The purposes of this paper are threefold: to suggest a conceptual scheme which encompasses innovations and improvements in industry; to express this scheme in the mathematical form of Poisson jump processes; and, finally, to illustrate it with a sequence of cost data drawn from three‐quarters of a century of company operations. In the stochastic process, three parameters summarise the events: (i) random intervals between innovations; (ii) different random intervals between improvements; and (iii) the rate at which the benefits of improvements decay over time. The fit between the model’s performance, on the one hand, and the cost data, on the other, is good: the theoretical deductions and the actual cost series bear a close resemblance. Most models of technological progress are based upon the mere passage of time, whereas this model stems from the events.

PAGES
5 – 13
DOI
All content is freely available without charge to users or their institutions. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission of the publisher or the author. Articles published in the journal are distributed under a http://creativecommons.org/licenses/by/4.0/.
Issues
Also in this issue:
-
Agnes Horvath, Magic and the Will to Science: A Political Anthropology of Liminal Technicality
-
Gibson Burrell, Ronald Hartz, David Harvie, Geoff Lightfoot, Simon Lilley and Friends, Shaping for Mediocrity: The Cancellation of Critical Thinking at our Universities
-
Bas de Boer, How Scientific Instruments Speak: Postphenomenology and Technological Mediations in Neuroscientific Practice
-
Bjørn Lomborg, False Alarm
-
How does innovation arise in the bicycle sector? The users’ role and their betrayal in the case of the ‘gravel bike’