Abstract: The meta-inductive justification of induction is usually regarded as a social learning strategy. But, pre-theoretically, induction can be justified even for an isolated thinker, incapable of, or unwilling to engage in, any social interactions. This paper presents reinforcement learning (RL) as a natural meta-inductive strategy for such isolated thinkers. The meta-inductive reinforcement learner learns to choose an optimal prediction method among the available methods by applying trial and error learning. We use computer simulations to show that, under plausible assumptions, trial and error learning indeed leads the thinker to favor induction over non-inductive predictive methods. Under further plausible assumptions, the meta-inductive thinker itself is even seen to outperform all object-level methods. The results are shown to hold true in both stationary and non-stationary environments.