The Multilevel Group Selection I (MGS I) model simulates a population of contributing and non-contributing agents, competing on a social landscape for higher-value spots in an effort to withstand some selection pressure. It may be useful to both scientists and students in hypothesis testing, theory development, or more generally in understanding multilevel group selection.

this agent-based model explores the dynamics of volunteer participation in urban community gardens, by combining behavioral theory and institutional theory

We model the epistemic dynamics preceding political uprising. Before deciding whether to start protests, agents need to estimate the amount of discontent with the regime. This model simulates the dynamics of group knowledge about general discontent.

Agents are linked in a social-network and make decisions on which of 2 types of behavior to adopt. We explore consequences of different information feedback and providing targeted feedback to individuals.

The model simulates seven agents engaging in collective action and inter-network social learning. The objective of the model is to demonstrate how mental models of agents can co-evolve through a complex relationship among factors influencing decision-making, such as access to knowledge and personal- and group-level constraints.

We provide an agent-based model of collective action, informed by Granovetter (1978) and its replication model by Siegel (2009). We use the model to examine the role of ICTs in collective action under different cultural and political contexts.

The Groundwater Commons Game synthesises and extends existing work on human cooperation and collective action, to elucidate possible determinants and pathways to regulatory compliance in groundwater systems globally.