We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. This modification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrod model, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the differences in behavior of the two models over time, but also provides a more comprehensive view into the behavior of Axelrod’s original model. The quantitative metrics introduced in this paper have broad potential applicability across a large variety of agent-based cultural dissemination models.

This model implements a coupled opinion-mobility agent-based framework in NetLogo, extending Attraction-Repulsion Model (ARM) dynamics with endogenous migration in continuous 2D space.

Each agent has an opinion s in [0,1] and a spatial position (x,y). Agents interact locally within an interaction radius, with exposure-controlled interaction probability. Opinion updates follow ARM rules: attraction for small opinion distance and repulsion for large distance (tolerance threshold T). After social interaction, agents move according to a social-force mechanism that balances attraction to similar neighbors and avoidance of dissimilar neighbors, controlled by orientation bias (approaching goods vs leaving bads). The model also includes an optional exposure-mobility coupling setting.

Main outputs include polarization (P), spatial assortativity (Moran’s I), mixed-neighbor fraction (f_mix), and good-component count (N_g). The model is designed to study phase behavior of polarization and segregation under mobility and tolerance heterogeneity.

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A simple model illustrating RA Fisher’s (1930) reconciliation of Darwinian selection with particulate genetic contributions.

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 Python module contain a function that is able to test the ergodicity of a given agent based model. It is sufficient to produce one long time series and many smaller time series. The function uses

Demographic microsimulation model used in speed tests against LIAM 2.

This model illustrates a positive ‘growth’ feedback loop in which the areal extent of an entity increases through time.

This is the same model as used in the article ‘Modelling Society’s Evolutionary Forces’ except the Fertility graph has been corrected. The Fertility graph was not used in the published article.

Toolkit to specify demographic multistate model with a behavioural element linking intentions to behaviour

PoliSEA represents a continuous policy process cycle, integrated with the dynamics of a fishery social-ecological system. The policy process in the model is represented by interactions between policymakers and interest groups and subsequent voting during which policymaker decide to increase or decrease the fishing quota for the next season. Policymakers’ positions can be influenced by lobbying of interest groups or interest group coalitions. The quota adopted through the policy process determines the amount of fish that can be harvested from the fish population during the season.