Costly mistakes can occur when decision makers rely on intuition or learned biases to make decisions. To better understand the cognitive processes that lead to bias and develop strategies to combat it, we developed an intelligent agent using the cognitive architecture, ACT-R 7.0. The agent simulates a human participating in a decision making task designed to assess the effectiveness of bias reduction strategies. The agent's performance is compared to that of human participants completing a similar task. Similar results support the underlying cognitive theories and reveal limitations of reducing bias in human decision making. This should provide insights for designing intelligent agents that can reason about bias while supporting decision makers.