The Impact of Increasing Returns on Knowledge and Big Data: From Adam Smith and Allyn Young to the Age of Machine Learning and Digital Platforms

Abstract

Allyn Young’s concept of increasing returns, not to be confounded with static, equilibrium constructs of economies of scale and increasing returns to scale, is applied in this article to analyze how and why increasing returns arise in the production (generation) and use (application) of knowledge and of big data, thereby driving economic growth and progress. Knowledge is chosen as our focus because it is ‘our most powerful engine of production’ and big data is included to make the analysis more complete and up-to-date. We analyze four mechanisms or sources of increasing returns in the production of knowledge, and four in the use of knowledge. Turning to big data, increasing returns in the use thereof are examined in two spheres: the dominance resulting from the selfreinforcing functioning of digital platforms and machine learning through gigantic amounts of training data. Concluding remarks concern some key differences between knowledge and big data, some policy implications, and some of the social negative impacts from the ways in which big data is being used.