This is a first preliminary simulation model to model segregation in the city of Salzburg, Austria.

This model is a more comprehensive version of the original model; descriptions and expanations are added

This Agent-Based model intends to explore the conditions for the emergence and change of land use patterns in Central Asian oases and similar contexts.

The Nice Musical Chairs (NMC) model represent the competition for space between groups of stakeholders of farming and herding activities in the arid Afro-Eurasia.

This model simulates networking mechanisms of an empirical social network. It correlates event determinants with place-based geography and social capital production.

Overview

The Weather model is a procedural generation model designed to create realistic daily weather data for socioecological simulations. It generates synthetic weather time series for solar radiation, temperature, and precipitation using algorithms based on sinusoidal and double logistic functions. The model incorporates stochastic variation to mimic unpredictable weather patterns and aims to provide realistic yet flexible weather inputs for exploring diverse climate scenarios.

The Weather model can be used independently or integrated into larger models, providing realistic weather patterns without extensive coding or data collection. It can be customized to meet specific requirements, enabling users to gain a better understanding of the underlying mechanisms and have greater confidence in their applications.

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We build a stylized model of a network of business angel investors and start-up entrepreneurs. Decisions are based on trust as a decision making tool under true uncertainty.

Hierarchical problem-solving model
The model simulates a hierarchical problem-solving process in which a manager delegates parts of a problem to specialists, who attempt to solve specific aspects based on their unique skills. The goal is to examine how effectively the hierarchical structure works in solving the problem, the total cost of the process, and the resulting solution quality.

Problem-solving random network model
The model simulates a network of agents (generalists) who collaboratively solve a fixed problem by iterating over it and using their individual skills to reduce the problem’s complexity. The goal is to study the dynamics of the problem-solving process, including agent interactions, work cycles, total cost, and solution quality.

This purpose of this model is to understand how the coupled demographic dynamics of herds and households constrain the growth of livestock populations in pastoral systems.

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.