Goldman (2001) asks how novices can trust putative experts when background knowledge is scarce. We develop a reinforcement-learning model, adapted from Barrett, Skyrms, and Mohseni (2019), in which trust arises from experience rather than prior expertise labels. Agents incrementally weight peers who outperform them. Using a large dataset of human probability judgments as inputs, we simulate communities that learn whom to defer to. Both a strictly individual-learning variant and a reputation-sharing variant yield performance-sensitive deference, the latter accelerating convergence. Our results offer an empirically grounded account of how communities identify and trust experts without blind deference.