We reconstruct Cohen, March and Olsen’s Garbage Can model of organizational choice as an agent-based model. We add another means for avoiding making decisions: buck-passing difficult problems to colleagues.

This model, realized on the NetLogo platform, compares utility levels at home and abroad to simulate agents’ migration and their eventual return. Our model is based on two fundamental individual features, i.e. risk aversion and initial expectation, which characterize the dynamics of different agents according to the evolution of their social contacts.

Model of shifting cultivation. All parameters can be controlled by the user or the model can be run in adaptive mode, in which agents innovate and select parameters.

The various technologies used inside a Dutch greenhouse interact in combination with an external climate, resulting in an emergent internal climate, which contributes to the final productivity of the greenhouse. This model examines how differing technology development styles affects the overall ability of a community of growers to approach the theoretical maximum yield.

This is a model of innovation implementation inside an organization. It characterizes an innovation as a set of distributed and technically interdependent tasks performed by a number of different and socially interconnected frontline workers.

This model expands approaches from social practice theories and is used to investigate the ability of the underlying conceptual model to explain the emergence of social practices, defined as routine behaviour that is similar amoung peers.

We build a stylized model of a network of business angel investors and start-up entrepreneurs. Decisions are based on trust as a decision making tool under true uncertainty.

MoPAgrIB model simulates the movement of cultivated patches in a savannah vegetation mosaic ; how they move and relocate through the landscape, depending on farming practices, population growth, social rules and vegetation growth.

Emulation is one of the simplest and most common mechanisms of social interaction. In this paper we introduce a descriptive computational model that attempts to capture the underlying dynamics of social processes led by emulation.

This model uses ’satisficing’ as a model for farmers’ decision making to learn about influences of alternative decision-making models on simulation results and to exemplify a way to transform a rather theoretical concept into a feasible decision-making model for agent-based farming models.