Competition and extinction explain the evolution of diversity in American automobiles

Abstract

One of the most remarkable aspects of our species is that while we show surprisingly little genetic diversity, we demonstrate astonishing amounts of cultural diversity. Perhaps most impressive is the diversity of our technologies, broadly defined as all the physical objects we produce and the skills we use to produce them. Despite considerable focus on the evolution of technology by social scientists and philosophers, there have been few attempts to systematically quantify technological diversity, and therefore the dynamics of technological change remain poorly understood. Here we show a novel Bayesian model for examining technological diversification adopted from palaeontological analysis of occurrence data. We use this framework to estimate the tempo of diversification in American car and truck models produced between 1896 and 2014, and to test the relative importance of competition and extrinsic factors in shaping changes in macro-evolutionary rates. Our results identify a four-fold decrease in the origination and extinction rates of car models, and a negative net diversification rate over the last 30 years. We also demonstrate that competition played a more significant role in car model diversification than either changes in oil prices or gross domestic product. Together our analyses provide a set of tools that can enhance current research on technological and cultural evolution by providing a flexible and quantitative framework for exploring the dynamics of diversification.

Macroecological and macroevolutionary patterns emerge in the universe of GNU/Linux operating systems

“…Our analyses demonstrate that the Linux universe and biological systems not only showcase many structural analogies (as already pointed out by Mens et al 2014a, b), but also share quantitatively similar emerging properties, including patterns of commonness and evolutionary rates. This is in line with findings from other complex systems, which also exhibit macroecological patterns (Gaston et al 1993, Mace and Pagel 1995, Bettencourt et al 2007, Nekola and Brown 2007, Warren et al 2011, Blonder et al 2014; and macroevolutionary patterns (Gjesfjeld et al 2016). The Linux universe shows that not one, but a whole spectrum of biological patterns can emerge in a single non-biological system.…”

“…Striking examples can be found in the rapidly expanding literature on cultural evolution (Dawkins 1976, Cavalli-Sforza and Feldman 1981, Boyd and Richerson 1985, Sperber 1996, Mesoudi 2016) which exploits parallels between (mostly) Darwinian evolution and the evolution of languages, beliefs, skills, knowledge, institutions or other forms of socially transmitted information. The notable exceptions (Mace and Pagel 1995, Nekola and Brown 2007, Blonder et al 2014, Gjesfjeld et al 2016 often focus on one selected pattern or on multiple but closely related ecological patterns (Blonder et al 2014). However, these fields have been emphasizing micro-evolutionary, population-genetic, or population-dynamic processes, lacking an evaluation of the emerging macro-ecological and macro-evolutionary patterns.…”

“…However, it has been demonstrated that some of the patterns are not unique to ecological and evolutionary systems, and often emerge in other complex systems (Gaston et al 1993, Mace and Pagel 1995, Bettencourt et al 2007, Nekola and Brown 2007, Warren et al 2011, Blonder et al 2014, Scheffer et al 2017). Examples are: species-abundance distributions of music festival setlists (Nekola and Brown 2007) and basketball wins by a team (Warren et al 2011), frequency distributions of components of software (Pang and Maslov 2013), latitudinal gradients of language diversity (Mace and Pagel 1995), species-area relationships in corporations, industrial codes, and minerals (Blonder et al 2014), and diversification slowdowns in American automobiles (Gjesfjeld et al 2016). To explain such conspicuous universality, theories have been proposed that predict the patterns across different systems for pure statistical reasons, given that the systems share structural constraints (Frank 2009, Sizling et al 2009, Harte 2011, Blonder et al 2014.…”

“…However, these fields have been emphasizing micro-evolutionary, population-genetic, or population-dynamic processes, lacking an evaluation of the emerging macro-ecological and macro-evolutionary patterns. The notable exceptions (Mace and Pagel 1995, Nekola and Brown 2007, Blonder et al 2014, Gjesfjeld et al 2016 often focus on one selected pattern or on multiple but closely related ecological patterns (Blonder et al 2014). While there are many examples of non-biological systems following patterns observed in biology, we are unaware of a system simultaneously exhibiting macroecological and macroevolutionary patterns.…”

Multi-disciplinarity breeds diversity: the influence of innovation project characteristics on diversity creation in nanotechnology

“…These alternatives can be designs (Carlsson and Jacobsson 1997;Murmann and Frenken 2006;Van Rijnsoever et al 2015), technical characteristics (Murmann and Frenken 2006;Saviotti and Metcalfe 1984;Van Rijnsoever et al 2015) or numbers of different technological lineages represented in a technology (Gjesfjeld et al 2016).…”