Maintaining Diversity in Parallel Problem Solving: The Influence of Network Structure and Learning Strategy

Abstract

Recent research on maintaining diversity in parallel problem solving takes into consideration only network structure, without considering the agents’ learning strategies. In this paper, we use a simulation study to extend March’s classic model by using locomotion and assessment as agents’ problem-solving strategies. First, we present a simulation framework that consists of external environment, communication networks, and agents’ learning strategies. Second, based on the framework, we develop March’s model to depict external environment. Third, we introduce four archetypical networks: a regular network, a small-world network, a preferentially attached network, and a totally connected network as agents’ communication structure. Finally, we design three experiments to explore the performance implication of locomotors and assessors under different networks. Results suggest that network structure affects performance more than learning strategy. The more efficient the network is at diffusing knowledge, the better the performance in the short run but the worse in the long run. Locomotors can help keep diversity; a high proportion of locomotors’ team has a better final performance but need more equilibrium time. Furthermore, moderate composition among locomotors and assessors increases costly interaction uncertainty. We discuss the findings’ implications for the regulatory mode and problem-solving literature.