In the bandit setting with reputation-seeking experts, the lower bound proof for WSU-UX demonstrates that it cannot achieve regret better than Ω(T 2/3). However, there are alternative incentive-compatible algorithms that may potentially achieve better regret bounds. One approach could involve exploring different update rules or mechanisms that balance exploration and exploitation more effectively. By designing algorithms that can adapt more efficiently to the changing dynamics of the environment, it may be possible to achieve lower regret bounds than WSU-UX. Additionally, incorporating insights from recent advancements in online learning and game theory could lead to the development of novel algorithms with improved performance in this setting.