Phase 2: Theme A - Demographic processes and cultural change: Project A001|
Population size and structure as determinants of behavioural diversity and innovation rate
SUPERVISOR: - Steele/Laland
Anne Kandler (AHRC CECD, Institute of Archaeology, University College London)
James Steele (AHRC CECD, Institute of Archaeology, University College London)
Kevin Laland (Centre for Social Learning and Cognitive Evolution, University of St. Andrews)
Cards Against Humanity
A data-modelling system will be set up and developed to simulate gene-culture co-evolution, and used to investigate the possible effects of population size and structure, and of behavioural innovation rate, on cultural diversity. The project will utilise a meta-population structure model (with demes linked by low-level migration), in which cultural variants spread between individuals through social learning, and can propagate to other demes through migration. Such social interaction models are familiar in mathematical epidemiology(7), but their application to knowledge diffusion dynamics is less well understood.
This project will explore how within- and between-population cultural diversity is affected by the rate and location of innovation. Also manipulated will be the rate of information transmission within and between populations. We will assume cultural transmission is mildly conformist(1) – that is, that individuals are predisposed disproportionately to adopt the most frequent variant. The models will allow for stochasticity in deme sizes and meta-population structure, and will also explore how such stochasticity facilitates or impedes the diffusion of innovation. A second set of (formally related) analyses, again building on established models(2), will explore the effects of innovation and transmission on diversity in a hierarchically structured society. The objective will be to gain a general understanding of the relationships between innovation, transmission, migration, and fluctuating population size and their impact on diversity.
The model output will be summarised in a set of graphs of the phase space, and empirical data on cultural diversity in historical and contemporary human societies of known population structure will be used to fit innovation rate estimates. A simple demonstration of this is given by Hahn and Bentley(4), who use modern baby name registers in the United States to fit sex-specific innovation rates for names, assuming a single deme with random mixing. The metapopulation structure model will be used to fit innovation rates for languages in a sample of populations where resource structure is known to have had an effect on population structure and on linguistic diversity (paralleled by similar effects on biodiversity[3,6]). The hierarchically-structured society model will be used to fit innovation rates to societies with known historical levels of hierarchical integration, as have been studied recently for linguistic diversity(5).
(1) Boyd, R. and Richerson, P. J. (1985) Culture and the Evolutionary Process. Chicago: Chicago University Press.
(2) Cavalli-Sforza, L. & Feldman, M. (1981) Cultural Transmission and Evolution: A Quantitative Approach, Princeton: Princeton University Press.
(3) Collard I., and Foley R. (2002) Latitudinal patterns and environmental determinants of recent human cultural diversity: do humans follow biogeographical rules? Evolutionary Ecology Research 4: 371-383
(4) Hahn, M. and A. Bentley (2003). Cultural transmission and genetic drift: A case study using baby names. Proceedings of the Royal Society B. 270: S1-S4.
(5) Hamed, M. (2005) Neighbour-nets portray the Chinese dialect continuum and the linguistic legacy of China’s demic history. Proceedings of the Royal Society B 272: 1015–1022.
(6) Mace, R. and Pagel, M. (1995) A latitudinal gradient in the density of human languages in North America. Proceedings of the Royal Society B 261: 117-121.
(7) Watts, D. , Muhamad, R., Medina, D. & Dodds, P. (2005) Multiscale, resurgent epidemics in a hierarchical metapopulation model. Proc. Natl. Acad. Sci. 102: 11157-11162.
FINAL PROJECT REPORT:
In order to explore effects of population structure on cultural diversity for varying innovation and migration rates we studied two cultural systems, namely agricultural innovations and languages, with the population structure/migration rate given by the spatial dependence. In a slight modification of the original project brief, we changed 'varying innovation rates' into 'varying competition coefficients in a two-trait system, one being novel', in both cases.
In cooperation with Kevin Laland we addressed this issue directly by analysing the effects of innovation/invention on cultural diversity for populations with different social learning biases.
The problem of fitting of innovation rates in empirical instances of cultural diversity with known historical population structure is explored in papers written with J. Steele, in which we analyse census data on the decline of Scottish Gaelic (where the innovation is the English language and its associated set of social norms). We have also analysed data on the spread of hybrid corn in the U.S. In each of these two very diverse applications, we have focused on fitting competition coefficients and adoption rates for the more advantageous of two traits.
The major methodological contribution of this project was to adapt a Lotka-Volterra two-species competition model to social science applications, to develop a bilingual transition state term for the language shift appplication, and to add spatial dependence.
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|•||Kandler, A. and Laland, K.N (2009).|
An investigation of the relationship between innovation and cultural diversity. Theoretical Population Biology. Vol 76:59-67.
|•||Steele, J (2008).|
Population structure and diversity indices. In: S. Matsumura, P. Forster & C. Renfrew (eds.) (ed\s) Simulations, Genetics and Human Prehistory. Cambridge: McDonald Institute for Archaeological Research.
|•||A. Kandler and J. Steele (2007).|
Reaction-Diffusion Models of Language Competition. Emergence of Social Behaviour workshop. Proceedings 9th European Conference on Artificial Life. Lisbon, Portugal. September 10-14, 2007.
|•||A. Kandler and J. Steele (2007).|
Innovation diffusion in time and space: social learning and threshold heterogeneity models. European Conference on Complex Systems. Proceedings of the European Conference on Complex Systems. Dresden, Germany, October 1-5, 2007.