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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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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The model represents a large, traditional firm competing with small, sustainable food producers. The large firm operates under conventional assumptions, pursuing monopoly profits maximized through standard economic principles. Its behavior is driven by economies of scale, which compel small firms to strategically differentiate themselves to deliver added value to consumers. Consequently, small firms focus on offering higher-quality products—a factor that matters significantly to consumers.

The gains achieved by small firms represent social profits that hinge upon establishing co-participation schemes with farmers and building trustworthy relationships with consumers. Cooperation with farmers directly shapes production processes and product quality. Because a firm trusted by one consumer becomes visible to neighbors, trust can diffuse and evolve into a collective identity. Ultimately, population-level legitimation occurs when the organizational form of the organic food producer gains widespread acceptance within the consumer community.

The model’s behavior depends on three key factors: a trust parameter, a legitimation intensity parameter, and an aspiration-level weight (for the large firm).

This model extends Schelling’s segregation model with mobility sharing. Agents of two groups occupy cells of a 32 × 32 grid and repeatedly choose activity destinations subject to a preference threshold, the minimum share of ingroup neighbours they accept. Trips are matched into shared rides between members of different groups, using one of four matching algorithms on a shareability network (maximal cross-group matching, maximum weight matching on contact time, maximum cardinality matching, or random greedy matching) with a maximum detour constraint. After each trip, an agent’s preference threshold changes with the time spent in contact with outgroup members.

Starting from complete segregation, the model examines under which conditions contact during shared trips leads to integration. Besides the main scenario, in which contact always lowers the threshold, it includes two extensions. In the first, a share of adversarial agents becomes less tolerant with contact, and this share grows or shrinks with the current level of segregation, governed by a branching dissimilarity index and a conversion rate. In the second, each contact raises or lowers the threshold with a probability that depends on the agent’s current attitude relative to a compatibility threshold.

Segregation is measured with the index of dissimilarity and the Gini coefficient. The model file includes BehaviorSpace experiments for each analysis in the associated article. The matching step uses the NetworkX library through the Python extension of NetLogo (requires Python 3 with numpy and networkx 3.x).

This model represents individuals from two different groups interacting and sharing attitudes toward both groups. Through these interactions, individuals adjust their attitudes, which can lead to different outcomes in opinion dynamics. The model can study both intra-group and inter-group opinion dynamics. Under certain conditions, agents with initially moderate attitudes might enter a negative spiral, leading to increasingly extreme intergroup attitudes. Alternatively, these moderate attitudes can foster consensus between groups, resulting in agreement on intergroup attitudes. This model serves as a tool for exploring opinion polarization, particularly in contexts where attitudes evolve through interaction rather than remaining static.

By adjusting different parameters, one can examine the effects of variations in attitude distributions; group size and relative group size; contributions of xenophobia, opinion distance, and individual attitudes towards the outgroup; as well as ratio between xenophobia and attitudinal bias to the overall intergroup bias.

The model accompanies the paper When contact backfires, and when it does not: A computational model of the opinion dynamics of intergroup attitudes.

This repository contains a Python simulation script for studying network-based linguistic diversity. The model extends the classic Greenberg Diversity Index by introducing diversity measures defined over social ties rather than only over population shares. It also includes two extensions: a competence-conditioned version and a multilayer version.

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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Replication package for a network diffusion model of how Protestant belief spreads through a
signed social network, used to compare three versions of balance theory.

Artificial Anasazi is an agent-based model of farming households in the Long House Valley, Arizona, from 800 to 1350 CE. Each household chooses where to farm and settle based on expected harvest, water sources and stored maize, and simulated household counts are compared with the archaeological record.

This release adapts the NetLogo Models Library version (Stonedahl & Wilensky 2010) of Janssen’s (2009) NetLogo replication of the original Ascape model (Dean et al. 2000; Axtell et al. 2002). It contains four configurations in one NetLogo 7.0.4 file, selected with two switches:

• Both switches off: the baseline, which is the Models Library model with small fixes that make it work as intended (the arable-uplands zone name, the setup base yield, the initial household count, one-tick-lag capacity reporting, and seed control with observation-only instruments).
• soil-on?: dynamic soil quality that depletes under cultivation and regrows, following Janssen (2010, eq. 4).
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