The Natural Evolution of Computing

Good Coverage ◽

Innovation Processes

The evolution of computing is an example of a major, transformative technological adaptation still unfolding in human history. Information technologies are supported by many other knowledge domains that have evolved through a cumulative cultural process, yet at the same time computing affects the tempo and mode of cultural evolution, greatly accelerating innovation processes driven by recombination of present technologies. Additionally, computing has created entire new domains for cumulative cultural evolution, furthering an era dominated by digital economies and media. These new domains offer very desirable qualities for cultural evolution research and digital archaeology, including good coverage in data completeness in widely different aspects of human culture, from social networks to innovation in programming languages. We review the major transitions in information technologies, with especial interest in their connections to a biological evolutionary framework. In particular, software vs. hardware evolution poses an interesting example of symbiotic technologies that display strong social dependencies as well as an extrinsic fitness due to energetic and temporal constrains. Properly accounting for the interplay of material and social factors can explain the coexistence of gradualism and punctuated dynamics in cultural and technological evolution.

Revisiting the effect of population size on cumulative cultural evolution

10.1101/001529 ◽

2013 ◽

Author(s):

Ryan Baldini

Keyword(s):

Population Size ◽

Cultural Evolution ◽

Transmission Error ◽

Network Size ◽

Original Model ◽

Technological Evolution ◽

Technological Complexity ◽

Wide Range ◽

Size Population ◽

AbstractHenrich (2004) argued that larger populations can better maintain complex technologies because they contain more highly skilled people whom others can imitate. His original model, however, did not distinguish the effects of population size from population density or network size; a learner’s social network included the entire population. Does population size remain important when populations are subdivided and networks are realistically small? I use a mathematical model to show that population size has little effect on equilibrium levels of mean skill under a wide range of conditions. The effects of network size and transmission error rate usually overshadow that of population size. Population size can, however, affect the rate at which a population approaches equilibrium, by increasing the rate at which innovations arise. This effect is small unless innovation is very rare. Whether population size predicts technological complexity in the real world, then, depends on whether technological evolution is innovation-limited and short of equilibrium. The effect of population “connectedness,” via migration or trade, is similar. I discuss the results of this analysis in light of the current empirical debate.

Analogy as a Catalyst for Cumulative Cultural Evolution

Trends in Cognitive Sciences ◽

10.1016/j.tics.2021.03.002 ◽

2021 ◽

Author(s):

C.O. Brand ◽

A. Mesoudi ◽

P.E. Smaldino

Keyword(s):

Cultural Evolution ◽

Copy the In-group: Group Membership Trumps Perceived Reliability, Warmth, and Competence in a Social-Learning Task

Psychological Science ◽

10.1177/09567976211032224 ◽

2021 ◽

pp. 095679762110322

Author(s):

Marcel Montrey ◽

Thomas R. Shultz

Keyword(s):

Social Learning ◽

Cultural Evolution ◽

Learning Strategy ◽

Group Membership ◽

Social Groups ◽

Learning Task ◽

Minimal Group Paradigm ◽

Learning Game ◽

Group Members ◽

Surprisingly little is known about how social groups influence social learning. Although several studies have shown that people prefer to copy in-group members, these studies have failed to resolve whether group membership genuinely affects who is copied or whether group membership merely correlates with other known factors, such as similarity and familiarity. Using the minimal-group paradigm, we disentangled these effects in an online social-learning game. In a sample of 540 adults, we found a robust in-group-copying bias that (a) was bolstered by a preference for observing in-group members; (b) overrode perceived reliability, warmth, and competence; (c) grew stronger when social information was scarce; and (d) even caused cultural divergence between intermixed groups. These results suggest that people genuinely employ a copy-the-in-group social-learning strategy, which could help explain how inefficient behaviors spread through social learning and how humans maintain the cultural diversity needed for cumulative cultural evolution.

Understanding cumulative cultural evolution

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1610005113 ◽

2016 ◽

Vol 113 (44) ◽

pp. E6724-E6725 ◽

Cited By ~ 55

Author(s):

Joseph Henrich ◽

Robert Boyd ◽

Maxime Derex ◽

Michelle A. Kline ◽

Alex Mesoudi ◽

...

Keyword(s):

Cultural Evolution ◽

Cultural accumulation and improvement in online fan fiction

10.31219/osf.io/4wjnm ◽

2020 ◽

Author(s):

Federico Pianzola ◽

Alberto Acerbi ◽

Simone Rebora

Keyword(s):

Digital Media ◽

Cultural Evolution ◽

Harry Potter ◽

Fan Fiction ◽

Cumulative Culture ◽

Cultural Systems ◽

Cultural Traits ◽

Cultural Evolution Theory ◽

Digital Literary Studies

We analyse stories in Harry Potter fan fiction published on Archive of Our Own (AO3), using concepts from cultural evolution. In particular, we focus on cumulative cultural evolution, that is, the idea that cultural systems improve with time, drawing on previous innovations. In this study we examine two features of cumulative culture: accumulation and improvement. First, we show that stories in Harry Potter’s fan fiction accumulate cultural traits—unique tags, in our analysis—through time, both globally and at the level of single stories. Second, more recent stories are also liked more by readers than earlier stories. Our research illustrates the potential of the combination of cultural evolution theory and digital literary studies, and it paves the way for the study of the effects of online digital media on cultural cumulation.

Cumulation

Cultural Evolution in the Digital Age ◽

10.1093/oso/9780198835943.003.0008 ◽

2019 ◽

pp. 186-212

Author(s):

Alberto Acerbi

Keyword(s):

Digital Media ◽

Cultural Evolution ◽

Cultural Selection ◽

Cultural Domain ◽

Efficient Mechanisms

Chapter 8 considers what cultural evolutionists call cumulative cultural evolution, that is, the idea that culture increases in complexity. For a cultural domain being defined as cumulative, it needs to show accumulation (more traits), improvement (traits are more efficient), and ratcheting (new traits build on previous innovations). The author proposes that this is not a necessary outcome, and that different domains show different signs of cumulation. It is suggested that the fidelity and the hyper-availability provided by digital media allow for more cumulation in domains where it was limited before. Not surprisingly, they also allow for the retention of vast amounts of useless information—junk culture. A central challenge for the coming years is thus finding efficient mechanisms of online cultural selection. Algorithmic selection is finally discussed, along with how mainstream criticisms, such as the fact that algorithms are biased or opaque to users, are not decisive arguments against their efficacy and utility.

Human Cumulative Cultural Evolution as a Form of Distributed Computation

Handbook of Human Computation ◽

10.1007/978-1-4614-8806-4_76 ◽

2013 ◽

pp. 979-992 ◽

Cited By ~ 9

Author(s):

Paul E. Smaldino ◽

Peter J. Richerson

Keyword(s):

Cultural Evolution ◽

Distributed Computation ◽

The evolution of high-fidelity social learning

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2020.0090 ◽

2020 ◽

Vol 287 (1928) ◽

pp. 20200090

Author(s):

Marcel Montrey ◽

Thomas R. Shultz

Keyword(s):

Social Learning ◽

Cultural Evolution ◽

Bayesian Model ◽

Individual Learning ◽

High Fidelity ◽

Human Culture ◽

Over Time

A defining feature of human culture is that knowledge and technology continually improve over time. Such cumulative cultural evolution (CCE) probably depends far more heavily on how reliably information is preserved than on how efficiently it is refined. Therefore, one possible reason that CCE appears diminished or absent in other species is that it requires accurate but specialized forms of social learning at which humans are uniquely adept. Here, we develop a Bayesian model to contrast the evolution of high-fidelity social learning, which supports CCE, against low-fidelity social learning, which does not. We find that high-fidelity transmission evolves when (1) social and (2) individual learning are inexpensive, (3) traits are complex, (4) individual learning is abundant, (5) adaptive problems are difficult and (6) behaviour is flexible. Low-fidelity transmission differs in many respects. It not only evolves when (2) individual learning is costly and (4) infrequent but also proves more robust when (3) traits are simple and (5) adaptive problems are easy. If conditions favouring the evolution of high-fidelity transmission are stricter (3 and 5) or harder to meet (2 and 4), this could explain why social learning is common, but CCE is rare.