The purpose of the model is to generate the spatio-temporal distribution of bicycle traffic flows at a regional scale level. Disaggregated results are computed for each network segment with the minute time step. The human decision-making is governed by probabilistic rules derived from the mobility survey.

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.

The purpose of this model is to explore the effects of different power structures on a cross-functional team’s prosocial decision making. Are certain power distributions more conducive to the team making prosocial decisions?

Demand planning requires processing of distributed information. In this process, individuals, their properties and interactions play a crucial role. This model is a computational testbed to investigate these aspects with respect to forecast accuracy.

The Carington model is designed to provide insights into the factors affecting informal health care for older adults. It encompasses older adults, caregivers, and factors affecting informal health care. The Carington model includes no submodels.

This is a simulation model of an intelligent agent that has the objective to learn sustainable management of a renewable resource, such as a fish stock.

The model represents an archetypical fishery in a co-evolutionary social-ecological environment, capturing different dimensions of trust between fishers and fish buyers for the establishment and persistence of self-governance arrangements.

The CHIME ABM explores information distribution networks and agents’ protective decision making in the context of hurricane landfall.

The objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.

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