In this work, we investigate how the order in which tasks are considered to be acted upon by probabilistic agents with time-variant response thresholds within a fully decentralized multiagent system may affect task allocation, task fulfillment levels, specialization tendencies, and system robustness. The tested ordering schemas are: (1) ascending subjective action threshold, (2) descending objective task stimulus, (3) descending action probability P as defined by Theraulaz, Bonabeau, and Deneubourg (1998) which combines both subjective threshold and objective stimulus, and (4) a random ordering to serve as a baseline for comparison. As the behavior of real-world systems tends to stem from complex interactions of multiple system aspects, we expand our analysis by further breaking down each of these ordering approaches into positive vs. negative reinforcement under different learning and forgetting rates for updating agent response thresholds.