Chapter 4 Understanding rural farming systems with agent-based modelling and decision making
4.2 Background and cash transfers in study area
4.5.2 Modelling coupled human-environment systems
To solve the complex issues of coupled human-environment system and the modelling representation, O’Sullivan et al (2015) summarized four approaches including (1) sensitivity analysis, (2) participatory modelling, (3) hybrid modelling, and (4) theoretical engagement. This chapter utilized theoretical engagement to explain the patterns of livelihood that we discovered from empirical analysis and link the pattern-to-process as the foundation of the decision theory in our ABM. We also applied sensitivity analysis to our model by using Turkey HSD analysis and variance-based sensitivity analysis, which helps us understand the coupled system thoroughly and in-depth. The hybrid modelling approach, in O’Sullivan’s opinion, is to couple models from different discipline, such as link carbon model and the human behavior model together
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(Robinson et al., 2009). We adopt the ensemble approach from the climate change modelling community which is to gather a few models that solve one problem with different assumptions. This ensemble approach can be combined with the idea of modular development in ABM (Bell et al., 2015). Same module but with different ingredients can be used as ensemble members for ABM.
Uncertainty in ABM and coupled human-environment systems has been a long-term challenge that modelers are facing. There have been many advancements in model development and evaluation, such as ODD process for modelers to communicate (Polhill et al., 2008;
Schreinemachers and Berger, 2011) model component evaluation (Parker et al., 2006), and meta- analysis (Magliocca et al., 2015). Sensitivity analysis is a commonly used approach, and its explicit assessment of uncertainty can help modelers focus on research questions, lead to proper explanations, and make novel predictions. Nevertheless, this post-hoc analysis might be operated on a wrong fundamental assumption within the model. The ensemble approach, together with sensitivity analysis, treats uncertainty from the outset instead in one direction and a fixed setting, which gives us better confidence and more options to inform policy makers.
Nonlinearity is a significant attribute of any coupled human-environment system (Liu et al., 2007). Our results clearly show that there is no general pattern of the relationship between different features to the livelihood outcome. The initial capital distribution changing from low to high does not cause the community level livelihood metrics to switch from poor to good. The impact of cash transfer is even more distinctive. Having no cash transfer at all and having higher cash transfer unit are simultaneously negative or positive for community income equality and wealth. This has also been observed in our empirical data analysis in Chapter 3 (e.g., the size of household contributing to the likelihood of dependence differently in two cohorts).The baseline
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experiment has lower total Gini index and Poverty Gap index compared to both the no cash transfer experiment and higher cash transfer experiment. For individual decision regimes, sometimes the baseline has better community performance; sometimes the no- and high-cash transfer experiments have better outcomes. This nonlinearity has also emerged in other ABM simulations, such as the results from FEARLUSS-SPOMM, which suggests against the naïve expectations of more incentives will secure more biodiversity (Polhill et al., 2013), instead, it is a nonlinear relation between incentives and biodiversity. Despite the nonlinearity in cash transfer and capital attributes, labour shows a more consistent effect on the outcome: having more large families tend to make the community wealthier and income more equally distributed.
4.6 Conclusion
Multiple efforts have been put to cope with uncertainty that emerges from modelling coupled human-environment systems, especially the human decision making as a main source for the uncertainty. In addition to current attempts of using ABMs to understand rural livelihood systems, we construct an ABM with three livelihood strategy modules as ensemble members to represent the livelihood dynamics of smallholders in the Brazilian Amazon estuary region, to fully explore the alternative outcomes around different decision making models. The ensemble approach that we presented here is not new in climate modelling community, which has been used as a standard approach to predict future climate trajectories, but it is rather novel for ABMs to incorporate the uncertainty into the structure of the modelling process. With multiple decision makings established as ensemble members, the ABM is capable of investigating the range of livelihood outcomes and the socio-economic and demographic variables that play significant roles in different decision regimes.
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Developing ABMs with an ensemble approach (in this case, it is the human decision
module) can also provide insights to the different impacts of cash transfers on livelihood income. Results from sensitivity analysis suggest that a household’s livelihood strategy is the most
influential to livelihood outcomes at both the community level and individual level. Cash transfer programs largely improve the income and its equal distribution, but the influence changes with the decision strategy (e.g., the most drastic impacts are on Max Leisure households). Further, it suggests that policies that target at different livelihood factors (e.g., land type, family size, education) will also have different impacts for household agents who use different decision making models. For example, improving education is significant for Max Profit adopters but not so much for households with Subsistence First or Max Leisure, where the demographic structure plays a significant role. As such, the integration of the ensemble approach in ABMs offers the chance to cope with uncertainty that inherited in coupled human-environment systems and emerged in the modelling process, which allows us to evaluate more possible outcomes from policies and better inform future policy changes.
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