Multi-agent modelling and analysis of the knowledge learning of a human-machine hybrid intelligent organization with human-machine trust

Abstract

Machine learning (ML) technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, the general distrust among humans towards knowledge derived from machine learning has hindered effective knowledge exchange between humans and machines, thereby compromising the efficiency 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. The simulation showed that whether human-machine trust has a positive effect on knowledge level depends on the initial input and the magnitude of the effect depends on the human learning propensity (exploration and exploitation). When humans reconfigure machine learning excessively, whether human-machine trust has a positive effect on the knowledge level depends on human learning propensity (exploration and exploitation). Maintaining appropriate human-machine trust in turbulent environments assists humans in integrating diverse knowledge to meet changing knowledge needs. Our study extends the human-machine hybrid intelligence organizational learning model by modeling human-machine trust. It will assist managers in effectively designing the most economical level of human-machine trust, thereby enhancing the efficiency of human-machine collaboration in human-machine hybrid intelligent organization.