“A Stochastic Steepest Descent Approach to Entrepreneurial Opportunity Search” by Mr. Zhengli Wang
Mr. Zhengli Wang
Ph.D. Candidate in Operations, Information and Technology
Graduate School of Business
Entrepreneurs engage in a simultaneous process of search and hypothesis testing. They search for a set of strategic and operational choices that will maximize their venture’s profits and they test the hypothesis that these profits exceed a minimum threshold of viability. We formulate a problem of the entrepreneur opportunity search process, where in each time period, the entrepreneur can stop and conclude, or choose an experiment from a set of strategic and operational options, implement it and observe the resulting profit. Using tools from machine learning to model the search process and tools from sequential hypothesis testing to model the testing procedure, we analytically characterize the optimal testing strategy for the resulting problem. We demonstrate that in certain scenarios the optimal testing strategy from our framework and that predicted by the Lean Startup Theory are consistent, while in others they disagree.