based models? A complicated issue. Environmental Modelling and Software, 86. https://doi.org/10.1016/j.envsoft.2016.09.006
Taberna, A., Filatova, T., Hadjimichael, A., & Noll, B. (2023). Uncertainty in boundedly rational household adaptation to environmental shocks. Proceedings of the National Academy of Sciences, 120(44), e2215675120. https://doi.org/10.1073/pnas.2215675120
Troost, C., Huber, R., Bell, A. R., van Delden, H., Filatova, T., Le, Q. B., Lippe, M., Niamir, L., Polhill, J. G., Sun, Z., & Berger, T. (2023). How to keep it adequate: A protocol for ensuring validity in agent-based simulation. Environmental Modelling & Software, 159, 105559. https://doi.org/10.1016/j.envsoft.2022.105559
Valbuena, D., Verburg, P. H., & Bregt, A. K. (2008). A method to define a typology for agent-based analysis in regional land-use research. Agriculture, Ecosystems and Environment, 128(1–2), 27–36. https://doi.org/10.1016/j.agee.2008.04.015
van Duinen, R., Filatova, T., Jager, W., & van der Veen, A. (2016). Going beyond perfect rationality: drought risk, economic choices and the influence of social networks. The Annals of Regional Science, 57, 335-369.
Verburg, P. H., Alexander, P., Evans, T., Magliocca, N. R., Malek, Z., Rounsevell, M. D., & van Vliet, J. (2019). Beyond land cover change: towards a new generation of land use models. Current Opinion in Environmental Sustainability, 38. https://doi.org/10.1016/j.cosust.2019.05.002
Verburg, P. H., Dearing, J. A., Dyke, J. G., Leeuw, S. van der, Seitzinger, S., Steffen, W., & Syvitski, J. (2016). Methods and approaches to modelling the Anthropocene. Global Environmental Change, 39, 328–340. https://doi.org/10.1016/J.GLOENVCHA.2015.08.007
Viglione, A., Di Baldassarre, G., Brandimarte, L., Kuil, L., Carr, G., Salinas, J. L., Scolobig, A., & Blöschl, G. (2014). Insights from socio-hydrology modelling on dealing with flood risk – Roles of collective memory, risk-taking attitude and trust. Journal of Hydrology, 518, 71–82. https://doi.org/10.1016/j.jhydrol.2014.01.018
Vicente-Serrano, S. (2014). Standardized Precipitation Evapotranspiration Index (SPEI). NCAR Climate Data. Available at: https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-evapotranspiration-index-spei. Last accessed June 12, 2023.
Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., 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.4.1.5.16466
Wens, M., Johnson, J. M., Zagaria, C., & Veldkamp, T. I. E. (2019). Integrating human behavior dynamics into drought risk assessment—A sociohydrologic, agent‐based approach. Wiley Interdisciplinary Reviews: Water, 6(4), e1345. https://doi.org/10.1002/wat2.1345
Wens, M., Veldkamp, T. I. E., Mwangi, M., Johnson, J. M., Lasage, R., Haer, T., & Aerts, J. C. J. H. (2020). Simulating Small-Scale Agricultural Adaptation Decisions in Response to Drought Risk: An Empirical Agent-Based Model for Semi-Arid Kenya. Frontiers in Water, 2, 15. https://doi.org/10.3389/frwa.2020.00015
Werner, B. T., & McNamara, D. E. (2007). Dynamics of coupled human-landscape systems. Geomorphology, 91(3–4), 393–407. https://doi.org/10.1016/j.geomorph.2007.04.020
Wijermans, N., Scholz, G., Chappin, É., Heppenstall, A., Filatova, T., Polhill, J. G., Semeniuk, C., & Stöppler, F. (2023). Agent decision-making: The Elephant in the Room-Enabling the justification of decision model fit in social-ecological models. Environmental Modelling & Software, 105850. https://doi.org/10.1016/j.envsoft.2023.105850
Williams, T. G., Brown, D. G., Guikema, S. D., Logan, T. M., Magliocca, N. R., Müller, B., & Steger, C. E. (2022). Integrating Equity Considerations into Agent-Based Modeling: A Conceptual Framework and Practical Guidance. 2021:136:2, 25(3). https://doi.org/10.18564/JASSS.4816
Williams, T. G., Bui, S., Conti, C., Debonne, N., Levers, C., Swart, R., & Verburg, P. H. (2023). Synthesising the diversity of European agri-food networks: A meta-study of actors and power-laden interactions. Global Environmental Change, 83, 102746. https://doi.org/10.1016/j.gloenvcha.2023.102746
Yan, H., Zarekarizi, M., & Moradkhani, H. (2018). Toward improving drought monitoring using the remotely sensed soil moisture assimilation: A parallel particle filtering framework. Remote Sensing of Environment, 216, 456–471. https://doi.org/10.1016/j.rse.2018.07.017
Zagaria, C., Schulp, C. J. E., Zavalloni, M., Viaggi, D., & Verburg, P. H. (2021). Modelling transformational adaptation to climate change among crop farming systems in Romagna, Italy. Agricultural Systems, 188, 103024. https://doi.org/10.1016/j.agsy.2020.103024
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