Discrete choice modeling of fishing location tries to resolve simultaneously behavioral responses to information, what this information is, and the way that the information is gathered and processed. Without observing the process itself, the modeler really only sees choices and revenue (or catch) histories. We cannot separate how these histories are combined into measures of profitability and how these measures affect choice. This paper discussed two fundamentally different attempts to resolve questions about information. The author concludes that structural attempts are essentially intractable in every setting, and reduced-form methods are by nature ad hoc. Nevertheless, some hope lies in reduced-form methods. A conclusion of the first section is that even ad hoc approaches to information processing should account for sampling variance in addition to variance of the underlying stochastic process. Of the methods discussed, the Bayesian approach with a realistic probability model seems most compelling but is also the most difficult to implement. In contrast, it is much less clear how best to model the decay of information.