PRIOR KNOWLEDGE VERSUS CONSTRUCTED KNOWLEDGE: WHAT IMPACT ON LEARNING?

The aim of this paper is to model the process of learning within a social network and compare the levels of learning in two different situations: one where individuals know others' competencies as given data and interact on this basis; and one where individuals know nothing about others' competencies but rather build this knowledge over time, according to their past interactions. For this purpose, we build an agent-based model, and model these two scenarios of simulations. Results are partly studied using network analysis, and they show that in the second type of simulations agents are able to identify the most competent agents in the network and increase their competencies. Results also show that learning is easier when there is no prior knowledge of others' competencies. Otherwise, agents deal with a congestion effect that slows down the learning process.