(searched for: doi:10.1287/stsc.2021.0132)
Journal of Physics: Conference Series, Volume 2113; https://doi.org/10.1088/1742-6596/2113/1/012034
Imitation is ubiquitous, yet what self-regulation orientations’ role played in imitation strategies is poorly understood, which is particularly challenging in dynamic and uncertain environments. According to regulatory mode theory, we model two imitation strategies: assessment and locomotion. Assessment pays more attention on comparation among different alternatives, they repeatedly measure, evaluate, and compare desired means and try to find out the ‘best’ one. Contrariwise locomotion refers to ‘keep moving’, once choosing one alternative, they change some choices and learn from the resulting performance feedback. Using a computational model, we explore the performance implications of dynamic environments for these two imitation strategies. Consequently, when environment is stable, assessment is more effective in maintaining the lead, whereas locomotion prevails as environmental changes become more frequent and substantial. We contribute to the literatures on strategy, imitation, and NK studies.