Role Assignment Adaptation: An Intentional Forgetting Approach

Abstract

In organizations the distribution of tasks is a rising challenge in complex and dynamic environments. By structuring responsibilities and expectations for task processing in roles, organizations provide a transparent approach for collaboration. However, if tasks are being generated unexpectedly, actors who enact multiple roles might be overloaded in dynamic environments. By focusing on relevant information in terms of an intentional forgetting mechanism, actors could overcome these overload situations. Therefore, we provide an agent-based simulation to model and analyze effects of intentional forgetting by adapting role assignments in dynamic environments. The agent architecture utilizes separated revision functions to control an agent’s perception and belief acquisition to focus on relevant information. The model is tested using a case-study in a simulated emergency response scenario. The simulation results show that adapting role assignments at runtime improves team performance significantly.