Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda
Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda
- Ecological Economics, 200, p.107529, 2022 .
Advancing the transition towards more sustainable agriculture requires policy interventions that support farmers' adoption of sustainable practices. Models can support policy-makers in developing and testing interventions. For these models to provide reliable support, their underlying assumptions need to reflect reality and hence adequately represent human decision-making. This study compares several approaches that represent human decision-making. The comparison is applied to farmers' decision to adopt agroforestry. An agent-based simulation model is calibrated to a case study in rural Rwanda, where socio-economic survey data was collected from 145 small-scale farmers. Of these farmers, 72 were randomly selected to participate in a role-playing game, during which the players decided about adopting agroforestry. The game was conducted to validate the tested decision-making approaches. The simulations show that the decision-making approaches predict significantly different agroforestry adoption rates. Compared with the role-playing game, the Theory of Planned Behaviour exhibits the highest validity. Rational choice theory and the econometric approach overestimate implementation. Bounded rationality approaches underestimate the share of adopters. The results highlight the importance of adequately representing farmers' adoption decisions in models for providing reliable forecasts and effective policy support.
DECISION-MAKING
AGENT-BASED MODELLING
THEORY OF PLANNED BEHAVIOUR
RATIONAL CHOICE THEORY
BOUNDED RATIONALITY
AGROFORESTRY ADOPTION
Advancing the transition towards more sustainable agriculture requires policy interventions that support farmers' adoption of sustainable practices. Models can support policy-makers in developing and testing interventions. For these models to provide reliable support, their underlying assumptions need to reflect reality and hence adequately represent human decision-making. This study compares several approaches that represent human decision-making. The comparison is applied to farmers' decision to adopt agroforestry. An agent-based simulation model is calibrated to a case study in rural Rwanda, where socio-economic survey data was collected from 145 small-scale farmers. Of these farmers, 72 were randomly selected to participate in a role-playing game, during which the players decided about adopting agroforestry. The game was conducted to validate the tested decision-making approaches. The simulations show that the decision-making approaches predict significantly different agroforestry adoption rates. Compared with the role-playing game, the Theory of Planned Behaviour exhibits the highest validity. Rational choice theory and the econometric approach overestimate implementation. Bounded rationality approaches underestimate the share of adopters. The results highlight the importance of adequately representing farmers' adoption decisions in models for providing reliable forecasts and effective policy support.
DECISION-MAKING
AGENT-BASED MODELLING
THEORY OF PLANNED BEHAVIOUR
RATIONAL CHOICE THEORY
BOUNDED RATIONALITY
AGROFORESTRY ADOPTION