Overcoming the Lumberjack Effect Through Adaptive Autonomy

Abstract

Research shows that there are a variety of performance advantages to increasing the degrees of autonomy that human-supporting autonomous systems possess, including organizational productivity and the reduction of human workload. Yet, not all the consequences of increasing autonomy are positive. One such negative consequence is the risk of human operators becoming more incapable of responding to system failures as autonomy levels increase, a phenomenon known as the Lumberjack Effect. This paper proposes a conceptual model for using adaptive autonomy as a means to avoid the risks of this phenomenon while retaining the advantages of higher autonomy levels in earlier stages of an organization’s work cycle. We apply our model to two research-based scenarios and discuss future research necessary to validate the model. This model provides the Human Factors community with a possible solution to the debate over the risks of designing human-supporting autonomous systems with higher degrees of autonomy.