The Effectiveness of Simple Decision Heuristics: Forecasting Commercial Success for Early-Stage Ventures

Authors:
Thomas stebro;Samir Elhedhli
Affiliations:
Joseph L. Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, Ontario M5S 3E6, Canada;Department of Management Sciences, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
Venue:
Management Science
Year:
2006

Citing 3
Cited 2

Maximizing the predictive value of production rules

Artificial Intelligence
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Simple Inference Heuristics versus Complex Decision Machines

Minds and Machines

Predictive rule discovery from electronic health records

Proceedings of the 1st ACM International Health Informatics Symposium
Psychological Heuristics for Making Inferences: Definition, Performance, and the Emerging Theory and Practice

Decision Analysis

Quantified Score

Hi-index	0.01

Visualization

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 and 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 91.8 forecasting accuracy of the experts correct forecasts. 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 well in a natural and very difficult decision-making context.