This repository contains the Python implementation of an agent-based model investigating how localized boundary-crossing dynamics generate large-scale connectivity in structured multi-attractor landscapes.

Agents evolve in a continuous two-dimensional environment composed of attractor basins. A fraction of agents exhibits exploratory higher-mobility dynamics, while the remaining agents remain locally constrained. The model analyzes how localized configurational transitions accumulate into transition networks that progressively integrate the explored state space.

The repository includes:

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MixFarmABM Model examines the competitiveness of second-generation biofuel crops with existing crops and beef cows at the farm level and their impact on the farm structure.

This is an agent-based model of the implementation of the self-enforcing agreement in cooperative teams.

This is an agent-based model that allows to test alternative designs for three model components. The model was built using the LUDAS design strategy, while each alternative is in line with the strategy. Using the model, it can be shown that alternative designs, though built on the same strategy, lead to different land-use patterns over time.

This model explores the effects of agent interaction, information feedback, and adaptive learning in repeated auctions for farmland. It gathers information for three types of sealed-bid auctions, and one English auction and compares the auctions on the basis of several measures, including efficiency, price information revelation, and ability to handle repeated bidding and agent learning.

We explore how dynamic processes related to socioeconomic inequality operate to sort students into, and create stratification among, colleges.

This model simulates the spread of anti-vaccine sentiments in cyber and physical space and how it creates emergence of clusters of anti-vacciners, which eventually lead to higher probablity of disease outbreaks.

This model explores a price Q-learning mechanism for perishable products that considers uncertain demand and customer preferences in a competitive multi-agent retailer market (a model-free environment).

ARISE is a hybrid energy model incorporating macroeconomic data, micro socio-economic data, engineering data and environmental data. This version of ARISE can simulate scenarios of solar energy policy for Indonesia case.

The model aims at estimating household energy consumption and the related greenhouse gas (GHG) emissions reduction based on the behavior of the individual household under different operationalizations of the Theory of Planned Behaviour (TPB).
The original model is developed as a tool to explore households decisions regarding solar panel investments and cumulative consequences of these individual choices (i.e. diffusion of PVs, regional emissions savings, monetary savings). We extend the model to explore a methodological question regarding an interpretation of qualitative concepts from social science theories, specifically Theory of Planned Behaviour in a formal code of quantitative agent-based models (ABMs). We develop 3 versions of the model: one TPB-based ABM designed by the authors and two alternatives inspired by the TPB-ABM of Schwarz and Ernst (2009) and the TPB-ABM of Rai and Robinson (2015). The model is implemented in NetLogo.