A NetLogo 3D 7.0 model of a market with several floors. Buyers walk, keep their distance from one another, choose among the stalls they can see and change floors. Social proof acts on where buyers go (attraction) and on whether they buy (conversion), and crowding lowers the chance of a purchase. The model tests whether social proof is sufficient to congest such a market and separates the roles of influence, layout and local density. The archive contains the model with every BehaviorSpace experiment of the companion paper, all simulation outputs, the pre-analysis plan, an ODD+D description and the Python code that reproduces every reported number, table and figure.

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.

This article builds on the ideas of Hayes (2002) (see Hayes, B. (2002). Follow the Money, American Scientist 90(5), 400, doi:10.1511/2002.33.3291) on the rise of inequality in economic exchange, using the analogy of a yard sale exchange. The Yard Sale model illustrates how inequality can emerge among traders that initially have a homogeneous wealth distribution. We explore ethics interventions applying the six ethics lenses framework, as defined by the Markkula Center for Applied Ethics (Santa Clara University) through the Yard Sale model.

The baseline conditions of the model assume a radius of interaction = 10, a protection factor = 0, a risk upper bound = 1, no risk and wealth distribution mechanisms, no taxes, and no universal income, and absence of prosocial agents. Variations of these factor are associated with severla ethics related interventions to mitigate inequality. There are six ethics lenses, which are explained next.

The model’s primary aim was not to be entirely realistic; instead, it was designed to point to new questions, test the usefulness of the tool, and lay the groundwork for new insights. As such, this model is targeted at ecologists, ornithologists, and urban planners who want to test and simulate how different birds face the challenge of a densely urbanized city that aims to enhance its natural connectivity and foster a wide range of biodiversity. We aimed to identify bird movement at the city-wide scale, encompassing representatives from the entire avian community, while examining differences in movement between totally random models and an informed model, defined by expert criteria.

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 model builds on Smaldino et al (2017) by using the outgroup aversion model on a network platform and analyzing the results.

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.