This model explores different aspects of the formation of urban neighbourhoods where residents believe in values distant from those dominant in society. Or, at least, this is what the Danish government beliefs when they discuss their politics about parallel societies. This simulation is set to understand (a) whether these alternative values areas form and what determines their formation, (b) if they are linked to low or no income residents, and (c) what happens if they disappear from the map. All these three points are part of the Danish government policy. This agent-based model is set to understand the boundaries and effects of this policy.

This model is a replication of Torsten Hägerstrand’s 1965 model–one of the earliest known calibrated and validated simulations with implicit “agent based” methodology.

C++ and Netlogo models presented in G. Bravo (2011), “Agents’ beliefs and the evolution of institutions for common-pool resource management”. Rationality and Society 23(1).

This repository the multi-agent simulation software for the paper “Comparison of Competing Market Mechanisms with Reinforcement Learning in a CarPooling Scenario”. It’s a mutlithreaded Javaapplication.

The BENCH agent-based model is designed and developed to study shifts in residential energy use and corresponding emissions driven by behavioral changes among heterogeneous individuals.

3 simple models to illustrate diffusion of innovations.
The models are discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

This is based off my previous Profiler tutorial model, but with an added tutorial on converting it into a model usable with BehaviorSpace, and creating a BehaviorSpace experiment.

A discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations focusing on possible risks that could materialize in the final phase of the epidemic.

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 study presents a System Dynamics (SD) model that explores the “trajectories of homelessness” among youth outside of the formal care system. Unlike traditional approaches that view runaway behavior as a discrete choice, this model reinterprets it as a neurobiological adaptation to chronic resource deprivation and systemic neglect.
​The model incorporates key mechanisms such as ‘Allostatic Load’ accumulation, ‘PFC-Amygdala Switching’, and the ‘Iatrogenic Effects’ of shelter policies. It utilizes Monte Carlo simulations to demonstrate how structural factors create a “probabilistic vulnerability,” trapping youth in cycles of survival crime and isolation regardless of individual resilience.
​The uploaded code includes a Python implementation of the model to ensure reproducibility of the stochastic analysis presented in the paper.