Although more valid predictions are made when information is combined algorithmically (mechanical prediction) than in the decision-maker’s mind (holistic prediction), decision makers rarely use algorithms in practice. One main reason is that decision-makers’ autonomy is restricted when algorithms are used to combine information. Unfortunately, affording decision makers greater autonomy in information combination decreases predictive validity compared to consistent algorithm use, creating an “autonomy-validity dilemma”. We hypothesized that two hybrid approaches to decision making - clinical and mechanical synthesis - should retain decision-makers’ autonomy while increasing predictive validity compared to pure holistic prediction. In clinical synthesis the decision maker can adjust an algorithms prediction, holistically. In mechanical synthesis the decision maker forms a holistic prediction that is weighted and subsequently combined with all other available information, algorithmically. In Study 1 (N = 261), mechanical and clinical synthesis resulted in higher predictive validity than holistic prediction, but user perceptions on these procedures were mixed. In Study 2 (N = 610), mechanical and clinical synthesis again resulted in much higher predictive validity than holistic prediction, and these procedures were perceived much more positively than the strict use of a prescribed algorithm. We recommend decision makers use mechanical synthesis for the most optimal balance of autonomy and predictive validity within decision making.

Jacob Matić is a PhD Candidate in the Work and Organizational Psychology Department at the VU Amsterdam.