(168e) When to Stop Additional Attempts in a Particular Innovation

The purpose of this article is to assess the Optimal Stopping point in innovation processes. How do we know when to stop trying new prototypes, new hypotheses, new experiments for a given innovation project? Optimal Stopping theory from probability analysis gives us some key results, and here I extend some of these to more realistic situations, finding several surprising results. It is proposed that organizations score their successive trials so that they can determine when to stop, so that they optimize their return on investment and reduce risk of loss. A practical Delphi method will be introduced for this scoring. This talk is an important example of an “algorithm for innovation”.