Optimal cache search depends on precision of spatial memory and pilfering, but what if that knowledge is not perfect?

The problem of when an action should be abandoned because it is not worth further effort occurs in many situations. In the spatial domain, the relevant information can be quantified. Two essential pieces of information are the precision with which the target location and the probability of the target being present are known. We offer a quantitative description of optimal solutions to cache retrieval, treating it as a 2D investment problem with search cost proportional to area. We estimated the value of knowing the precision of spatial information and the precision of information regarding probability: how precisely should you estimate the precision of your knowledge? We compared the expected gain from assessing the precision of spatial knowledge and probability with the expected gain from decisions based on aggregate knowledge of the distribution of precision and probability. We found that heuristics, represented here as default search limits based on aggregate knowledge, are useful only under limited conditions.