  Widely considered a cornerstone of human morality, trust shapes many aspects
of human social interactions. In this work, we present a theoretical analysis
of the $\textit{trust game}$, the canonical task for studying trust in
behavioral and brain sciences, along with simulation results supporting our
analysis. Specifically, leveraging reinforcement learning (RL) to train our AI
agents, we systematically investigate learning trust under various
parameterizations of this task. Our theoretical analysis, corroborated by the
simulations results presented, provides a mathematical basis for the emergence
of trust in the trust game.
