Making biased but better predictions: The tradeoffs strategists face when they learn and use heuristics

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • Max Planck Institute for Mathematics in the Sciences


The heuristics strategists use to make predictions about key decision variables are often learned from only a small sample of observations, which leads to a risk of inappropriate generalization when strategists misjudge regularities. Building on the statistical learning literature we show how strategists can mitigate this risk. Strategies to learn heuristics that accept a bias, that is, a systematic deviation of predictions from actual outcomes can outperform unbiased strategies because they can reduce the variance component of prediction error: The degree to which random fluctuations in observational data are inappropriately generalized. We demonstrate how strategists who are aware of the trade-off between bias and variance can learn heuristics more effectively if they are also aware of the relevant characteristics of their learning environment. We discuss the implications of our results for our understanding of heuristics, (dynamic) capabilities and managerial cognitive capabilities, and we outline opportunities for empirical work.


Original languageEnglish
JournalStrategic Organization
Early online date25 Sep 2019
Publication statusE-pub ahead of print - 25 Sep 2019
MoE publication typeA1 Journal article-refereed

ID: 35633809