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.

This model is an abstract simulation of the COVID-19 virus in the United States population. It demonstrates how different masks of different types affect the progress of the virus.

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

The basic premise of the model is to simulate several ‘agents’ going through build-buy cycles: Build: Factories follow simple rules of strategy in the allocation of resources between making exploration and exploitation type products. Buy: Each of two types of Consumers, early-adopters and late adopters, follow simple purchase decision rules in deciding to purchase a product from one of two randomly chosen factories. Thus, the two working ‘agents’ of the model are ‘factories’ and […]

Objective is to simulate policy interventions in an integrated demand-supply model. The underlying demand function links both sides. Diffusion proceeds if interactions distribute awareness (Epidemic effect) and rivalry reduces the market price (Probit effect). Endogeneity is given due to the fact that consumer awareness as well as their willingness-to-pay drives supply-side rivalry. Firm´s entry and exit decisions as well as quantity and price settings are driven by Cournot competition.

This generic agent-based model allows the user to simulate and explore the influence of servicising policies on the uptake of servicising and on economic, environmental and social effects, notably absolute decoupling.

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

The model explores the impact of journal metrics (e.g., the notorious impact factor) on the perception that academics have of an article’s scientific value.