The Effectiveness of Simple Decision Heuristics: A Case Study of Experts' Forecasts of the Commercial Success of Early-Stage Ventures

Abstract

We investigate the decision heuristics used by experts to forecast that early-stage ventures are subsequently commercialized. Experts evaluate 37 project characteristics and subjectively combine data on all cues by examining both critical flaws as well as positive factors to arrive at a forecast. A conjunctive model is used to describe their process which sums good and bad cue counts separately. This model achieves a 94.1% classification accuracy of the experts' correct forecasts of 561 projects. The model correctly predicts 86.0% of outcomes in out-of-sample, out-of-time tests. Results indicate that reasonably simple decision heuristics can perform very well in a natural and very difficult decision-making context.