Coordinated search processes are pervasive in both organizations and product development projects. In such processes, designers with different specialties learn about their interdependent alternatives through a mutual adjustment process. In the context of a product development with several teams developing the new product's subsystems, and using reinforcement learning and agent-based simulation modeling, this study looks at the performance effects of design teams' initial mental characterizations about subsystem interactions. The focus is on two initial mental models, one in which teams over-weight their own subsystem's element interactions, and another, in which teams over-weighting interactions between subsystems. The results indicate that both initial representations have critical performance consequences for product development. Specifically, teams prioritizing their interactions of their own subsystem's elements gain short-run performance benefits as they converge to a local optimum in a short time period. Contrarily, over-weighting between-subsystem interactions leads to a tendency for teams to have long-run performance advantages.