Abstract:
How the Feedback of Audiences Advantages Fast Learners: Evidence from Canadian Software Firms from 2004 to 2013 David Maslach, Florida State University Chengwei Liu, University of Warwick Rod McNaughton, University of Auckland Slow learning – low sensitivity to recent performance – has been argued to be essential for adaptation in uncertain environments because learners do not converge to local maxima. Using a simulation model, we demonstrate that fast learning can outperform slow learning when the role of audience feedback is incorporated. A differential sampling process determines how audiences form expectations and react to performance deviations, and potentially reward fast learners for unreliability. We empirically examine our argument using the Google Search data regarding the Canadian software firms from 2004 to 2013. The results support that fast learners tend to attract more (less) attention when outperforming (underperforming) the expectations than slow learners. More generally, we present a sampling account for why fast learning is a sensible strategy, even with uncertain outcomes.
