This archive contains the SAFE-ABM computational model developed to evaluate targeted intervention strategies for protecting essential workers during epidemic conditions, accompanying the published article “Essential Workers at Risk: An Agent-Based Model (SAFE-ABM) with Uncertainty Quantification” in the Journal of Artificial Societies and Social Simulation, 29(4), Article 7 (2026). https://doi.org/10.18564/jasss.6276. SAFE-ABM is an agent-based model that simulates disease transmission through structured interactions across households, workplaces, and schools, while incorporating stochastic disease progression and uncertainty in transmission, recovery, and mortality parameters.

The model evaluates five intervention scenarios: Scenario 1, no restrictions; Scenario 2, school-from-home; Scenario 3, essential-workers-only movement; Scenario 4, rotational shifts among essential workers with quarantine of symptomatic active workers; and Scenario 5, rotational shifts among essential workers without quarantine. The archive includes scenario-specific simulation scripts, comparison-analysis scripts, saved derived outputs, README documentation, software requirements, and reproducibility notes.

The included comparison scripts reproduce the main numerical summaries and manuscript outputs, including median epidemic trajectories, predictive-uncertainty plots, cumulative recovery and death summaries, inflection-point summaries, Welch-test comparisons, and mutual-information sensitivity rankings. Saved derived outputs are included so that the main figures and tables can be reproduced without rerunning all large-scale simulations.

…

HOSS(-5) is an exploratory agent-based prototype of a small artificial society, written in Python. Each agent is a “ph” (punto humano, “human point”) that is born, is educated, works, consumes and dies. Its yearly decisions depend on a psychological profile (archetype × character), its economic status and its intentionality. Agents interact indirectly through families, firms in 10 sectors, a labour market with about 200 job categories, and a State that provides public services, employment and a Universal Basic Income, adjusting taxes and UBI each year to seek fiscal balance.

The model simulates a few thousand agents over decades of virtual time and records yearly aggregates (population, GDP, income, Gini index, HDI). A separate analysis script explores the results and fits Gamma distributions to individual incomes, comparing the shape parameter with Gini and HDI.

HOSS does not aim to predict real societies or claim empirical validation. It is a sandbox for comparing scenarios and thinking about the organizational design of society from an engineering perspective. Known limitations: no direct social relations between agents, prices and production follow simple rules rather than supply-demand dynamics, and organizations are black boxes.

…

An agent-based model in which fourteen European Union member states each carry a cooperation
propensity in [0,1], updated by a logistic link applied to a latent index that combines the unit’s
own previous state, a weighted average of the other units’ states, and four min-max normalised
exogenous indicators (cooperative benefit, opportunistic temptation, systemic exit cost,
intertemporal confidence). The logistic centres are anchored so that the observed 2024
configuration is an exact fixed point of the baseline map, the device that makes such models
…

GFN & Technology Models Library (2025–2035) is a collection of four interrelated computational models focused on global financial networks, cascading default risks, technological development, and technological lag, with particular attention to Russia, the USA, and China. The library combines agent-based modeling, system dynamics, Monte-Carlo simulation, and network analysis. It includes: (1) a Numba-accelerated Monte-Carlo technology index model; (2) a hybrid GFN + cascading defaults (Gai–Kapadia) + Russian system dynamics model; (3) a twin-simulation GFN+SBS framework with selective bailout and Russia’s peripheral position; and (4) an endogenous GFN model with technology centrality, dependence, and tech lag dynamics under sanctions scenarios. GFN & Technology Models Library (2025–2035) is a collection of four interrelated computational models focused on global financial networks, cascading default risks, technological development, and technological lag, with particular attention to Russia, the USA, and China.

The library combines agent-based modeling, system dynamics, Monte-Carlo simulation, and network analysis. It includes:

1. Technology Index 2035 (Monte-Carlo + Numba) — hybrid SD/ABM model projecting a composite technology index under different scenarios (Russia baseline, China, USA) with aggressive Numba optimization.
2. RUS-GFN-TSI-2035 CASCADE — hybrid model integrating a Gai–Kapadia-style cascading defaults network, an agent-based Global Financial Network layer, and a system-dynamics module for the Russian economy (TSI, inflation, technology, trust, etc.).
   …

This NetLogo model simulates the movement and foraging behavior of 20 bird species (classified as urban avoiders or utilizers) across a 20×20m grid landscape representing southern Bogotá, Colombia. Agents follow an optimal foraging strategy (90% of the time) or move randomly (10%), gaining energy from land-cover-dependent food sources and dying if energy falls below their basal metabolic rate. The model compares two landscape scenarios — current land cover (2016) and a proposed scenario incorporating the ‘Media Luna del Sur’ ecological corridor from Bogotá’s 2022–2035 Land Use Plan (POT) — to evaluate whether the proposed green infrastructure improves functional connectivity for avian biodiversity across the urban matrix. Outputs include cumulative patch visitation counts, exported as raster files, used to generate connectivity heatmaps.

Organizations operate under conditions of imperfect performance in which human error is inevitable, yet errors are rarely examined as the triggers for the managerial interactions that shape organizational culture over time. The present research introduces an agent-based simulation of a work team completing a fixed sequence of tasks under varying degrees of managerial oversight and response policy. The model isolates the mechanical loss of throughput caused by errors from the psychological and cultural consequences of managerial reactions, which are categorized into ignoring, correcting or punishing. Furthermore, the model incorporates an autonomous self-notice mechanism, allowing workers to correct themselves in the absence of managerial intervention. By tracking the accumulation of worker resentment and the transient enhancements in learning, the simulation acts as a dynamic laboratory for observing delayed consequences, nonlinear tipping points and systemic organizational collapse. The results reveal a central paradox of organizational control. Highly monitored punitive environments generate high short-term throughput, yet they simultaneously accumulate interactional injustice and resentment that engineer a rapid cascading turnover and the highest probability of systemic collapse. Conversely, corrective policies combined with active monitoring achieve equivalent throughput while sustaining workforce viability. A comprehensive sensitivity analysis establishes that error accumulation is primarily determined by structural factors, namely agent-level mistake propensity and task difficulty, while resignation dynamics, resentment accumulation and collapse timing remain predominantly governed by managerial policy, a hierarchy independently corroborated by a surrogate model and shown to be stable across independent seeds and across stakeholder weighting scenarios. The study bridges the operational mechanics of task completion and the social dynamics of workplace mistreatment, illustrating how short-term punitive success often masks long-term structural fragility.

Agent-based model of cooperation among fourteen EU member states under geopolitical shocks, 2024-2040. States are adaptive agents on the observed intra-EU trade network, updating a cooperation propensity through inertia, network influence, and a repeated-game payoff structure built from verified Eurostat, Eurobarometer and IMF data. An anchoring device makes the observed 2024 configuration exactly stationary absent shocks, so results are read as deviations from an empirical baseline. Five scenarios of increasing severity are simulated with Monte Carlo replication, up to an extreme counterfactual crisis in the Taiwan Strait. Includes six robustness batteries and a full ODD description.

The Oussa Pañn model is an agent-based representation of the social-ecological system embedded in the Oussa Pañn role-playing game. It represents shellfish harvesters making individual livelihood decisions between harvesting renewable shellfish resources from mudflats and engaging in alternative income-generating activities in a village. The model was developed to (i) compare simulated and observed game trajectories, (ii) verify the internal consistency of recorded game data, and (iii) provide a baseline against which observed player behaviour can be compared, notably through random decision-making scenarios.

This entry provides an Agent-Based Model (ABM) of opinion and tolerance dynamics in artificial societies, implemented in the frame of the Concord/Partial Antagonism (C/PA) model. This version is specifically designed to investigate the impact of asymmetric influence through two leadership archetypes—Dictator and Democrat—within networked societies.
The model is highly modular, allowing the user to simulate three distinct social scenarios:
Baseline C/PA: Self-organization of a society without external influence.
Stubborn Leadership: Introduction of a “solid conviction” leader (Dictator or Democrat) who exerts constant pressure without changing their own stance.
Feedback Leadership: A democratic leader receptive to social feedback, where the leader’s opinion evolves based on interactions with the population.
Theoretical Foundations:
…

Agent-based model simulating 60 undergraduate CS students over 8 semesters to investigate how differential AI tool access affects academic inequality, performance distributions, and dependency formation. Implements a three-phase AI dependency mechanism (effort reduction, metacognitive miscalibration, capability erosion) under five policy scenarios with a 4,050-run parameter sensitivity sweep.