Machine learning technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, it remains unclear how human-machine trust, which is an important factor that influences human-machine knowledge exchange, affects the effectiveness of human-machine hybrid intelligent organizational learning. To explore this issue, we used multi-agent simulation to construct a knowledge learning model of a human-machine hybrid intelligent organization with human-machine trust.

This model introduces individual bias to the model of exploration and exploitation, simulates knowledge diffusion within organizations, aiming to investigate the effect of individual bias and other related factors on organizational objectivity.

This agent-based model represents a stylized inter-organizational innovation network where firms collaborate with each other in order to generate novel organizational knowledge.

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.

REHAB has been designed as an ice-breaker in courses dealing with ecosystem management and participatory modelling. It helps introducing the two main tools used by the Companion Modelling approach, namely role-playing games and agent-based models.