Repeated discrete choices in geographical agent based models with an application to fisheries

Most geographical agent-based models simulate agents through custom-made decision-making algorithms. This makes it difficult to assess which results are general and which are contingent on the algorithm's details. We present a set of general algorithms, applicable in any agent-based model for choosing repeatedly from a set of alternatives. We showcase each in the same fishery agent-based model and rank their performance under various scenarios. While complicated algorithms tend to perform better, too much sophistication lowers performance. Further, while some algorithms perform well under all scenarios, others are optimal only in specific circumstances. It is therefore impossible to produce a single, unequivocal performance ranking even for simple general algorithms. We advocate then a heuristic zoo approach where multiple algorithms are implemented in the same model; this allows us to identify its best algorithm and test sensitivity to misspecifications of the decision-making component.