The purpose of the model is to study the dynamical relationship between individual needs and group performance when focusing on self-organizing task allocation. For this, we develop a model that formalizes Deci & Ryan’s self-determination theory (SDT) theory into an ABM creating a framework to study the social dynamics that pertain to the mutual relations between the individual and group level of team performance. Specifically, it aims to answer how the three individual motivations of autonomy, competence, and belonging affect team performance.

This model demonstrates how different psychological mechanisms and network structures generate various patterns of cultural dynamics including cultural diversity, polarization, and majority dominance, as explored by Jung, Bramson, Crano, Page, and Miller (2021). It focuses particularly on the psychological mechanisms of indirect minority influence, a concept introduced by Serge Moscovici (1976, 1980)’s genetic model of social influence, and validates how such influence can lead to social change.

The current rate of production and consumption of meat poses a problem both to peoples’ health and to the environment. This work aims to develop a simulation of peoples’ meat consumption behaviour in Britain using agent-based modelling. The agents represent individual consumers. The key variables that characterise agents include sex, age, monthly income, perception of the living cost, and concerns about the impact of meat on the environment, health, and animal welfare. A process of peer influence is modelled with respect to the agents’ concerns. Influence spreads across two eating networks (i.e. co-workers and household members) depending on the time of day, day of the week, and agents’ employment status. Data from a representative sample of British consumers is used to empirically ground the model. Different experiments are run simulating interventions of application of social marketing campaigns and a rise in price of meat. The main outcome is the average weekly consumption of meat per consumer. A secondary outcome is the likelihood of eating meat.

The HUMan impact on LANDscapes (HUMLAND) model has been developed to track and quantify the intensity of different impacts on landscapes at the continental level. This agent-based model focuses on determining the most influential factors in the transformation of interglacial vegetation with a specific emphasis on burning organized by hunter-gatherers. HUMLAND integrates various spatial datasets as input and target for the agent-based model results. Additionally, the simulation incorporates recently obtained continental-scale estimations of fire return intervals and the speed of vegetation regrowth. The obtained results include maps of possible scenarios of modified landscapes in the past and quantification of the impact of each agent, including climate, humans, megafauna, and natural fires.

HUMLAND Fire-in-the-Hole is a conceptual agent-based model (ABM) designed to explore the ecological and behavioral consequences of fire-driven hunting strategies employed by hunter-gatherers, specifically Neanderthals, during the Last Interglacial period around the Neumark-Nord (Germany) archaeological site.

This model builds on and specializes the HUMLAND 1.0.0 model (Nikulina et al. 2024), integrating anthropogenic fires, elephant group behavior, and landscape response to simulate interactions between humans, megafauna, and vegetation over time.

This model simulates a foraging system based on Middle Stone Age plant and shellfish foraging in South Africa.

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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Dawkins’ Weasel is a NetLogo model that illustrates the principle of evolution by natural selection. It is inspired by a thought experiment presented by Richard Dawkins in his book The Blind Watchmaker (1996).

This model replicates the Axelrod prisoner’s dilemma tournaments. The model takes as input a file of strategies and pits them against each other to see who achieves the best payoff in the end. Change the payoff structure to see how it changes the tournament outcome!

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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