A tutorial on mechanical decision-making for personnel and educational selection

In decision-making, it is important not only to use the correct information but also to combine information in an optimal way. There are robust research findings that a mechanical combination of information for personnel and educational selection matches or outperforms a holistic combination of information. However, practitioners and policy makers seldom use mechanical combination for decision-making. One of the important conditions for scientific results to be used in practice and to be part of policy-making is that results are easily accessible. To increase the accessibility of mechanical judgment prediction procedures, we (1) explain in detail how mechanical combination procedures work, (2) provide examples to illustrate these procedures, and (3) discuss some limitations of mechanical decision-making.