Dependence of technological improvement on artifact interactions

TL;DR

This study empirically investigates how artifact interactions influence technological improvement rates across various domains, finding that higher interactions correlate with slower progress, supporting existing models.

Contribution

It introduces a novel, patent-based, domain-agnostic method to measure artifact interactions and empirically tests their impact on improvement rates across 27 technological domains.

Findings

Improvement rates are positively correlated with the inverse of artifact interactions.

Higher artifact interactions (complexity) lead to slower technological progress.

The proposed patent-based method effectively estimates artifact interactions across diverse domains.

Abstract

Empirical research has shown performance improvement of many different technological domains occurs exponentially but with widely varying improvement rates. What causes some technologies to improve faster than others do? Previous quantitative modeling research has identified artifact interactions, where a design change in one component influences others, as an important determinant of improvement rates. The models predict that improvement rate for a domain is proportional to the inverse of the domain interaction parameter. However, no empirical research has previously studied and tested the dependence of improvement rates on artifact interactions. A challenge to testing the dependence is that any method for measuring interactions has to be applicable to a wide variety of technologies. Here we propose a patent-based method that is both technology domain-agnostic and less costly than alternative methods. We use textual content from patent sets in 27 domains to find the influence of interactions on improvement rates. Qualitative analysis identified six specific keywords that signal artifact interactions. Patent sets from each domain were then examined to determine the total count of these 6 keywords in each domain, giving an estimate of artifact interactions in each domain. It is found that improvement rates are positively correlated with the inverse of the total count of keywords with correlation coefficient of +0.56 with a p-value of 0.002. The empirical results agree with model predictions and support the suggestion that domains with higher number of artifacts interactions (higher complexity) will improve at a slower pace.