TY - JOUR

T1 - Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models

T2 - Learning-by-doing and social learning

AU - Apetrei, Cristina I.

AU - Strelkovskii, Nikita

AU - Khabarov, Nikolay

AU - Javalera Rincón, Valeria

N1 - Funding Information: Part of this research was developed in the Young Scientists Summer Program (YSSP) at the International Institute for Applied Systems Analysis (IIASA), Laxenburg (Austria). We thank YSSP members of the 2021 cohort and the members of the Advanced Systems Analysis program at IIASA, and in particular Dr Matthias Wildemeersch, for their feedback on early versions of our model design and implementation. This study follows from previous conceptual work conducted by the first author as part of a large transdisciplinary research project (Leverage Points for Sustainability Transformation – https://leveragepoints.org) at Leuphana University Lüneburg (Germany). The first author acknowledges and thanks all project members for their collegiality. The first author extends their gratitude to Prof Dr Dennis Meadows and Prof Dr Daniel Lang for inspiration and support. We also warmly thank Prof Dr Bert de Vries at Utrecht University for comments on our draft manuscript, as well as several anonymous reviewers for their constructive suggestions. Nikita Strelkovskii, Nikolay Khabarov and Valeria Javalera Rincón gratefully acknowledge funding from IIASA and the National Member Organizations that support the institute. Publisher Copyright: © 2023 The Author(s)

PY - 2024/3/1

Y1 - 2024/3/1

N2 - 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.

AB - 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.

KW - Adaptive (co-)management

KW - Agent-based modelling

KW - Learning-by-doing

KW - Smallholder farmers’ decisions

KW - Social learning

KW - Social-ecological systems

KW - Biology

KW - Ecosystems Research

UR - http://www.scopus.com/inward/record.url?scp=85184999163&partnerID=8YFLogxK

U2 - 10.1016/j.ecolmodel.2023.110609

DO - 10.1016/j.ecolmodel.2023.110609

M3 - Journal articles

AN - SCOPUS:85184999163

VL - 489

JO - Ecological Modelling

JF - Ecological Modelling

SN - 0304-3800

M1 - 110609

ER -