Market-Aware Proactive Skill Posting

Abstract

Referral networks consist of a network of experts, human or automated agent, with differential expertise across topics and can redirect tasks to appropriate colleagues based on their topic-conditioned skills. Proactive skill posting is a setting in referral networks, where agents are allowed a one-time local-network-advertisement of a subset of their skills. Heretofore, while advertising expertise, experts only considered their own skills and reported their strongest skills. However, in practice, tasks can have varying difficulty levels and reporting skills that are uncommon or rare may give an expert relative advantage over others, and the network as a whole better ability to solve problems. This work introduces market-aware proactive skill posting where experts report a subset of their skills that give them competitive advantages over their peers. Our proposed algorithm in this new setting, proactive-DIEL$_{\varDelta }$, outperforms the previous state-of-the-art, proactive-DIEL$_t$ during the early learning phase, while retaining important properties such as tolerance to noisy self-skill estimates, and robustness to evolving networks and strategic lying.