(89b) Gambling on Innovation with Learning

To maximize your median return (i) and minimize your probability of “going bust” in innovation, you need to invest the proper amount in your innovation projects. A previous article (IECR, 60, 7689, 2021) showed that the Kelly gambling (or investing) strategy helps to make the optimal decisions. To use the Kelly strategy, you must know or estimate the anticipated win ratio (b) of an innovation project and its estimated probability (p) of success. But both b and p will likely increase when you bet a fraction (f) of your innovation budget on a project, due to learning that increases both b and p (IECR, 61, 18457, 2022). This article analyzes how the optimal f changes when learning takes place. I use simple linear approximations p = p0 + πf and b = b0 + 𝛽f. As π and/or 𝛽 increases, the optimal Kelly Criterion value fKC increases, often significantly, calling for a higher resource investment, and also giving a dramatic increase in growth rate (i). It is proposed that organizations measure π, 𝛽, and k, where k is the rate of attempts made per time, and that a primary purpose of an innovating organization is to increase them quickly, both to maximize ROI, and to minimize the probability of “going bust”. This talk gives an important “algorithm for innovation”.