June 23 - Chance Models

Chance Models - Chengwei Liu

What is a Chance Model? - Reading

Chance models—mechanisms that explain empirical regularities through unsystematic variance—have a long tradition in the sciences, such as evolutionary biology, ecology and chemistry. But chance models have been historically marginalized in management scholarship, relative to an agentic worldview about the role of managers and organizations.

One possible reason may be that chance models can seem to give into defeatism, as they do not appear to provide causal explanations or pragmatic implications. If performance differences are mainly a matter of luck, what should we tell our MBA students—that they should be lucky?

I argue that, on the contrary, a chance model actually produces a causal explanation when its theoretical mechanism produces predictions that approximate the empirical regularity for which it aims to account. Chance models also help management scholars and practitioners experience history more richly in order to avoid being fooled by randomness.

In this course, we will go through (1) how to use chance models to explain empirical regularities; (2) seven theoretical mechanisms of chance models; (3) the canonical chance models in management and organization sciences.

Most ideas covered in this course can be found in two of my published papers and my book. The videos below are complementary as they provide quick summaries as well as additional examples and applications.

References

Denrell, Jerker, Christina Fang, and Chengwei Liu. 2015. “Chance Explanations in the Management Sciences.” Organization Science 26(3):923–40. (Link)
Liu, C. (2020). Luck: A Key Idea for Business and Society. Oxford, UK: Routledge. (Link)
Liu, Chengwei and Chia-Jung Tsay. 2021. The Variance of Variance. In print at Carnegie goes to California: Advancing and Celebrating the Work of James G. March (Research in the Sociology of Organizations), Emerald Publishing Limited. (Link)