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

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.

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.

We built a model of knowledges diffusion in networks. The particularity is that knowledges to be passed on are cumulative : rely on previous acquisition of specific knowledges of a lower level to be passed from an agent to another. We test for different types of networks, different selection rules for the knowledge to be passed and the possibility to introduce a turnover among agents.
We concentrate on the learning speed and the convergence level of the learning process.

An agent-based model of saving and dissaving behaviour under quasi-hyperbolic (β–δ) discounting. Building on the individual decision problem of Cao and Werning (2018), the model embeds present-biased agents in a Watts–Strogatz small-world network and adds three configurable mechanisms of social influence — information diffusion, peer comparison, and social-norm conformity — across five heterogeneous behavioural profiles (Planners, Moderates, Procrastinators, Inverse Procrastinators, and Impulsive agents).
Each profile’s saving policy is approximated by value-function iteration over a discretised wealth grid; the solved policies are cached and applied as agents interact over their network neighbourhoods. The model tests whether each social mechanism can alter the saving and wealth trajectories that present-biased agents would otherwise follow in isolation, and characterises the direction and size of each effect on median wealth, wealth inequality (Gini), and the incidence of severely depleted agents.
The deposit includes the core model (Model.py), an analysis and visualisation pipeline (analyze_results.py), a standalone ODD description (ODD.md), and pinned dependencies.

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.

This model explores the coupled dynamics of social norm diffusion and finite resource depletion. Extending the “Affordance Landscape” framework by Kaaronen & Strelkovskii (2020), this simulation investigates how resource scarcity and regeneration rates influence the adoption of pro-environmental behaviours.

The model addresses the gap by linking behavioural norms to a depleting common-pool resource. It tests whether sustainable norms can diffuse rapidly enough to prevent ecological collapse and identifies “tipping points” where resource scarcity acts as a driver for behavioural change.

This Agent-Based Model is designed to simulate how similarity-based partner selection (homophily) shapes the formation of co-offending networks and the diffusion of skills within those networks. Its purpose is to isolate and test the effects of offenders’ preference for similar partners on network structure and information flow, under controlled conditions.

In the model, offenders are represented as agents with an individual attribute and a set of skills. At each time step, agents attempt to select partners based on similarity preference. When two agents mutually select each other, they commit a co-offense, forming a tie and exchanging a skill. The model tracks the evolution of network properties (e.g., density, clustering, and tie strength) as well as the spread of skills over time.

This simple and theoretical model does not aim to produce precise empirical predictions but rather to generate insights and test hypotheses about the trade-off between partnership stability and information diffusion. It provides a flexible framework for exploring how changes in partner selection preferences may lead to differences in criminal network dynamics. Although the model was developed to simulate offenders’ interactions, in principle, it could be applied to other social processes involving social learning and skills exchange.

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The agent-based simulation of innovation diffusion is based on the idea of the Bass model (1969).

The adoption of an agent is driven two parameters: its innovativess p and its prospensity to conform with others. The model is designed for a computational experiment building up on the following four model variations:

(i) the agent population it fully connected and all agents share the same parameter values for p and q
(ii) the agent population it fully connected and agents are heterogeneous, i.e. individual parameter values are drawn from a normal distribution
(iii) the agents population is embeded in a social network and all agents share the same parameter values for p and q
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The code and data in this repository are associated with the article titled:

Leal, Diego F. 2025. “Locating Cultural Holes Brokers in Diffusion Dynamics across Bright Symbolic Boundaries.” Sociological Methods & Research OnlineFirst https://doi.org/10.1177/00491241251322517

The NetLogo code (version 6.4.0) is designed to be a standalone piece of code although it uses the ‘nw’ and ‘matrix’ extensions that come integrated with NetLogo 6.4.0. The code was ran on a Windows 10 x 64 machine.