Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We provide a model of boundedly rational, multidimensional learning and characterize when beliefs will converge to the truth. Agents maintain beliefs as marginal probabilities instead of joint probabilities, and agents' information is of lower dimension than the model. As a result, for some observations, agents may face an identification problem affecting the role of data in inference. Beliefs converge to the truth when these observations are rare, but beliefs diverge when observations presenting an identification problem are frequent. Robustly, two agents with differing priors who observe identical, unambiguous information may disagree forever, with stronger disagreement the more information received.