chitwanabm is a spatially explicit agent-based model of population and land use in the Chitwan Valley, Nepal, designed to explore feedbacks between population and environment, with a heavy focus on community context and individual-level variation.

This ABM deals with commuting choices in the Italian city of Varese. Empirical data inform agents’ attitudes and modal choices costs and emissions. We evaluate ex ante the impact of policies for less polluting commuting choices.

A dynmaic microsimulation model to project the UK population over time

HUMLAND Fire-in-the-Hole is a conceptual agent-based model (ABM) designed to explore the ecological and behavioral consequences of fire-driven hunting strategies employed by hunter-gatherers, specifically Neanderthals, during the Last Interglacial period around the Neumark-Nord (Germany) archaeological site.

This model builds on and specializes the HUMLAND 1.0.0 model (Nikulina et al. 2024), integrating anthropogenic fires, elephant group behavior, and landscape response to simulate interactions between humans, megafauna, and vegetation over time.

Agers and non-agers agent compete over a spatial landscape. When two agents occupy the same grid, who will survive is decided by a random draw where chances of survival are proportional to fitness. Agents have offspring each time step who are born at a distance b from the parent agent and the offpring inherits their genetic fitness plus a random term. Genetic fitness decreases with time, representing environmental change but effective non-inheritable fitness can increase as animals learn and get bigger.

This model aims to replicate the evolution of opinions and behaviours on a communal plan over time. It also aims to foster community dialogue on simulation outcomes, promoting inclusivity and engagement. Individuals (referred to as agents), grouped based on Sinus Milieus (Groh-Samberg et al., 2023), face a binary choice: support or oppose the plan. Motivated by experiential, social, and value needs (Antosz et al., 2019), their decision is influenced by how well the plan aligns with these fundamental needs.

The model is a microsimulation, where the agents don’t Interact with each other. It simulates income distribution, unemployment dynamics, education, and Family grant in Brazil, focusing on the impact on social inequality. It tracks the indicators Gini index, Lorenz curve, and Palma ratio. The objective is to explore how these factors influence wealth distribution and social inequality over time.
This work was developed in partnership with the Graduate Program in Computational Modeling, in the Universidade Federal do Rio Grande - FURG, in Brazil.

The (cultural) evolution of cooperative breeding in harsh environments.

This Python module contain a function that is able to test the ergodicity of a given agent based model. It is sufficient to produce one long time series and many smaller time series. The function uses

Demographic microsimulation model used in speed tests against LIAM 2.