This paper examines how parallel search and shared knowledge generation affect individual search effectiveness in multi-actor environments. While individuals aim to improve their own solutions and achieve global breakthroughs, prior research suggests that learning from others’ search consistently enhances individual effectiveness. We challenge this perspective. Using data from the “speedrunning” community—where players strive to complete video games as fast as possible—we show that other actors’ search activity and knowledge sharing can simultaneously foster individual improvements and hinder the discovery of globally superior solutions. Greater search activity by other actors saturates the solution landscape and intensifies competition, reducing the marginal benefits of individual learning-by-doing for individual improvements and diminishing the likelihood of global breakthroughs. Yet, by intensifying their own search efforts, individuals can mitigate these negative effects at the global level. While search by other actors in the environment broadens one’s awareness of the best existing solutions, it paradoxically complicates the path to surpassing them, altering the interplay between learning-by-doing, vicarious learning, and competitive success.
