Entrepreneurs as Scientists, Bayesian Inference, and Belief Revision

We are delighted to see active conversation and debate (see also Sergeeva, Bhardwaj, & Dimov, 2022) around our paper “Entrepreneurs as Scientists: A pragmatist approach to producing value under uncertainty”, forthcoming in the Academy of Management Review (Zellweger & Zenger, 2022b). In a thoughtful and provocative comment published in Journal of Business Venturing Insights, Ehrig and Foss (2022), while embracing the emerging idea of entrepreneurs as scientists who compose and test theories (Camuffo, Cordova, Gambardella, & Spina, 2020; Ehrig & Schmidt, 2022; Felin & Zenger, 2009), offer two important critiques focused on our use of Bayesian logic and language. First, they argue that Bayesian learning fails in the presence of Knightian uncertainty or the unknown unknowns that are commonplace in entrepreneurial settings. In a nutshell, they argue that the entrepreneurial process is frequently filled with what Simon characterized as ‘surprises’ (Simon, 1989) or the arrival of data or states that are outside any composition of subjective probabilities, and therefore cannot be a basis for Bayesian updating. Second, they argue that (p. 3) “entrepreneurial theories (in the sense of Felin & Zenger, 2017) contain systems of conditional belief or assumptions” that again cannot be tested and subjected to Bayesian updating as a whole, though they acknowledge that individual assumptions or subsets of assumptions, may be testable through Bayesian inference. As their title, “Why we need normative theories of entrepreneurial learning that go beyond Bayesianism,” aptly summarizes, their message is that a normative theory in which entrepreneurs are encouraged to act like scientists needs a model of learning that goes beyond Bayesianism or at least an augmented or qualified version of Bayesianism—one that allows unsupported beliefs or priors to be discarded and replaced.