Lake-rich landscapes operate as socio-ecological systems (SESs), with complex interactions between humans and fish populations which present a variety of unique problems from the perspective of recreational fishery management. Management and regulation are moving from one-size-fits-all (OSFA) to aggregated management, where similar lakes share the same policy (Ostrom 2009). However, the criteria for effectively sorting and sizing aggregated policies have not been fully considered. To explore the differences in utility of small-scale and OSFA management policies, an agent-based model was implemented to compare the performance of different aggregations at increasingly variable angling pressure and intrinsic growth rates. Instead of simulating policies as a static submodel, agents make flexible, reactive policy decisions. Along with simple equations, the model accurately reflects patterns observed in lake-rich landscapes. Simulations suggest individual-level management consistently performs better than all aggregation setups as intrinsic growth rates vary. However, aggregates and individual-level management unexpectedly reached optimal levels of performance at different variabilities of angling mortality. Although the results may be influenced by artifacts within the model, these simulations present a novel way to approach modeling recreational fisheries policy.