Adaptive Situational Leadership Framework

This work proposes an adaptive leadership framework that provides intelligent decision making solutions for a leader to provide cost-effective feedback to followers with different levels of mission readiness. Useful, timely provided feedback can improve follower's readiness and performance. However, feedback provision can be costly and strain the leader, especially when the leader's resources are limited. To model adaptive feedback provision, we use instance-based learning (IBL) theory, a cognitive architecture that can account for human decision making and learning from experience in a dynamic environment. In addition, we leverage the concept of trust, to capture the dynamic performance of the follower. Trust in a follower is used as a key attribute for the leader's decision making process. To evaluate the proposed framework and the interplay between leader's trust in follower and utility from feedback provision, we devised four different feedback schemes with or without adaptive learning and with or without trust, and conducted comparative performance analysis among them. Our key findings show that in less resource constraint situations the adaptive feedback provision using IBL model and trust can significantly help increase decision utility, representing a balance between the follower's trust improvement and the leader's feedback cost. In addition, a leader's high willingness to provide feedback does not necessarily lead to high decision utility, while a follower's high learning capability is a key to maximize decision utility.