Article: Brian Arthurs Technological Competition Model and its application to the digital platform economy

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W. Brian Arthur has been one of the first researchers doing work in the field of complexity economics. In recent years, he has become particularly well known for his seminal work on economic dynamics under increasing returns. The rise of the digital platform economy has brought new relevance to Arthurs work – even though its foundations originated from two decades before digital platforms gained economic importance.

As an electrical engineer and mathematician by training, W. Brian Arthur always had a strong fascination for economic development and its linkage to the evolvement of technology (Arthur, 2009). His work on economic dynamics under incrasing returns has become particularly well know in recent years. Following Arthur, increasing returns arise when the return (or utility) of a certain product or technology increases the more it is adopted. Whereas many products exhibit decreasing returns – for example because prices of input factor prices are rising due to increasing demand – this is not necessarily the case for technologies: Due to learning effects, technologies often are improved the more they are adopted, which leads to increasing returns and positive feedback effects for successful technologies. A similar dynamic can be observed in the case of digital platforms, where network effects lead to a situation in which returns increase in relation to the number of users that a platform attracts. Therefore – even though the foundation of Arthurs work originated from two decades before digital platforms gained economic importance – the rise of the digital platform economy has brought new relevance to his work.

In his article “Competing Technologies, Increasing returns and lock-in by historical events” (1989), Arthur compares the dynamics of technology competition under increasing, constant and decreasing returns with a focus on the impact of random events on adoption share outcomes. He understands random events as “those events […] outside of the ex-ante knowledge of the observer”(Arthur, 1989: 118), which might give rise to an early lead of one of the technologies. By modeling the competition between two technologies as a dynamical system for all three cases, Arthur explains how in the case of increasing returns those random events may determine the final outcome, whereas in the other cases they are most likely “averaged out” over time. Thus, in the case of increasing returns the adoption process is path-dependent. As a result, an inferior technology might gain high adoption shares, if it by chance got an initial advantage over the competing technology. Inferior here means that the overall welfare outcome would be higher if the competing technology would have reached the same degree of adoption as the dominant one has. However, because of increasing returns, the system is locked-in with the inferior technology.

Arthur concludes that under increasing returns, market outcomes are not predictable, given that they depend on random events. As such, there is no guarantee that superior technologies or products will succeed. How to make sure then that a lock-in with inferior technology on the system-level can be avoided? To answer this question, Arthur turns towards politics and makes the case for regulation. To him, governmental intervention is needed to support the exploration of “promising but less popular technological paths” (Arthur, 1989:127).


Arthur, W. B. (1989). Competing Technologies, Increasing Returns, and Lock-In by Historical Events. Economic Journal, 99(394), 116–131.

Arthur, W. B. (2009). The nature of technology: what it is and how it evolves. New York, NY: Free Press.