Making biased but better predictions: The trade-offs strategists face when they learn and use heuristics
研究了战略家从少量样本中学习启发式进行预测时，如何通过接受偏差来降低预测误差的方差，从而提升预测效果，并讨论了学习环境特征的重要性。
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.