Every new technology builds on what came before, filling niches based on user needs and evolving into "adjacent possibles" through processes of cultural transmission and social learning. Or said more succinctly -- technology evolves. Thus it can be studied using the tools of evolutionary biology.
Many approaches to the study of technology focus on material qualities for the tools and devices. A cultural evolutionary lens draws attention to the social patterns of learning and innovation that give rise to stable technology designs that persist for great lengths of time as well as to the rapid alterations of designs that change quickly. An example of the former is the simple stone tools used by early hominids that changed very little over the span of a million years and chimpanzee nutcracking tools found at an archeological depth of 4,000 years. An example of relatively rapid evolution is the well-known computer processing curve known as Moore's Law -- where a doubling of price performance is achieved every two years.
Cultural evolution researchers ask questions about the environments where technologies arise and how these contexts shape the dynamics of technological change. They also explore the extent to which observed changes can be attributed to random mutations in technique (cultural drift), vertical or horizontal transmission (learning from parents or copying from one's peers), and how different communities interact with one another to share innovations.
While it is often the case that technology improves with time, there are strong examples where environmental changes have impacted the size and diversity of skills within a population that cause it to go into decline. One such example is Tasmania where sea-level rise at the end of the last Ice Age caused a portion of the Aboriginal population to become physically isolated from the mainland of Australia. This event was followed by decreasing quality of canoes and hunting equiptment, and even to the dramatic loss of technologies like the boomerang.
When viewed through the lens of knowledge ecology, a richly complex and dynamic perspective becomes possible for the study of technology. Social networks can be mapped to identify how capable a community is at improving the technologies they use and whether they are at risk of losing aggregate knowledge that will be essential for maintaining what they have. Where thresholds exist it becomes possible to measure the stability of a developmental pathway or to track population-level changes in technical innovation. This can be done for innovation ecosystems from Silicon Valley to the stagecraft of Broadway musicals -- as well as for making sense of the archeological record of prior technological explosions.
Our scientific community is comprised of many different areas of expertise pertaining to technology studies. There are anthropologists conducting ethnographic studies of social learning; archeologists exploring ancient field sites to uncover historic patterns of social complexity; data scientists tracking the exchange of information in digital learning environments; cognitive scientists conducting diffusion experiments, as well as animal behavior researchers looking at similar mechanisms and processes in ants and bees, monkeys and chimpanzees, crows and whales, and other technological species.
This diversity of approaches to technology makes it possible to achieve coherence across the studies at the micro level of individual and small group learning (as might be done in psychology research, for example). It also enables a bridging to the macro level more commonly studied by sociologists, economists, and historians.
Boyd, R., Richerson, P. J., & Henrich, J. (2014). The cultural evolution of technology: facts and theories. In P. J. Richerson & M. H. Christiansen (Eds.), Cultural evolution (pp. 119–142). Cambridge, MA: MIT Press.
Ziman, J. (2000). Technological Innovation as an Evolutionary Process. Cambridge: Cambridge University Press.
Basalla, G. (1988). The Evolution of Technology. Cambridge: Cambridge University Press