Theoretical Cultural Evolution
The multidisciplinary Theory in Cultural Evolution Lab (TICE Lab) is based in the Department of Human Behavior, Ecology and Culture (HBEC) at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. The lab brings together a unique group of mathematicians, physicists, theoretical biologists, and statisticians contributing to a general theory of cultural evolution.
Cultural evolution aims in part to explain the dynamics of cultural change, defined as changes in the frequency and diversity of cultural traits over time. We work on the premise that a mechanistic understanding of the processes underlying cultural change can help us to explain something about the human species beyond what can be gleaned from genetic or even cultural data alone. Thus, we aim to place the study of cultural evolution on a firm theoretical footing, and provide a bridge between that theory and the cultural data collected by anthropologists and archaeologists.
To do this, we focus on developing analytical and simulation models of various cultural phenomena. The Projects section lists some current lab projects that illustrate the kind of research we do and outline the methods we use.
About HBEC and the TICE lab
At the Max Planck Institute for Evolutionary Anthropology
The Department of Human Behavior, Ecology and Culture, headed by Director Richard McElreath, is a unique environment for theoreticians where there is daily interaction with field anthropologists and empirical researchers. The exchange of ideas, knowledge and data in the department is invaluable, it drives our theory towards useful and grounded questions and allows that theory, in turn, to shape the development of effective data collection and quantitative analysis in anthropology.
The department is highly interdisciplinary with researchers from diverse backgrounds including comparative behavioral ecology, psychology, economics, archaeology, anthropology, evolutionary biology, statistics, and mathematics - all now focused on the study of human ecology and culture.
Because of the diversity of our department and the value we place on interdisciplinary research, the TICE lab explicitly seeks students and post doctoral researchers from diverse backgrounds. We especially encourage applications from students with quantitative backgrounds. However, prospective students and post docs are not expected to have prior experience modelling cultural evolution. We offer a training program designed to enable students with more technical backgrounds to get to grips with evolutionary biology and cultural evolution, and to help students with less technical backgrounds improve their modelling skills.
Linking Individual-Level Processes to Population-Level Patterns
Understanding how group-level patterns of culture emerge from individual-level behaviour is a long-standing question in the biological and social sciences. We study whether, and under what conditions, different processes of cultural transmission can indeed be distinguished based on their group-level signature, in an effort to establish theoretical limits to inference. In other words, we ask how much information about underlying transmission processes can be extracted from population-level frequency data of a given temporal resolution. We also develop statistical inference frameworks to analyse real-world datasets (e.g. datasets recording the temporal change in frequency of different types of decorated pottery in the LBK period). Borrowing insights from population genetics, we use generative inference approaches which broadly speaking evaluate the consistency between different individual-level transmission processes and observed population-level patterns. In this way we hope to link empirical and theoretical work in cultural evolution closer together.
Demography and Cultural Evolution
This research examines the interaction between subsistence strategy, demographic structure, and the spread of demographically important cultural traits affecting survival, fertility, or both. The work investigates how the cultural transmission of such traits can influence the birth rate, age structure, and asymptotic growth rate of a population. In particular, the ages at which different types of learning (for example, vertical or oblique learning) may be important can vary among populations, particularly those with different subsistence strategies, and this may have important effects on cultural evolutionary processes and, in turn, demographic structure itself.
Innovation and Creativity
Creativity represents the starting point of the cultural evolutionary process but has typically received less attention in models of cultural evolution than, for example, modes of cultural transmission. Studies of the neuroscience and psychology of creativity have shown that it is a nuanced, idiosyncratic, and complex process that is rarely adequately captured by the single ‘cultural mutation rate’ that is often used in models. Our work aims to improve the representation of creativity in models of cultural evolution and to develop new theoretical frameworks to untangle the effects of cultural transmission and creativity on patterns of cultural accumulation.
Literature in cultural evolution often suggests, or explicitly states, that humans have colonised much of the planet because of our unique ability to amass complex culture and to adapt culturally to new environments. However, we understand little about what this process of cultural adaptation might look like. We work on mathematical models of cultural adaptive processes that investigate the different roles of existing cultural variation, cultural transmission, and innovation. We examine the probability of adaptation under different circumstances and discuss circumstances under which cultural adaptation might fail. We also investigate ways in which we can use such models to identify cultural selective sweeps in cultural data, exploring the kind of data and analysis needed to infer selection and cultural adaptation.
Niche Construction and Cultural Niche Construction
Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. We develop models of this process and examine the interactions between cultural transmission, population structure, selection, and niche construction, in particular focusing on new models of niche construction and cultural niche construction with a view to resolving controversies surrounding the evolutionary importance of niche construction and driving forward our theoretical understanding of non-genetic inheritance.
There often is a disconnect between mathematical models describing cultural systems and cultural data. Where the models focus on equilibrium states, it is often unclear whether data is collected from a system in equilibrium. Reconciling the theory and the data requires models that can replicate the temporal dynamic that can be compared to the observed data.
Process to Patterns
Kandler, A., & Powell, A. (2018). Generative inference for cultural evolution. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences,373(1743).
O'Dwyer, J. P., & Kandler, A. (2017). Inferring processes of cultural transmission: The critical role of rare variants in distinguishing neutrality from novelty biases. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences,372(1735): 20160426.
Kandler, A., Wilder, B., & Fortunato, L. (2017). Inferring individual-level processes from population-level patterns in cultural evolution. Royal Society Open Science,4: 170949
Kandler, A., & Powell, A. (2015). Inferring learning strategies from cultural frequency data. In A. Mesoudi, & K. Aoki ( Eds. ), Learning strategies and cultural evolution during the Palaeolithic (pp. 85-101). Tokyo: Springer Japan.
Kandler, A., & Shennan, S. (2015). A generative inference framework for analysing patterns of cultural change in sparse population data with evidence for fashion trends in LBK culture. Journal of the Royal Society, Interface,12(113): 20150905.
Wilder, B., & Kandler, A. (2015). Inference of cultural transmission modes based on incomplete information. Human Biology,87(3), 193-204.
Demography, Structure and Culture
Fogarty, L., Creanza, N., & Feldman, M. W. Subsistence strategy, age-structured social learning, and cultural evolution: the construction of learning niches. In press at PLOS Computational Biology.
Fogarty, L., Creanza, N., & Feldman, M. W. (2013). The role of cultural transmission in human demographic change: An age-structured model. Theoretical Population Biology, 88, 68-77.
Kandler, A., Perreault, C., & Steele, J. (2012). Cultural evolution in spatially structured populations: A review of alternative modeling frameworks. Advances in Complex Systems,15(1-2): 1203001.
Fogarty, L. and Feldman, M.W. (2011) The cultural and demographic evolution of son preference and marriage type in contemporary China. Biological Theory, vol. 3 no. 6, pp. 272-282.
Rendell, L.E., Fogarty, L. & Laland, K.N. (2010) Rogers' paradox recast and resolved: population structure and the evolution of social learning strategies, Evolution, vol. 64, no. 2, pp. 534-548.
Kandler, A. (2009). Demography and language competition. Human Biology,81(2-3), 181-210.
Innovation and Creativity
Fogarty, L. & Creanza, N. (2017) The niche construction of cultural complexity: interactions between innovations, population size, and the environment, Phil. Trans. R. Soc. B 372: 20160428
Fogarty, L., Creanza, N. & Feldman, M.W. (2015) Cultural evolutionary perspectives on early human innovation. Trends in Ecology and Evolution, vol. 30, no. 12, pp. 736-754.
Kandler, A., & Laland, K. N. (2009). An investigation of the relationship between innovation and cultural diversity. Theoretical Population Biology,76(1), 59-67.
Kandler, A., & Steele, J. (2009). Innovation diffusion in time and space: Effects of social information and of income inequality. In Diffusion Fundamentals III: Athens 2009 (pp. 82-98). Leipzig: Leipziger Universitätsverlag.
Kandler, A., & Steele, J. (2009). Social learning, economic inequality, and innovation diffusion. In M. O'Brien, & S. J. Shennan ( Eds. ), Innovation in cultural systems: Contributions from evolutionary anthropology (pp. 193-216). Cambridge, MA: MIT Press.
Fogarty, L., Wakano, J.Y., Feldman, M.W. & Aoki, K. (2016) The driving forces of cultural complexity: Neanderthals, modern humans, and the question of population size. Human Nature, doi:10.1007/s12110-016-9275-6
Niche Construction and Cultural Niche Construction
Creanza, N., Fogarty, L. & Feldman, M.W. (2016) Cultural niche construction of repertoire size and learning strategies in songbirds. Evolutionary Ecology, vol. 30, pp. 285–305.
Creanza N, Fogarty L., Feldman MW. (2014) Cultural niche construction from the Paleolithic to modern hunter-gatherers. Dynamics of Learning in Neanderthals and Modern Humans, Vol. 1: Cultural Perspectives.
Creanza, N., Fogarty, L. & Feldman, M.W. (2012) Models of Cultural Niche Construction with Selection and Assortative Mating. PLoS ONE, vol. 7, no. 8, e42744
Fogarty, L., Rendell, L.E. & Laland, K.N. (2012) The importance of space in models of social learning, cultural evolution and niche construction, Advances in Complex Systems vol. 15, no. 1&2.
Rendell, L.E., Fogarty, L. & Laland, KN (2011) Runaway cultural niche construction, Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 366, no. 1566, pp. 823-835.
Non-Equilibrium Models of Cultural Evolution
Crema, E. R., Kandler, A., & Shennan, S. (2016). Revealing patterns of cultural transmission from frequency data: Equilibrium and non-equilibrium assumptions. Scientific Reports,6: 39122.
Kandler, A., & Shennan, S. (2013). A non-equilibrium neutral model for analysing cultural change. Journal of Theoretical Biology,330, 18-25.
Kandler, A., & Steele, J. (2017). Modeling language shift. Proceedings of the National Academy of Sciences,114(19), 4851-4853.
Kandler, A., Unger, R., & Steele, J. (2010). Language shift, bilingualism and the future of Britain's Celtic languages. Philosophical Transactions of the Royal Society B: Biological Sciences,365(1559), 3855-3864.
Steele, J., & Kandler, A. (2010). Language trees ≠ gene trees. Theory in Biosciences,129(2-3), 223-233.
Kandler, A., & Steele, J. (2008). Ecological models of language competition. Biological Theory,3(2), 164-173.
Join The TICE Lab
Please contact us if you are interested in post doctoral or PhD positions with the lab
We are always keen to hear from potential PhD students or post-doctoral researchers with interest (or experience) in theoretical cultural evolution, especially from mathematical or quantitative backgrounds. We are open to individuals interested in specific projects above or with an interest in the general topics and themes of the lab.
Potential PhD students who have a Masters Degree can apply through the International Max Planck Research School (IMPRS): the Leipzig School of Human Origins or contact us directly.
Potential postdocs should contact the lab initially and can also apply for external funding - here is a useful list of funding opportunities.