Original Article
Cultural complexity and demography: The case of folktales

https://doi.org/10.1016/j.evolhumbehav.2017.03.005Get rights and content

Abstract

We investigate the relationship between cultural complexity and population size in a non-technological cultural domain for which we have suitable quantitative records: folktales. We define three levels of complexity for folk narratives: the number of tale types, the number of narrative motifs, and, finally, the number of traits in variants of the same type, for two well-known tales for which we have data from previous studies. We found a positive relationship between number of tale types and population size, a negative relationship for the number of narrative motifs, and no relationship for the number of traits. The absence of a consistent relationship between population size and complexity in folktales provides a novel perspective on the current debates in cultural evolution. We propose that the link between cultural complexity and demography could be domain dependent: in some domains (e.g. technology) this link is important, whereas in others, such as folktales, complex traditions can be easily maintained in small populations as well as large ones, as they may appeal to universal cognitive biases.

Introduction

Recent work in cultural evolutionary theory has explored the relationship between demography (in particular, population size) and cultural complexity (French, 2015, Henrich, 2004, Shennan, 2015). Formal models, both involving selectively neutral (Premo and Kuhn, 2010, Shennan, 2001) and non-neutral traits (Henrich, 2004, Kobayashi and Aoki, 2012, Powell et al., 2009, Shennan, 2001), predict that population size affects a population's ability to invent and maintain complex culture (but see, for a criticism to this approach: Querbes et al., 2014, Vaesen, 2012, Andersson and Read, 2016, Vaesen et al., 2016a, Vaesen et al., 2016b).

These models are based on two widely shared intuitions: that small societies, due to having fewer inventors, have lower rates of invention; and that, in the rare event of invention, innovations are more likely to be lost in smaller populations, simply as a result of random loss or incomplete transmission (Richerson, Boyd, & Bettinger, 2009). For example, an influential model developed by anthropologist Joe Henrich (Henrich, 2004) proposes that, in any given population, individuals will attempt to copy the most accomplished demonstrator of a particular skill, but, since social learning is error prone, on average, learners would not be expected to attain the level of skill of the demonstrator, with only a small chance of equalling or surpassing him/her. Consequently, in a small population, it is rare that complex traits (for which errors are more likely) will be copied correctly, resulting in a loss of cultural complexity. “Population size” in this model is intended as the number of individuals that are potentially able to interact, and is referred to as “effective population size” to distinguish it from “census population size”, i.e. census data on the estimated total number of individuals belonging to a particular ethno-linguistic group (Henrich et al., 2016, see also Lycett & Norton, 2010 for a similar definition). It has been pointed out that for cultural traits, the true “effective population size” may vary from a census count due to the possibility of cultural exchanges across ethno-linguistic boundaries (Henrich et al., 2016).

The existence of a positive effect of population size on cultural complexity is supported by a growing body of results from laboratory experiments in which larger groups of participants are able to create, and support, more complex culturally-transmitted behaviours than smaller groups (Derex et al., 2013, Derex and Boyd, 2015, Kempe and Mesoudi, 2014, Muthukrishna et al., 2014). In parallel, a number of empirical studies have explored the existence of a correlation between cultural complexity and population size. These studies generally focused on subsistence-related technologies (see e.g. Buchanan et al., 2015, Collard et al., 2013c, Collard et al., 2013a, Collard et al., 2005, Collard et al., 2013b, Kline and Boyd, 2010, Read, 2008). The majority of cultural evolutionists consider the results of these analyses to provide robust support for a positive correlation between cultural complexity and population size (Henrich et al., 2016), although some researchers remain skeptical (Andersson and Read, 2016, Vaesen et al., 2016a, Vaesen et al., 2016b).

While the relationship between demography and cultural complexity has been a key debate in the field of cultural evolution, the evidence produced by the empirical studies above is restricted to the domain of technology. Some studies have explored how linguistic complexity is influenced by demographic variables, but the results remain contentious (Hay and Bauer, 2007, Lupyan and Dale, 2010, Moran et al., 2012, Roberts and Winters, 2012). To our knowledge, no studies have explored, from a cultural evolutionary perspective, how population size might influence cultural complexity in other non-technological domains, where the same intuitions about social learning might be expected to apply. Here, we investigate one such domain, for which we have suitable quantitative information: folktales.

A folktale is defined as a prose narrative that cannot be attributed to any individual author, but, rather, constitutes a shared cultural tradition that has been passed on from person to person, and from generation to generation, usually by means of oral transmission (Thompson, 1951). While these stories will be familiar to many or most members of a population, literary and ethnographic research suggests that their long-term transmission depends on a small percentage of “active bearers” – expert storytellers who are directly analogous to “skilled demonstrators” in models of technological transmission (Henrich, 2004) – without whom these traditions would rapidly degenerate (e.g. Hansen, 2002, Sydow, 1948). Based on the demographic models discussed above, we might therefore expect the complexity of folk narrative traditions to covary with the number of active bearers available in a population. This is because when an individual invents a new tale or elaborates on an existing one (e.g. by introducing new characters and events) their innovations are more likely to catch on when there are other individuals who are sufficiently talented to memorise and reproduce them, in a manner directly analogous to the accumulation of technological complexity.

In what follows, using both data from the Aarne Thompson Uther (ATU) Index and from previous phylogenetic analysis of two tales, we analyse three levels of cross-cultural complexity in folk narrative, based on: (i) the number of “tale types”, (ii) the number of narrative “motifs”, and (iii) the number of traits in different variants of the same “type”.

Section snippets

Material and methods

We define three levels of complexity in folk narrative.

Results

Table 1 summarises the main results of our models. The output we are interested in is β, which represents the “slope” of the regression complexity/population size. Following McElreath (2016a), we report the 5.5% and 94.5% quantiles of the posterior probability, and we consider that, when these (arbitrary-sized) 89% intervals do not overlap zero, this provides strong evidence of a relationship between complexity and population size. Three out of five measures were affected by population size,

Discussion

The results of our analyses provide conflicting evidence about the relationship between demography and the complexity of oral traditions. On the one hand, we found strong support for a positive relationship between population size and the overall number of folktales recorded in different societies. On the other, it appears that the tales told in larger populations have a lower average number of motifs than those in smaller groups, while a comparison of variants of two international tale types

Data availability

The data associated with this research are available as Supplementary material.

Funding

The work of AA was supported by a grant from The Netherlands Organisation for Scientific Research (NWO VIDI-grant 016.144312).

Acknowledgements

We thank Sάndor Darάnyi and Nir Ofek for providing the data on motif distributions, and Robert Ross for providing the data used in the analysis of “The tale of the kind and unkind girls”. We also thank Eugenio Bortolini, Thom Scott-Phillips, Krist Vaesen, and two anonymous reviewers for comments on previous versions of the manuscript.

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