Shared vocabularies are not designed by any central authority; they emerge from the bottom up as communities of speakers repeatedly try to understand one another. We study this process with an agent-based Naming Game in which a population that begins with no common vocabulary self-organises a shared language through local pairwise interaction, and we ask how the social network governs the outcome under communication noise and under selection on communicative strategy. The study combines baseline experiments with an extended suite built specifically to meet the standards of replication and statistical inference: every reported quantity is averaged over many independent seeded runs with confidence intervals, and the central claim is tested with a formal interaction statistic. Convergence time scales with population size as a power law with exponent close to four-thirds, matching analytical results for the Naming Game on networks. Restricted networks sustain many coexisting regional dialects, which we quantify with a cluster-based metric and show to be metastable, decaying to a single language as the interaction budget grows. Our central methodological contribution concerns noise robustness. A naive comparison suggests that denser networks simply tolerate more noise, but by comparing topologies at matched mean degree and by sweeping degree directly we separate two effects: connectivity raises robustness with sharply diminishing returns, while at identical degree locally clustered topologies remain significantly less robust than well-mixed ones. The effect of topology therefore survives the removal of the degree confound. Finally, an evolutionary extension shows that selection for communicative success drives inventiveness towards a low level that a frequency-dependent reward for useful innovation raises only slightly, so conservatism is robust rather than an artefact of the fitness function.

An agent-based model of one community whose members meet every week. Members decide whether to attend the community’s gatherings, meet partners there, reveal or withhold their views on two topics, and judge each partner as “like me” or as one of “those people”. Views move toward accepted voices and are held by each member’s starting view. Each conversation has a feeling, which updates the member’s warmth toward her own side and toward the other side and strengthens or weakens the tie between the two partners; ties decide belonging, and belonging decides attendance. Listening enters as one trait, conversational receptiveness. The release includes the BehaviorSpace setup file and the output of every run reported in the accompanying paper.

Green Consumption ABM – Version 2.0.0

This release presents an updated agent-based modelling (ABM) framework developed to investigate social tipping dynamics in green consumption markets.

The model integrates heterogeneous consumer preferences, social influence, firm-level economic decision-making, endogenous pricing, and environmental policy interventions to examine how individual behavioural responses can generate collective market transitions.

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A holistic, agent-based model of scientific knowledge production to simulate large-scale collaborative research processes. The model represents the scientific process as a stochastic, cooperative task-completion game where agents repeatedly collaborate on projects that can yield publications.

The model is implemented in Python as an environment in the popular Gymnasium reinforcement learning library.

This model compares rules for allocating a fixed budget of opportunity (places, training, funding) among people who differ in potential and will. Each agent’s effective potential, its denatotita, is potential multiplied by will. Realisation is denatotita multiplied by the opportunity received, and an agent succeeds when realisation reaches a threshold. Nine allocation regimes are compared: equal shares; shares proportional to denatotita; three threshold rules that reclaim and redistribute the allocations of those who would fail; an efficient benchmark that maximises successes; and three forward-looking rules (foresight, will-aware foresight and hindsight). A mixed regime blends targeting with equal shares. Over time, opportunity develops potential through a skill-formation technology, and will rises with success and falls when an agent is passed over. The model reproduces the article’s results: a closed-form crossover between equal and proportional allocation; a reversal in favour of broad allocation when development is productive; a hollowing-out of potential under success-maximising allocation; and the failure of mixtures of targeting and equal shares. Written in Python (NumPy, pandas, Matplotlib), with fixed random seeds so results are exactly reproducible.

This agent-based model is supporting a simulation that has been used in participatory workshop in Vietnam. It was designed to foster discussion about the joint reading of territorial evolutions and the decrease of investment in the maintenance of hydraulic works, especially irrigation pumping station in a case study territory in the Red River Delta.
The code comprises a number of different experiment to facilitate discussion on specific variables during workshops. One of the experiment has been developped to allow users to interact with hydraulic works and decide to prioritise or de-prioritise their maintenance.
This model has been implemented using the GAMA platform, relying on the gaml language.

This study separates operational interoperability, fare integration and revenue-sharing governance in a stylised multi-operator bus network. An agent-based model implemented in NetLogo 7.0.3 represents passengers, buses and stations across 14 stations, nine routes and three operators. A 2 × 2 institutional design is complemented by financial clearing comparisons and 43,200 robustness runs: behavioural sensitivity (E1), a 243-configuration operational factorial (E2), and targeted joint behavioural–operational stress testing (E3). The audited experiment records show favourable interoperability effects on waiting, total travel time, transfers and realised generalised cost in all 1,944 E2 and 768 E3 configuration-level primary comparisons. Fare integration substantially reduces passenger expenditure but produces an aggregate deficit under the assumed accounting coefficients. Revenue-sharing rules redistribute operator-level outcomes without changing aggregate profit or passenger trajectories by construction. The model is intended to identify mechanisms and test conditional robustness, not predict city-specific policy effects. The paper contributes an explicit separation of access, pricing and settlement mechanisms coupled with reproducible factorial and stress-test comparisons.

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.

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This Python agent-based model simulates the diffusion of instructional practices among primary-school science teachers connected through within-school and cross-school networks. It represents teachers as agents nested in schools and compares five adoption mechanisms: simple contagion, proportion thresholds, count thresholds, adaptive norm conformity, and an individual-only baseline. The model evaluates fixed-budget training allocations, including concentrated and distributed policies, and estimates when school-level dose-response evidence can support targeted allocation. Experiments examine peer interference, cross-school contact intensity, global critical transitions, allocation stability, and the distinction between participant-level and system-level outcomes. Teacher attributes are informed by a de-identified Beijing questionnaire, whereas team sizes and contact networks are explicit model assumptions. The package includes executable Python scripts, ODD documentation, parameter tables, experiment metadata, simulation outputs, and the authorized questionnaire workbook required to reproduce the questionnaire-informed analyses. Results are intended as model-based design evidence for future longitudinal research rather than forecasts of adoption rates.

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.

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