3spire is an ABM where farming households make management decisions aimed at satisficing along the aspirational dimensions: food self-sufficiency, income, and leisure. Households decision outcomes depend on their social networks, knowledge, assets, household needs, past management, and climate/market trends

This agent-based model simulates the implementation of a Transfer of Development Rights (TDR) mechanism in a stylized urban environment inspired by Dublin. It explores how developer agents interact with land parcels under spatial zoning, conservation protections, and incentive-based policy rules. The model captures emergent outcomes such as compact growth, green and heritage zone preservation, and public cost-efficiency. Built in NetLogo, the model enables experimentation with variable FSI bonuses, developer behavior, and spatial alignment of sending/receiving zones. It is intended as a policy sandbox to test market-aligned planning tools under behavioral and spatial uncertainty.

This agent-based model simulates the interactions between smallholder farming households, land-use dynamics, and ecosystem services in a rural landscape of Eastern Madagascar. It explores how alternative agricultural practices —shifting agriculture, rice cultivation, and agroforestry—combined with varying levels of forest protection, influence food production, food security, dietary diversity, and forest biodiversity over time. The landscape is represented as a grid of spatially explicit patches characterized by land use, ecological attributes, and regeneration dynamics. Agents make yearly decisions on land management based on demographic pressures, agricultural returns, and institutional constraints. Crop yields are affected by stochastic biotic and abiotic disruptions, modulated by local ecosystem regulation functions. The model additionally represents foraging as a secondary food source and pressure on biodiversity. The model supports the analysis of long-term trade-offs between agricultural productivity, human nutrition, and conservation under different policy and land-use scenarios.

This computational model accompanies the article “The Informational Assumptions of Schelling Segregation: An Agent-Based Decomposition of Cue Inference, Cultural Schemas, and Residential Sorting.” It implements an agent-based model in which agents infer latent neighborhood-type classes from noisy non-demographic cues through schema-specific diagnostic mappings, update beliefs, and relocate when satisfaction on a preferred latent class falls below a threshold.

The model serves as a mechanism-isolation device for studying the informational architecture underlying Schelling-style residential sorting. It includes the principal sweep configuration (14,400 runs across a seven-parameter grid), a disagreement-metric sub-sweep with permutation-minimized Jensen-Shannon divergence recorded natively, controls (positive, negative, and frozen-belief), a paired-seed cue-channel perturbation experiment, and selected-cell sensitivity sweeps for cue persistence and home-biased mobility.

The full ODD protocol, parameter manifests, deterministic seed schedules, processed outputs, regenerable figure scripts, the verification test suite, and the satisfaction-mapping audit document are included. Every reported run is deterministic given a (config, seed) pair, and an included audit script verifies bit-for-bit replay on sampled runs.

An agent-based model of how public space conditions shape the collective formation of local sports communities in high-mobility cities. A small gathering is already exercising in an abstract, systematically configurable public space; the model asks how strangers join them and whether the loose gathering consolidates into a stable community. Three conditions are manipulated, each acting through exactly one channel: spatial legibility governs whether a passer-by notices the activity, routine compatibility governs whether a member attends in a given week, and social anchor availability governs how much one episode of co-presence builds a tie. Ties carry a continuous strength with two thresholds, so that relationships of differing strength cross their thresholds at different condition levels. Because the model does not represent the origin of an activity, dissolution is irreversible and survival is a clean binary outcome. Across 32,790 runs in eight experiments the model yields two well separated thresholds along routine compatibility — one for survival, one for consolidation — with a wide band between them in which a space is used continuously but no community forms. The tie submodel is adapted from Jin, Girvan and Newman (2001) and verified against it by docking. Robustness is established against the seeded gathering, the pre-existing social network, a joint ±20 per cent perturbation of the behavioural parameters, and the parameters of the tie machinery.

This is an extension of the original RAGE model (Dressler et al. 2018), where we add learning capabilities to agents, specifically learning-by-doing and social learning (two processes central to adaptive (co-)management).

The extension module is applied to smallholder farmers’ decision-making - here, a pasture (patch) is the private property of the household (agent) placed on it and there is no movement of the households. Households observe the state of the pasture and their neighrbours to make decisions on how many livestock to place on their pasture every year. Three new behavioural types are created (which cannot be combined with the original ones): E-RO (baseline behaviour), E-LBD (learning-by-doing) and E-RO-SL1 (social learning). Similarly to the original model, these three types can be compared regarding long-term social-ecological performance. In addition, a global strategy switching option (corresponding to double-loop learning) allows users to study how behavioural strategies diffuse in a heterogeneous population of learning and non-learning agents.

An important modification of the original model is that extension agents are heterogeneous in how they deal with uncertainty. This is represented by an agent property, called the r-parameter (household-risk-att in the code). The r-parameter is catch-all for various factors that form an agent’s disposition to act in a certain way, such as: uncertainty in the sensing (partial observability of the resource system), noise in the information received, or an inherent characteristic of the agent, for instance, their risk attitude.

This is NetLogo code that presents two alternative implementations of Correlated Random Walk (CRW):
- 1. drawing the turning angles from the uniform distribution, i.e. drawing the angle with the same probability from a certain given range;
- 2. drawing the turning angles from von Mises distribution.
The move lengths are drawn from the lognormal distribution with the specified parameters.

Correlated Random Walk is used to represent the movement of animal individuals in two-dimensional space. When modeled as CRW, the direction of movement at any time step is correlated with the direction of movement at the previous time step. Although originally used to describe the movement of insects, CRW was later shown to sufficiently well describe the empirical movement data of other animals, such as wild boars, caribous, sea stars.
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A-KinGDom simulates the emergence of the social structure in a group of non-human primates. The model includes dominance and affiliative interactions which allow us to define four different attack and affiliative strategies.

The application of a smartphone application to register physical encounters between individuals is considered by public health authorities, as a means to reduce the number of infections in the 2020 COVID-19 pandemic. The general idea is that continuous registration of all other smartphones in the vicinity of an individual’s smartphone potentially enables early warning of the owners of the other smartphones, in case the individual is tested positive as infected. Those other individuals can then go into isolation and be considered for testing. The purpose of the present simulation is to explore the potential effects of this application on frequencies of infection, isolation, and positive and negative infection test results.

The Retail Competition Agent-based Model (RC-ABM) is designed to simulate the retail competition system in the Region of Waterloo, Ontario, Canada, which which explicitly represents store competition behaviour. Through the RC-ABM, we aim to answer 4 research questions: 1) What is the level of correspondence between market share and revenue acquisition for an agent-based approach compared to a traditional location-allocation-based approach? 2) To what degree can the observed store spatial pattern be reproduced by competition? 3) To what degree are their path dependent patterns of retail success? 4) What is the relationship between retail survival and the endogenous geographic characteristics of stores and consumer expenditures?