Performance and robustness of dynamic scheduling algorithms are evaluated in the presence of errors in the tasks’ resource requirements. Previous work found that incorporating task completion events from the actual distributed system into the algorithms’ model of the system was crucial for achieving robustness. In the present paper, various degrees of feedback, rather than simply all-or-none, are evaluated using the same simulated studies as in previous work and a proposed strategy for biasing model tasks’ resource requirement information is proposed in order to counteract the most egregious effects of model error on performance.