Interpersonal trust modelling through multi-agent Reinforcement Learning

Many existing approaches to model and compute trust in a quantitative way rely on ranking, rating or assessments of agents by other agents. Even though reputation is related with trust, it does not capture all its characteristics. In parallel, many works in neuroscience shows evidence about interpersonal trust being an associative learning process encoded in the human brain. Inspired by other subjects such as Cognitive Processing/Dopamine, where Reinforcement Learning algorithms have served to model those phenomena, we propose a model for trust dynamics based on a multi-agent RL algorithm. We corroborate some trust concepts developed in social sciences within a quantitative framework. We do also propose and assess some metrics for a better understanding about the relation between the trust behaviour and the performance of the agents. Finally, we show that Trust, as described by our proposal, can serve to accelerate learning.