Anne Kandler

Archaeologists have devoted a significant amount of effort to define their types, or units of analysis. However, strong disagreements persist even after years of debate. This may occur for many reasons, some intrinsic to the archaeological record itself—since different technocomplexes may involve distinct and not always comparable types—and others related to the discipline’s epistemology, where interobserver bias and differences in theoretical schools and training traditions can play a key role. As a result, most archaeological analyses, usually based on type presence/absence or type frequencies, present uncertainty levels from the start, which are not always due to the archaeological record itself, but to how we analyse that record. In this work, we adopt a materialist view to present an alternative approach where we analyse purely artefact shape without the need for previous categorisation. To do this, we resort to Geometric Morphometrics, widely used in archaeology, and we develop a simulation framework for stepwise artefactual change. We provide the software tools to do this, which have been compiled in the R-package Simumorph, and we showcase its performance in four simulated archaeological scenarios. Here, we base our simulations on the geometric microliths found during the Early and Middle Holocene in the Western Mediterranean, but the framework can be applied to any context where shape variation matters.

Comparative analyses of diversity in human populations often rely on historical, archival and observational data, which are systematically shaped by uneven documentation, preservation and research attention. Such collection bias can distort comparisons across populations, regions or time periods, obscuring genuine patterns of variation or generating spurious ones. Standard approaches that equalize sample size or sampling effort implicitly assume comparable sampling completeness, which is an assumption rarely satisfied in human and historical datasets. Here, we evaluate coverage-based standardization, adapted from ecological diversity estimation, as a general framework for comparative diversity analysis under biased and incomplete observation. Using population-level simulations, we show that coverage-based approaches reliably recover true diversity relationships across multiple, qualitatively distinct mechanisms of collection bias, whereas sample-size-based methods yield systematically distorted inferences. We illustrate the utility of this framework with an application to a large historical cultural dataset, where correcting for uneven documentation substantially refines inferred diversity patterns. By conditioning comparisons on sampling completeness rather than raw sample size, coverage-based standardization offers a principled and broadly applicable solution for comparative research in any domain where observation is biased, incomplete or historically contingent.

Cultural evolutionary processes can often lead to a statistical association between neutral and adaptive traits during episodes of population dispersal and the introduction of a beneficial technology in a geographic region. Here, we examine such cultural hitchhiking processes using an individual-based model that portrays the cultural interaction between a migrant and an incumbent population. Our model is loosely based on the interaction between farming and foraging populations during the initial stages of the adoption and diffusion of agricultural practices. The two populations are characterized by different variants for their neutral and adaptive cultural traits, with the latter set providing a reproductive advantage for the migrant communities over the incumbent ones. We explore how the neutral traits of the migrant population spread and how this process is conditioned by the following factors: 1) the possibility of transmission of the adaptive traits; 2) the extent of the increased reproductive advantage provided by the adaptive variants of the migrant population; 3) postmarital residence rules; and 4) how and when neutral traits are transmitted. Our results reveal a diverse range of outputs, highlighting the relevance of factors such as the nature of postmarital resocialization and the specific combination of postmarital residence rules and sex-biased transmission.

Investments in housing influence migration and landscape construction, making them a key component of human–environment interactions. However, the strategic decision-making that builds residential landscapes is an underdeveloped area of research in evolutionary approaches to human behaviour. Our contribution to this literature is a theoretical model and an empirical test of this model using data from Ulaanbaatar, Mongolia. We develop a model of strategic housing decisions using stochastic dynamic programming (SDP) to explore the trade-offs between building, moving and saving over time, finding different trade-offs depending on optimization scenarios and housing costs. Household strategies are then estimated using data on 825 households that settled in the Ger districts of Ulaanbaatar between 1942 and 2020. The Ger districts are areas of self-built housing that feature both mobile dwellings (gers) and immobile houses (bashins). Using approximate Bayesian computation (ABC), we find the parameters of our dynamic programming model that best fit the empirical data. The model is able to capture the time horizon of housing changes and their bi-directionality, showing that moving from a fixed to mobile dwelling can also be an optimal strategy. However, the model underpredicts household persistence in dwelling types. We discuss deviations from model predictions and identify a more detailed exploration of risk and population mixes of strategies as key steps for future research.

The adaptability of human populations to changing environments is often attributed to the human capacity for social learning, innovation, and culture. In rapidly changing environments, it has been shown that maintaining high levels of cultural variation is beneficial because it allows for efficient adaptation. However, in many theoretical models, a high level of cultural variation also implies that a large amount of useless and perhaps detrimental information must be maintained and used, leading to lower population fitness in general. Here, we begin to investigate this often conflicting relationship between adaptation and cultural variation. We explicitly allow for the interplay between social learning and environmental variability, alongside the capacity for “memory,” i.e., the storage, retrieval, and forgetting of information. Here, memory allows individuals to retain unexpressed cultural variation, which does not directly impact adaptation. We show that this capacity for memory facilitates the evolution of social learning across a broader range of circumstances than previously thought. Results from this analysis may help to establish whether and when memory should be incorporated into cultural evolutionary models focused on questions of adaptation.

Empirical work has shown that human cultural transmission can be heavily influenced by population age structure. We aim to explore the role of such age structure in shaping the cultural composition of a population when cultural transmission occurs in an unbiased way. In particular, we are interested in understanding the effect induced by the interplay between age structure and the cultural transmission process by allowing cultural transmission from individuals within a limited age range only. To this end we develop an age-structured cultural transmission model and find that age-structured and non age-structured populations evolving through unbiased transmission possess very similar cultural compositions (at a single point in time) at the population and sample level if the copy pool consists of a sufficiently large fraction of the population. If, however, an age constraint—a structural constraint restricting the pool of potential role models to individuals of a limited age range— exists, the cultural compositions of age-structured and non age-structured population show stark differences. This may have drastic consequences for our ability to correctly analyse cultural data sets. Rejections of tests of neutrality, blind to age structure and, importantly, the interaction between age structure and cultural transmission, are only indicative of biased transmission if it is known a priori that there are no or only weak age constraints acting on the pool of role models. As this knowledge is rarely available for specific empirical case studies we develop a generative inference approach based on our age-structured cultural transmission model and machine learning techniques. We show that in some circumstances it is possible to simultaneously infer the characteristics of the age structure, the nature of the transmission process, and the interplay between them from observed samples of cultural variants. Our results also point to hard limits on inference from population-level data at a single point in time, regardless of the approach used.

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed or modelled interchangeably, yet to date no study has explored the consequences of their interaction for cultural diffusions. We present a generative model that integrates the two, and ask how variation in production rules might influence diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires may also change, and the acquisition of behaviour is conditioned on its frequency. We analyse the diffusion of a novel behaviour through social networks, yielding generalizable predictions of how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking acquisition and production might affect the performance of two commonly used inferential models for social learning; network-based diffusion analysis, and experience-weighted attraction models. We find that the influence that production rules have on diffusion dynamics has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies.