IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v489y2024ics0304380023003393.html
   My bibliography  Save this article

Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning

Author

Listed:
  • Apetrei, Cristina I.
  • Strelkovskii, Nikita
  • Khabarov, Nikolay
  • Javalera Rincón, Valeria

Abstract

Computational models have been used to investigate farmers’ decision outcomes, yet classical economics assumptions prevail, while learning processes and adaptive behaviour are overlooked. This paper advances the conceptualisation, modelling and understanding of learning-by-doing and social learning, two key processes in adaptive (co-)management literature. We expand a pre-existing agent-based model (ABM) of an agricultural social-ecological system, RAGE (Dressler et al., 2018). We endow human agents with learning-by-doing and social learning capabilities, and we study the impact of their learning strategies on economic, ecological and social outcomes. Methodologically, we contribute to an under-explored area of modelling farmers’ behaviour. Results show that agents who employ learning better match their decisions to the ecological conditions than those who do not. Imitating the learning type of successful agents further improves outcomes. Different learning processes are suited to different goals. We report on conditions under which learning-by-doing becomes dominant in a population with mixed learning approaches.

Suggested Citation

  • Apetrei, Cristina I. & Strelkovskii, Nikita & Khabarov, Nikolay & Javalera Rincón, Valeria, 2024. "Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning," Ecological Modelling, Elsevier, vol. 489(C).
    • Handle: RePEc:eee:ecomod:v:489:y:2024:i:c:s0304380023003393
    DOI: 10.1016/j.ecolmodel.2023.110609
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023003393
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110609?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 115512, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Marra, Michele & Pannell, David J. & Abadi Ghadim, Amir, 2003. "The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve?," Agricultural Systems, Elsevier, vol. 75(2-3), pages 215-234.
    3. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    4. 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.
    5. Feola, Giuseppe & Binder, Claudia R., 2010. "Towards an improved understanding of farmers' behaviour: The integrative agent-centred (IAC) framework," Ecological Economics, Elsevier, vol. 69(12), pages 2323-2333, October.
    6. John H. Miller & Scott E. Page, 2007. "Social Science in Between, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    7. Sadahisa Kato & Jack Ahern, 2008. "'Learning by doing': adaptive planning as a strategy to address uncertainty in planning," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 51(4), pages 543-559.
    8. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    9. Gérard Weisbuch & Gérard Boudjema, 1999. "Dynamical Aspects in the Adoption of Agri-Environmental Measures," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 11-36.
    10. Hannah Muelder & Tatiana Filatova, 2018. "One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(4), pages 1-5.
    11. M. Muro & P. Jeffrey, 2008. "A critical review of the theory and application of social learning in participatory natural resource management processes," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 51(3), pages 325-344.
    12. Nicholas M. Gotts & J. Gareth Polhill, 2009. "When and How to Imitate Your Neighbours: Lessons from and for FEARLUS," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(3), pages 1-2.
    13. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    14. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    15. Oriel FeldmanHall & Amitai Shenhav, 2019. "Resolving uncertainty in a social world," Nature Human Behaviour, Nature, vol. 3(5), pages 426-435, May.
    16. Berglund, Christer & Soderholm, Patrik, 2006. "Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models," Energy Policy, Elsevier, vol. 34(12), pages 1344-1356, August.
    17. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    18. Janssen, Sander & van Ittersum, Martin K., 2007. "Assessing farm innovations and responses to policies: A review of bio-economic farm models," Agricultural Systems, Elsevier, vol. 94(3), pages 622-636, June.
    19. Jasmina Arifovic & John Ledyard, 2004. "Scaling Up Learning Models in Public Good Games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 203-238, May.
    20. Iris Lorscheid & Bernd-Oliver Heine & Matthias Meyer, 2012. "Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 22-62, March.
    21. Robert, Marion & Thomas, Alban & Bergez, Jacques Eric, 2016. "Processes of adpatation in farm decision-making models. A review," TSE Working Papers 16-731, Toulouse School of Economics (TSE).
    22. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    23. 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, Elsevier, vol. 35(3), pages 357-379, December.
    24. Anne-Maree Dowd & Nadine Marshall & Aysha Fleming & Emma Jakku & Estelle Gaillard & Mark Howden, 2014. "The role of networks in transforming Australian agriculture," Nature Climate Change, Nature, vol. 4(7), pages 558-563, July.
    25. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    26. Beaman, Lori & Dillon, Andrew, 2018. "Diffusion of agricultural information within social networks: Evidence on gender inequalities from Mali," Journal of Development Economics, Elsevier, vol. 133(C), pages 147-161.
    27. Hunecke, Claudia & Engler, Alejandra & Jara-Rojas, Roberto & Poortvliet, P. Marijn, 2017. "Understanding the role of social capital in adoption decisions: An application to irrigation technology," Agricultural Systems, Elsevier, vol. 153(C), pages 221-231.
    28. Pieter Berg & Tom Wenseleers, 2018. "Uncertainty about social interactions leads to the evolution of social heuristics," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
    29. Jouvet, Pierre-André & Schumacher, Ingmar, 2012. "Learning-by-doing and the costs of a backstop for energy transition and sustainability," Ecological Economics, Elsevier, vol. 73(C), pages 122-132.
    30. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 115511, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    31. 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.
    32. Reidsma, Pytrik & Janssen, Sander & Jansen, Jacques & van Ittersum, Martin K., 2018. "On the development and use of farm models for policy impact assessment in the European Union – A review," Agricultural Systems, Elsevier, vol. 159(C), pages 111-125.
    33. Monika Suškevičs & Thomas Hahn & Romina Rodela & Biljana Macura & Claudia Pahl-Wostl, 2018. "Learning for social-ecological change: a qualitative review of outcomes across empirical literature in natural resource management," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 61(7), pages 1085-1112, June.
    34. Malawska, Anna & Topping, Christopher John, 2016. "Evaluating the role of behavioral factors and practical constraints in the performance of an agent-based model of farmer decision making," Agricultural Systems, Elsevier, vol. 143(C), pages 136-146.
    35. Janssen, Marco & de Vries, Bert, 1998. "The battle of perspectives: a multi-agent model with adaptive responses to climate change," Ecological Economics, Elsevier, vol. 26(1), pages 43-65, July.
    36. François Rebaudo & Olivier Dangles, 2011. "Coupled Information Diffusion–Pest Dynamics Models Predict Delayed Benefits of Farmer Cooperation in Pest Management Programs," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-10, October.
    37. Jule Thober & Birgit Müller & Jürgen Groeneveld & Volker Grimm, 2017. "Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-8.
    38. Lindkvist, Emilie & Norberg, Jon, 2014. "Modeling experiential learning: The challenges posed by threshold dynamics for sustainable renewable resource management," Ecological Economics, Elsevier, vol. 104(C), pages 107-118.
    39. Fraser J Morgan & Adam J Daigneault, 2015. "Estimating Impacts of Climate Change Policy on Land Use: An Agent-Based Modelling Approach," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-20, May.
    40. Glock, C. H. & Grosse, E. H. & Jaber, M. Y. & Smunt, T. L., 2019. "Applications of learning curves in production and operations management: A systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 107692, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    41. Calum Brown & Peter Alexander & Sascha Holzhauer & Mark D. A. Rounsevell, 2017. "Behavioral models of climate change adaptation and mitigation in land‐based sectors," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 8(2), March.
    42. Rianne Duinen & Tatiana Filatova & Wander Jager & Anne Veen, 2016. "Going beyond perfect rationality: drought risk, economic choices and the influence of social networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 335-369, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noeldeke, Beatrice & Winter, Etti & Ntawuhiganayo, Elisée Bahati, 2022. "Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda," Ecological Economics, Elsevier, vol. 200(C).
    2. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    3. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
    4. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    5. Bourceret, Amélie & Amblard, Laurence & Mathias, Jean-Denis, 2022. "Adapting the governance of social–ecological systems to behavioural dynamics: An agent-based model for water quality management using the theory of planned behaviour," Ecological Economics, Elsevier, vol. 194(C).
    6. 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.
    7. George Van Voorn & Geerten Hengeveld & Jan Verhagen, 2020. "An agent based model representation to assess resilience and efficiency of food supply chains," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-27, November.
    8. Chion, Clément & Lamontagne, P. & Turgeon, S. & Parrott, L. & Landry, J.-A. & Marceau, D.J. & Martins, C.C.A. & Michaud, R. & Ménard, N. & Cantin, G. & Dionne, S., 2011. "Eliciting cognitive processes underlying patterns of human–wildlife interactions for agent-based modelling," Ecological Modelling, Elsevier, vol. 222(14), pages 2213-2226.
    9. Brinkmann, Katja & Kübler, Daniel & Liehr, Stefan & Buerkert, Andreas, 2021. "Agent-based modelling of the social-ecological nature of poverty traps in southwestern Madagascar," Agricultural Systems, Elsevier, vol. 190(C).
    10. Juana Castro & Stefan Drews & Filippos Exadaktylos & Joël Foramitti & Franziska Klein & Théo Konc & Ivan Savin & Jeroen van den Bergh, 2020. "A review of agent‐based modeling of climate‐energy policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    11. An, Li & Grimm, Volker & Sullivan, Abigail & Turner II, B.L. & Malleson, Nicolas & Heppenstall, Alison & Vincenot, Christian & Robinson, Derek & Ye, Xinyue & Liu, Jianguo & Lindkvist, Emilie & Tang, W, 2021. "Challenges, tasks, and opportunities in modeling agent-based complex systems," Ecological Modelling, Elsevier, vol. 457(C).
    12. Malawska, Anna & Topping, Christopher John, 2016. "Evaluating the role of behavioral factors and practical constraints in the performance of an agent-based model of farmer decision making," Agricultural Systems, Elsevier, vol. 143(C), pages 136-146.
    13. Mössinger, Johannes & Troost, Christian & Berger, Thomas, 2022. "Bridging the gap between models and users: A lightweight mobile interface for optimized farming decisions in interactive modeling sessions," Agricultural Systems, Elsevier, vol. 195(C).
    14. Reinhard, Stijn & Naranjo, María A. & Polman, Nico & Hennen, Wil, 2022. "Modelling choices and social interactions with a threshold public good: Investment decisions in a polder in Bangladesh," Land Use Policy, Elsevier, vol. 113(C).
    15. 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.
    16. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    17. Dakotah Hogan & John Elshaw & Clay Koschnick & Jonathan Ritschel & Adedeji Badiru & Shawn Valentine, 2020. "Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates," Forecasting, MDPI, vol. 2(4), pages 1-23, October.
    18. Wang, Xiong & Ferreira, Fernando A.F. & Chang, Ching-Ter, 2022. "Multi-objective competency-based approach to project scheduling and staff assignment: Case study of an internal audit project," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    19. Li, Francis G.N. & Trutnevyte, Evelina & Strachan, Neil, 2015. "A review of socio-technical energy transition (STET) models," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 290-305.
    20. Slijper, Thomas & Urquhart, Julie & Poortvliet, P. Marijn & Soriano, Bárbara & Meuwissen, Miranda P.M., 2022. "Exploring how social capital and learning are related to the resilience of Dutch arable farmers," Agricultural Systems, Elsevier, vol. 198(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:489:y:2024:i:c:s0304380023003393. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.