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.

大多数基于地理代理的模型都是通过定制的决策算法来模拟代理的。这使得很难评估哪些结果是通用的，哪些结果取决于算法的细节。我们提出了一套通用算法，适用于任何基于代理的模型，可以从一组替代方案中进行反复选择。我们以相同的基于渔业代理的模型展示每种产品，并对它们在各种情况下的表现进行排名。尽管复杂的算法往往会表现更好，但复杂程度过低则会降低性能。此外，尽管某些算法在所有情况下均表现良好，但其他算法仅在特定情况下才是最佳算法。因此，即使对于简单的通用算法，也不可能产生单一，明确的性能等级。然后，我们提倡一种启发式动物园方法，其中在同一模型中实现多种算法。这使我们能够确定其最佳算法，并测试其对决策组件错误指定的敏感性。