Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Abstract We propose a model of learning when experimentation is possible, but unawareness and ambiguity matter. In this model, complete lack of information regarding the underlying data generating process is expressed as a (maximal) family of priors. These priors yield posterior inferences that become more precise as more information becomes available. As information accumulates, however, the individual’s level of awareness as encoded in the state space may expand. Such newly learned states are initially seen as ambiguous, but as evidence accumulates there is a gradual reduction of ambiguity.