An inductive model of technological progress
Bibliographic Data
| ID | 15246747 |
|---|---|
| Authors | John L Enos (corresponding author), John Enos, Alison Etheridge (0000-0003-3669-8423) |
| Year | 2010 |
| Volume | 28 |
| Issue | 1 |
| Publication date | 2010-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Prometheus (JOURNAL) |
| Journal identifiers | ISSN: 0810-9028 • E-ISSN: 1470-1030 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/08109020903554294 |
| OpenAlex | W2072867999 |
| Language | EN |
| References cited | 6 |
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
Econometrics · Economics · Jump · Mathematical economics · Operations research · Poisson distribution · Poisson process · Process (computing · Sequence (biology · Series (stratigraphy · Statistics · Stochastic process · Technological change · Capital Investment and Risk Analysis · Computer Science · Mathematics · Artificial Intelligence
| Highly cited | No |
|---|