Title: The Role of Generalist and Specialist in Collaboration: An Agent-Based Simulation Model
We often solve problems with insufficient knowledge. The preference between knowledge depth and breadth makes solvers generalists or specialists. Despite the well-recognized performance differences between generalists and specialists, we know little about the reason for these differences, namely their behavioral differences. This is due to a long-standing ignorance of the distinction between knowledge depth and breadth, assuming that the depth of knowledge simply means an accumulative knowledge outcome in that field, as opposed to knowledge scope, which refers to search scope and usable elements. In this paper, we model generalists and specialists using knowledge depth and breadth, which determine their search behavior on multi-state NK fitness landscapes. We propose that there are more than superficial differences between generalists and specialists that they have different knowledge structures. Generalists and specialists have different problem-solving capabilities, and they will contribute to solutions differently if they collaborate. Specialists can perceive finer granularity and provide local refinement, while generalists can perceive more interdependencies and provide broad repositioning. In addition, specialists excel at independent searches, but generalists benefit more from collaborative searches. These insights advance our understanding of how to coordinate generalists and specialists.