The Role of Learning in Smallholder Farmers’ Decision Outcomes: An Agent-Based Modelling Approach

Apetrei, C. (2021). The Role of Learning in Smallholder Farmers’ Decision Outcomes: An Agent-Based Modelling Approach. IIASA YSSP Report. Laxenburg, Austria: IIASA

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Abstract

Smallholder farmers’ decisions have important implications for the global food system and global environmental changes. Models have been successfully used to understand human decision-making, including that of farmers, and to explore policy interventions towards sustainability. A frequent approach has been to represent economic decisions in aggregate ways and to assume perfect information and utility maximisation. In reality, farmers have to cope with uncertainties about the dynamics of the social-ecological system (SES) that they are part of and they also act in ways that deviate from rational choice theory. As such, learning processes and adaptive behaviour represent an unexplored, but potentially rich avenue for understanding decision-making. In this paper we study the social-ecological outcomes of two such processes that have been suggested as key by sustainability scholars: learning-by-doing and social learning. We expand a pre-existing stylised agent-based model (ABM) of human decision-making within an SES to include learning-by-doing and social learning agents, and we study the impact of their learning strategies on economic, ecological and social outcomes. Our results show that learning agents are able to better match their decisions to the ecological conditions than non-learning agents. In addition, depending on the normative goals pursued, one learning strategy might be more suitable than the other. Lastly, we analyse diffusion dynamics and we find that an initial share of learning-by-doing agents of about 11% might constitute a critical mass for this behaviour to become dominant in a population. This points to research areas of policy relevance that could be explored in future studies.

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