This agent-based model simulates the lifecycle, movement, and satisfaction of teachers within an urban educational system composed of multiple universities and schools. Each teacher agent transitions through several possible roles: newcomer, university student, unemployed graduate, and employed teacher. Teachers’ pathways are shaped by spatial configuration, institutional capacities, individual characteristics, and dynamic interactions with schools and universities. Universities are assigned spatial locations with a controllable level of centralization and are characterized by academic ratings, capacity, and alumni records. Schools are distributed throughout the city, each with a limited number of vacancies, hiring requirements, and offered salaries. Teachers apply to universities based on the alignment of their personal academic profiles with institutional ratings, pursue studies, and upon graduation become candidates for employment at schools.
The employment process is driven by a decentralized matching of teacher expectations and school offers, taking into account factors such as salary, proximity, and peer similarity. Teachers’ satisfaction evolves over time, reflecting both institutional characteristics and the composition of their colleagues; low satisfaction may prompt teachers to transfer between schools within their mobility radius. Mortality and teacher attrition further shape workforce dynamics, leading to continuous recruitment of newcomers to maintain a stable population. The model tracks university reputation through the academic performance and number of alumni, and visualizes key metrics including teacher status distribution, school staffing, university alumni counts, and overall satisfaction. This structure enables the exploration of policy interventions, hiring and training strategies, and the impact of spatial and institutional design on the allocation, retention, and happiness of urban educational staff.

LogoClim is a NetLogo model for simulating and visualizing global climate conditions. It allows researchers to integrate high-resolution climate data into agent-based models, supporting reproducible research in ecology, agriculture, environmental science, and other fields that rely on climate data integration.

The model utilizes raster data to represent climate variables such as temperature and precipitation over time. It incorporates historical data (1951-2024) and future climate projections (2021-2100) derived from global climate models under various Shared Socioeconomic Pathways (SSPs) (O’Neill et al., 2017). All climate inputs come from WorldClim 2.1, a widely used source of high-resolution, interpolated climate datasets based on weather station observations worldwide (Fick & Hijmans, 2017), available for academic and other non-commercial use.

LogoClim follows the FAIR Principles for Research Software (Barker et al., 2022) and is openly available on the CoMSES Network and GitHub.

BESTMAP-ABM-DE is an agent-based model to determine the adoption and spatial allocation of selected agri-environmental schemes (AES) by individual farmers in the Mulde River Basin located in Western Saxony, Germany. The selected AES are buffer areas, cover crops, maintaining permanent grassland and conversion of arable land to permanent grassland. While the first three schemes have already been offered in the case study area, the latter scheme is a hypothetical scheme designed to test the impact of potential policy changes. For the first model analyses, only the currently offered schemes are considered. With the model, the effect of different scenarios of policy design on patterns of adoption can be investigated. In particular, the model can be used to study the social-ecological consequences of agricultural policies at different spatial and temporal scales and, in combination with biophysical models, test the ecological implications of different designs of the EU’s Common Agricultural Policy. The model was developed in the BESTMAP project.

Sahelian transhumance is a seasonal pastoral mobility between the transhumant’s terroir of origin and one or more host terroirs. Sahelian transhumance can last several months and extend over hundreds of kilometers. Its purpose is to ensure efficient and inexpensive feeding of the herd’s ruminants. This paper describes an agent-based model to determine the spatio-temporal distribution of Sahelian transhumant herds and their impact on vegetation. Three scenarios based on different values of rainfall and the proportion of vegetation that can be grazed by transhumant herds are simulated. The results of the simulations show that the impact of Sahelian transhumant herds on vegetation is not significant and that rainfall does not impact the alley phase of transhumance. The beginning of the rainy season has a strong temporal impact on the spatial distribution of transhumant herds during the return phase of transhumance.

This agent-based model simulates the implementation of a Transfer of Development Rights (TDR) mechanism in a stylized urban environment inspired by Dublin. It explores how developer agents interact with land parcels under spatial zoning, conservation protections, and incentive-based policy rules. The model captures emergent outcomes such as compact growth, green and heritage zone preservation, and public cost-efficiency. Built in NetLogo, the model enables experimentation with variable FSI bonuses, developer behavior, and spatial alignment of sending/receiving zones. It is intended as a policy sandbox to test market-aligned planning tools under behavioral and spatial uncertainty.

A model to investigate the Evolution of Conditional Cooperation in a Spatial Public Goods Game. We consider two conditional cooperation strategies: one based on thresholds (Battu & Srinivasan, 2020) and another based on independent decisions for each number of cooperating neighbors. We examine the effects of productivity and conditional cooperation criteria on the trajectory of cooperation. Cooperation is evolving with no need for additional mechanisms apart from spatial structure when agents follow conditional strategies. We confirm the positive influence of productivity and cluster formation on the evolution of cooperation in spatial models. Results are robust for the two types of conditional cooperation strategies.

The emergence of cooperation in human societies is often linked to environmental constraints, yet the specific conditions that promote cooperative behavior remain an open question. This study examines how resource unpredictability and spatial dispersion influence the evolution of cooperation using an agent-based model (ABM). Our simulations test the effects of rainfall variability and resource distribution on the survival of cooperative and non-cooperative strategies. The results show that cooperation is most likely to emerge when resources are patchy, widely spaced, and rainfall is unpredictable. In these environments, non-cooperators rapidly deplete local resources and face high mortality when forced to migrate between distant patches. In contrast, cooperators—who store and share resources—can better endure extended droughts and irregular resource availability. While rainfall stochasticity alone does not directly select for cooperation, its interaction with resource patchiness and spatial constraints creates conditions where cooperative strategies provide a survival advantage. These findings offer broader insights into how environmental uncertainty shapes social organization in resource-limited settings. By integrating ecological constraints into computational modeling, this study contributes to a deeper understanding of the conditions that drive cooperation across diverse human and animal systems.

Reusing existing material stocks in developed built environments can significantly reduce the environmental footprint of the construction and demolition sector. However, material reuse in urban areas presents technical, temporal, and geographical challenges. Although a better understanding of spatial and temporal changes in material stocks could improve city resource management, limited scientific contributions have addressed this challenge.
This study details the steps followed in developing a spatially explicit rule-based simulation of materials stock. The simulation provides a proof of concept by incorporating the spatial and temporal dimensions of construction and demolition activities to analyse how various urban parameters determine material flows and embodied carbon in urban areas. The model explores the effects of 1) re-using recycled materials, 2) demolitions, 3) renovations and 4) various building typologies.
To showcase the model’s capabilities, the residential building stock of Gothenburg City is used as a case study, and eight building materials are tracked. Environmental impacts (A1-A3) are calculated with embodied carbon factors. The main parameters are explored in a baseline scenario. Then, a second scenario focuses on a hypothetical policy that promotes improvements in building energy performance.
The simulation can be expanded to include more materials and built environment assets and allows for future explorations on, for example, the role of logistics, the implementation of recycling or reuse stations, and, in general, supporting sustainable and circular strategies from the construction sector.

The HUMan Impact on LANDscapes (HUMLAND) 2.0.0 is an enhanced version of HUMLAND 1.0.0, developed to track and quantify the intensity of various impacts on landscapes at a continental scale. The model is designed to identify the most influential factors in the transformation of interglacial vegetation, with a particular focus on the burning practices of hunter-gatherers. HUMLAND 2.0.0 incorporates a wide range of spatial datasets as both inputs and targets (expected modelling results) for simulations across Last Interglacial (~130,000–116,000 BP) and Early Holocene (~11,700–8,000 BP).

FIsheries Simulation with Human COmplex DEcision-making (FISHCODE) is an agent-based model to depict and analyze current and future spatio-temporal dynamics of three German fishing fleets in the southern North Sea. Every agent (fishing vessel) makes daily decisions about if, what, and how long to fish. Weather, fuel and fish prices, as well as the actions of their colleagues influence agents’ decisions. To combine behavioral theories and enable agents to make dynamic decision, we implemented the Consumat approach, a framework in which agents’ decisions vary in complexity and social engagement depending on their satisfaction and uncertainty. Every agent has three satisfactions and two uncertainties representing different behavioral aspects, i.e. habitual behavior, profit maximization, competition, conformism, and planning insecurity. Availability of extensive information on fishing trips allowed us to parameterize many model parameters directly from data, while others were calibrated using pattern oriented modelling. Model validation showed that spatial and temporal aggregated ABM outputs were in realistic ranges when compared to observed data. Our ABM hence represents a tool to assess the impact of the ever growing challenges to North Sea fisheries and provides insight into fisher behavior beyond profit maximization.