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.

This model implements a classic scenario used in Reinforcement Learning problem, the “Cliff Walking Problem”. Consider the gridworld shown below (SUTTON; BARTO, 2018). This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff.” Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start (SUTTON; BARTO, 2018).

The problem is solved in this model using the Q-Learning algorithm. The algorithm is implemented with the support of the NetLogo Q-Learning Extension

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.

This agent-based model using ‘Blanche’ software provides policy-makers with a simulation-based demonstration illustrating how autonomous agents network and operate complementary systems in a decentral

The model explores the possibility of the evolution of cooperation due to indirect reciprocity when agents derive information about the past behavior of the opponent in one-shot dilemma games.

INOvPOP is designed to simulate population dynamics (abundance, sex-age composition and distribution in the landscape) of white-tailed deer (Odocoileus virginianus) for selected Indiana counties. Updated for netLogo 6.4.0

This model is to match students and schools using real-world student admission mechanisms. The mechanisms in this model are serial dictatorship, deferred acceptance, the Boston mechanism, Chinese Parallel, and the Taipei mechanism.

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