The Soy2Grow ABM aims to simulate the adoption of soybean production in Flanders, Belgium. The model primarily considers two types of agents as farmers: 1) arable and 2) dairy farmers. Each farmer, based on its type, assesses the feasibility of adopting soybean cultivation. The feasibility assessment depends on many interrelated factors, including price, production costs, yield, disease, drought (i.e., environmental stress), social pressure, group formations, learning and skills, risk-taking, subsidies, target profit margins, tolerance to bad experiences, etc. Moreover, after adopting soybean production, agents will reassess their performance. If their performance is unsatisfactory, an agent may opt out of soy production. Therefore, one of the main outcomes to look for in the model is the number of adopters over time.

The main agents are farmers. Generally, factors influencing farmers’ decision-making are divided into seven main areas: 1) external environmental factors, 2) cooperation and learning (with slight differences depending on whether they are arable or dairy farmers), 3) crop-specific factors, 4) economics, 5) support frameworks, 6) behavioral factors, and 7) the role of mobile toasters (applicable only to dairy farmers).
Moreover, factors not only influence decision-making but also interact with each other. Specifically, external environmental factors (i.e., stress) will result in lower yield and quality (protein content). The reducing effect, identified during participatory workshops, can reach 50 %. Skills can grow and improve yield; however, their growth has a limit and follows different learning curves depending on how individualistic a farmer is. During participatory workshops, it was identified that, contrary to cooperative farmers, individualistic farmers may learn faster and reach their limits more quickly. Furthermore, subsidies directly affect revenues and profit margins; however, their impact may disappear when they are removed. In the case of dairy farmers, mobile toasters play an important role, adding toasting and processing costs to those producing soy for their animal feed consumption.
Last but not least, behavioral factors directly influence the final adoption decision. For example, high risk-taking farmers may adopt faster, whereas more conservative farmers may wait for their neighbors to adopt first. Farmers may evaluate their success based on their own targets and may also consider other crops rather than soy.

This repository contains an agent-based simulation model exploring how status hierarchies influence the emergence and sustainability of cooperation in task-oriented groups. The model builds on evolutionary game theory to examine the dynamics of cooperation under single-leader and multi-leader hierarchies, investigating factors such as group size, assortativity, and hierarchical clarity. Key findings highlight the trade-offs between different leadership structures in fostering group cooperation and reveal the conditions under which cooperation is most stable.

The repository includes code for simulations, numerical analysis scripts, and visualization tools to replicate the results presented in the manuscript titled “Status hierarchies and the emergence of cooperation in task groups.”

Feel free to explore, reproduce the findings, or adapt the model for further research!

ViSA 2.0.0 is an updated version of ViSA 1.0.0 aiming at integrating empirical data of a new use case that is much smaller than in the first version to include field scale analysis. Further, the code of the model is simplified to make the model easier and faster. Some features from the previous version have been removed.
It simulates decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. It investigates conditions and scenarios that can increase the supply of ecosystem services while keeping the viability of the social system by suggesting different mixes of initial unit utilities and decision rules.

ViSA simulates the decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. The lack of sufficient supply of ESSs triggers stakeholders to apply different management options to increase their supply. However, while attempting to reduce the supply-demand gap, conflicts arise among stakeholders due to the tradeoff nature of some ESS. ViSA investigates conditions and scenarios that can minimize such supply-demand gap while reducing the risk of conflicts by suggesting different mixes of management options and decision rules.

AgentEx aims to advance understanding of group processes for sustainable management of a common pool resource (CPR). By supporting the development and test explanations of cooperation and sustainable exploitation.

Agent-based model of hunting behavior of Ache hunter-gatherers from Paraguay. We evaluate the effect of group size and cooperative hunting

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when sharing individuals engage in intentional consumption leveling with a given probability.

This model can be used to explore under which conditions agents behave as observed in field experiments on irrigation games.

This abstract model explores the emergence of altruistic behavior in networked societies. The model allows users to experiment with a number of population-level parameters to better understand what conditions contribute to the emergence of altruism.

The model explores food distribution patterns that emerge in artificial small-scale human groups when agents follow a set of spatially explicit sharing interaction rules derived from a theory on the evolution of the egalitarian social instinct.