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.

This model is a part of an ongoing research project on Multiagent Reinforcement Learning (MARL). The ODD protocol is included in the model. In this version of the model, Proximal Policy Optimization (PPO) is designed in the agent behaviors. It also includes a designed experiment in its Behavior Space which is used in the Response Surface Methodology and training of an Artificial Neural Network (ANN) based Recommender System.

An agent-based microsimulation of insecticide-treated net (ITN) distribution and adoption in Kenya (2003–2024), integrating the Theory of Planned Behaviour, Rogers diffusion, Weibull net decay, and a GPS-based two-layer social network. 8,561 household agents calibrated via Approximate Bayesian Computation to six DHS/MIS survey waves, achieving 2.42 pp mean absolute error on Kenya-level ownership. The analysis chain supports mechanism counterfactuals and policy experiments on equity outcomes of ITN distribution strategies.

The current model is designed to examine whether—and under what conditions—minority influence can generate social change. Specifically, the model assesses whether empirically validated psychological mechanisms of indirect minority influence, operating in combination, can produce system-level social change, defined as the initial minority opinion becoming the majority position. Notably, this model formalizes Moscovici’s (1976) genetic model of social influence using agent-based modeling.

This is a model that explores how a few fishermen sharing a common fishery learn their harvesting strategies under different incentive settings, and how individual greed, cooperation, and sustainability penalties shape resource depletion and the tragedy of the commons.

This model is an agent-based simulation designed to explore how climate-induced environmental degradation can contribute to the emergence of social violence in coastal communities that depend heavily on ecosystem services for their livelihoods. The model represents a coupled social–ecological system in which environmental shocks—such as sea level rise and marine ecosystem decline—affect local economic conditions, food security, and community stability.

Agents in the model represent individuals whose livelihoods depend on coastal ecosystems. Environmental degradation reduces ecosystem productivity and increases economic hardship, which can lead to the formation of grievances among agents. The model incorporates behavioral thresholds that determine how individuals respond to hardship and perceived injustice. Under certain conditions—particularly when institutional capacity and law enforcement effectiveness are limited—these grievances may escalate into violent behavior.

The simulation allows users to explore how different climate scenarios, levels of ecosystem degradation, livelihood dependence, and institutional responses influence the probability of social instability and violence. By modeling the interactions between environmental stress, socio-economic vulnerability, and governance capacity, the model provides a computational framework for examining potential pathways linking climate change and conflict in coastal social–ecological systems.

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An agent-based model of urban travel behaviour in Dublin, Ireland, built in NetLogo and empirically grounded in 2016 travel survey data. Each agent represents a Dublin resident initialised with real socio-demographic attributes — including age, gender, household size and car ownership, income, driving licence status, and access to local amenities — alongside observed trip characteristics such as distance, travel time, and trip type (work, shopping, leisure).
At each time step, agents choose between four transport modes (car, public transport, cycling, and walking) across short, medium, and long trips. Mode choice is governed by a preference vector that weighs personal need satisfaction against social influence from neighbouring agents reflecting consumat framework. Satisfaction evolves dynamically based on cost (incorporating Irish motor tax bands and per-km operating rates), travel time, and trip-type suitability, with an uncertainty parameter capturing variability in perceived utility over time.
The model tracks aggregate modal shares and total CO2 emission at each tick, enabling exploration of how policy interventions — such as fuel taxation, public transport pricing, or active travel incentives — might shift the city’s travel demand profile over 100 simulated days.

An agent-based model of irregular warfare in which civilians adapt their alignment in response to local violence, security presence, and territorial control. The simulation explores how decentralized interactions generate spatial patterns of loyalty, conflict dynamics, and stabilization.

The aim of this model is to study the dynamic propagation of individual climate adaptive behaviours in different scenarios within the analytical framework of conservation motivation theory, focusing on the impact of social and experiential learning on the adoption of climate adaptive behaviours by coastal farmers.
Model for paper “Promoting climate resilience through learning-based behavioural change: Insights from an agent-based model of a coastal farming community in Guangxi, China” in Environmental Science & Policy, Volume 179, May 2026, 104375, https://doi.org/10.1016/j.envsci.2026.104375

This model aims to study the dynamic propagation of individual behaviour within social networks, focusing on how normative expectations (NE) and experiential expectations (EE) jointly influence behavioural decisions. It also explores the long-term effects of different intervention scenarios (such as enhancing visibility, considering indirect social links, and education) on behavioural propagation patterns and the overall behaviour of the group.
The model was developed in NetLogo 6.4. It generates simulated groups based on large-scale survey data, utilizing NetLogo’s CSV, Table, and Matrix extensions. The model also employs the NW extension to enable network analysis functionality.
The model is designed for research “Shaping social norms to promote individual response behavior in public crises: An agent-based modeling approach” in Journal of Cleaner Production, Volume 554, 8 April 2026, 148014
https://doi.org/10.1016/j.jclepro.2026.148014