Division of labor involves task division and task allocation. An extremely important consequence of task division and allocation is the creation of interdependence between agents. In fact, division of labor can be seen as a process that converts interdependence between tasks into interdependence between agents. While there are many ways in which the task structure can be chunked and divided among agents, two important heuristic approaches involve division of labor by activity vs. object. I show that a choice between these two forms of division of labor only arises when the task structure is non-decomposable, but the product itself is decomposable. When the choice arises, a key criterion for selection between activity vs. object-based division of labor is the gain from specialization relative to the gain from customization.
Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles
