Decoding trust: A reinforcement learning perspective

Behavioral experiments on the trust game have shown that trust and trustworthiness are commonly seen among human beings, contradicting the prediction by assuming Homo economicus in orthodox Economics. This means some mechanism must be at work that favors their emergence. Most previous explanations, however, need to resort to some exogenous factors based upon imitative learning, a simple version of social learning. Here, we turn to the paradigm of reinforcement learning, where individuals revise their strategies by evaluating the long-term return through accumulated experience. Specifically, we investigate the trust game with the Q-learning algorithm, where each participant is associated with two evolving Q-tables that guide one’s decision-making as trustor and trustee, respectively. In the pairwise scenario, we reveal that high levels of trust and trustworthiness emerge when individuals appreciate both their historical experience and returns in the future. Mechanistically, the evolution of the Q-tables shows a crossover that resembles human psychological changes. We also provide the phase diagram for the game parameters, where the boundary analysis is conducted. These findings are robust when the scenario is extended to a latticed population. Our results thus provide a natural explanation for the emergence of trust and trustworthiness, and indicate that the long-ignored endogenous factors alone are sufficient to drive. More importantly, the proposed paradigm shows the potential to decipher many puzzles in human behaviors.