We examine how machine learning (ML) predictions of high-growth enterprises (HGEs) help a budget-constrained venture capitalist source investments for fixed size portfolio. Applying design science approach, we predict HGEs 3 years ahead and focus on decision (not statistical) errors, using an accuracy measure relevant to the decision-making context. find that when ML procedure adheres budget co...

Abstract We study the co-evolution of religion, science, and politics. first uncover, in international U.S. data, a robust negative relationship between religiosity patents per capita. The model then combines: (1) scientific discoveries that raise productivity but sometimes erode religious beliefs; (2) government allows innovations to diffuse, or blocks them; (3) institutions can invest doctrin...