United Kingdom

Cultural Evolution Society Conference 2024

 Introductions

Welcome to the Cultural Evolution Society (CES) Conference 2024, which was a hybrid conference held at Durham University (UK). The conference was hosted in Durham by the Durham Cultural Evolution Research Centre (DCERC) with administrative assistance from the CES Transformation Fund team.

**For more information about the CES Conference, please visit the [CES Website Home Page](https://ces2024.webspace.durham.ac.uk/).**

#### Durham Cultural Evolution Research Centre (DCERC) 
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**Interdisciplinary study of cultural change and diversity**

We are a highly interdisciplinary group of researchers using evolutionary models to study cultural change and diversity.  We apply cultural evolutionary methods to a wide variety of topics including cognition, innovation, social learning, technology, art, music, narrative, religion, sociality, language and communication. We have a growing interest in applying cultural evolutionary theory to current societal challenges such as understanding the spread of prosocial health behaviours and health conspiracies.<br>
**[Discover More](https://www.durham.ac.uk/research/institutes-and-centres/cultural-evolution/)**

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If you feel at any time you have been the victim of harassment or inappropriate behavior that contradicts our code of conduct, you may use the following form to report the incident. 

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The field of cultural evolution is growing in size and scope; its theories, methods and applications - both academic and applied - are getting ever more robust. Do join us in what promises to be an exciting exchange of ideas and a celebration of our field.

**Abstract:**

Paleobiologists use phylogenetics to study the patterns and processes of organismal evolution. Phylogenetic methods have also been deployed for reconstructing ancestor-descendant relationships using cultural data. Within evolutionary archaeology specifically, phylogenetic analyses based on maximum parsimony and discrete traits dominate, which sets limitations for the downstream role cultural phylogenies, once derived, can play in more elaborate analytical pipelines. Moreover, the use of discrete character traits in these efforts prevails, which in turn sets a number of non-trivial challenges. Recent methodological advances in Bayesian phylogenetics, however, now allow us to infer evolutionary dynamics using continuous characters. Capitalizing on these developments, we here present an exploratory analysis of cultural macroevolution of projectile point shape evolution in the European Final Palaeolithic and earliest Mesolithic (~15,000-11,000 BP) using a Bayesian phylodynamic approach and the fossilised birth-death process model. This model-based approach leaps far beyond the application of parsimony, in that it not only produces a tree, but also divergence times, and diversification rates, while incorporating uncertainties. This allows us to compare rates to the pronounced climatic changes that occurred during our timeframe. While common in cultural evolutionary analyses of language, the extension of Bayesian phylodynamic models to archaeology represents a major methodological breakthrough.

**Speaker's social media:**

Twitter: @david_matzig

RECORDING - A macroevolutionary analysis of European late upper palaeolithic stone tool shape using a Bayesian phylodynamic framework

**Abstract:**

Social learning is a key driver of the success of the human species (Henrich, 2015), enabling the preservation of technologies that individuals could not possibly have acquired on their own (Tennie, Call, & Tomasello, 2009). Presently, we are witnessing the rise of intelligent machines – “AI systems” – that are capable of making discoveries beyond the human horizon (Wang et al., 2023). This raises the question of whether social learning enables humans to also adopt and preserve inconceivable discoveries made by intelligent machines. In our behavioral experiment, participants’ success in a strategic task hinged on adopting a solution that humans struggle to discover due to a characteristic bias to avoid large losses. We contrast populations of humans transmitting solutions over multiple generations on their own with populations that had the opportunity to learn from AI agents in their first generation. Our findings show that an algorithmic model succeeds in discovering the adaptive strategy where humans fail on their own. Most importantly, humans are able to adopt this machine-introduced strategy and preserve it over multiple generations. Taken together, our results showcase the potential of intelligent machines to transform human cultural evolution, and an opportunity for designing them towards benefitting human progress.

**Speaker's social media:**

Twitter: @ThomasF_Mueller
Bluesky: @thomasfmueller.bsky.social

RECORDING - Humans can learn and culturally preserve inconceivable strategies discovered by intelligent machines

**Abstract:**

Cumulative cultural transmission preserves traditional knowledge and practices for generations, yet this phenomenon is largely understudied in Asian artistic traditions. Our talk aims to describe the cultural transmission process within the miniature painting community in Rajasthan, India. The apprenticeship model in miniature painting provides a unique context for studying how the ritualization of acquiring mastery manifests through procedural rigidity, conformity, and religious integration. Artists (N=262) ranging from novices to experts were interviewed to examine the extent to which the acquisition of mastery is ritualized. The results revealed that novice artists adhere more closely to the chronological order of prescribed steps in producing miniature paintings than their more expert counterparts. Most artists agreed that novices should conform to traditional aesthetics more than experts. Mastery of technical skills and extensive knowledge of culture and religion were deemed essential for artists before engaging in innovation. We also found the process of miniature painting to resemble a religious act, with its emphasis on customs, purity, and reverence. Our research highlights the function of ritualization in learning Indian miniature painting. Ritualization ensures the preservation of distinctive aesthetics, uniformity in learning, and the integration of spiritual and religious beliefs in the cultural transmission of traditional arts.

**Speaker's social media:**

Twitter: @Faiz04Hashmi
LinkedIn: linkedin.com/in/faiz-hashmi-99b40078/

LIVE - The functions of ritualization in acquiring mastery: Cultural transmission of Indian miniature paintings

**Abstract:**

Context
The cultural evolution of quantification systems is a recent phenomenon traced back to the Neolithic. Yet, research shows that basic mathematical ideas are part of human core knowledge and likely developed early in evolution. How such core knowledge gave way to complex, culturally diverse mathematical ideas is not fully understood. 
Purpose
This paper focuses on the potential mathematics implicit in material culture, including finished artefacts, the production of tools, and processing of materials, using fibre technologies as a prime example of how material culture preserves, transmits, and transforms mathematical knowledge. 
Methods
A comparative approach linking the ethnomathematics of fibre technologies to cognitive research and relevant archaeological evidence of string, basketry, and weaving, provides a way of testing hypotheses on the origins and development of mathematics in the remote past.
Key results
Results support the premise that weaving practices contain and afford operations such as geometry, measurement, ordering, grouping, and counting. Fibre technologies likely provided a context for the development of mathematical ideas over the past 100,000 years, intensifying during the Upper Palaeolithic. 
Implications
Archaeological material culture is an underused resource that can inform about the development of mathematical knowledge prior to the emergence of formal systems of quantification. 

**Speaker's social media:**



LIVE - The cultural evolution of mathematics through fibre technologies

**Abstract:**

Human technology is an open-ended system that increases in diversity and complexity over time. Explanations of open-endedness normally appeal to the idea of cumulative cultural evolution (CCE) and the ability to accumulate increasingly effective solutions. Less attention is paid to the search spaces in which cultural evolution unfolds and how changes to these spaces induce open-ended growth. Adopting a macrolevel modelling approach, where cultural evolutionary dynamics are simulated as two interacting processes of optimization (where populations generate and filter for increasingly effective technologies) and discovery (where populations change their existing search space in an effort to seek out new configurations), we investigate the situations in which open-ended CCE does and does not emerge. A key finding is that populations routinely end up in optimization traps: here, the long-term dynamics gravitate towards an equilibrium state, failing to exhibit open-ended growth. Escaping these optimization traps requires populations to reach a balance between optimization and discovery, whilst accelerated growth in complexity only occurs when populations engage in directed expansion of the search space. Such findings suggest that discovery dynamics, and the ability of populations to change the underlying search space, are a fundamental limiting or enabling constraint on the open-ended evolution of technology.

**Speaker's social media:**

@replicatedtypo

LIVE - Minimal requirements for the open-ended cultural evolution of technology

**Abstract:**

Mathematical and computational approaches have made important contributions to cultural evolutionary theory by highlighting the effect of factors such as population size, social network structure, and transmission fidelity on cultural evolution. However, some cultural phenomena have so far been more difficult to capture using agent-based and formal models. In particular, determining how personality, background, and cultural context shape how individuals select, transform and transmit social information has remained out of the reach of traditional modelling approaches, and has thus mainly been studied empirically. We here propose that leveraging the capacity of Large Language Models (LLMs) to “role-play” and impersonate specific profiles may be fruitful to address this gap. Indeed, these generative models have been found to exhibit quite satisfying accuracy and relatively good consistency when used to simulate different characters. We present a framework for simulating and analyzing cultural evolution in populations of role-playing LLMs, along with results from use-cases that illustrate the kind of hypotheses that can be generated using this approach. The software we developed for conducting these simulations is open-source and features an intuitive, code-free user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence.

**Speaker's social media:**

@Jeremy__Perez

LIVE - Extending cultural evolutionary theory to machines

**Abstract:**

The emergence of human societies with complex language and cumulative culture is considered a Major Evolutionary Transition. Yet why a similar transition has not occurred in other species is perplexing, given the potential fitness advantages of accumulating culture. Here, Boyd & Richerson’s ("Why culture is common, but cultural evolution is rare," 1996) discrete-cultural-trait model is extended to incorporate arbitrarily strong selection; conformist, anti-conformist and unbiased frequency-dependent transmission; random and periodic environmental variation; finite population sizes; and multiple 'skill levels.' From their infinite-population-size model with success bias and a single skill level, Boyd and Richerson concluded that social learning is favoured over individual learning under a wider range of conditions when social learning is initially common than initially rare. We find that this holds only if the number, n, of individuals observed by a social learner is sufficiently small. However, with a finite population or a combination of success-biased and conformist or unbiased transmission, this result holds with larger n. Assuming social learning has reached fixation, the increase in a population’s mean skill level is lower with smaller populations and frequency-dependent transmission. Hence, multiple barriers to cultural accumulation may explain its rarity.

**Speaker's social media:**

@KaledaDenton

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RECORDING - A macroevolutionary analysis of European late upper palaeolithic stone tool shape using a Bayesian phylodynamic framework

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Next from Cultural Evolution Society Conference 2024

RECORDING - A macroevolutionary analysis of European late upper palaeolithic stone tool shape using a Bayesian phylodynamic framework

Similar lecture

Introductions

Downloads