**Abstract:**

In this submission, we explore the concept of "machine culture", describing culture that is mediated or generated by machines. In particular, we review existing and foreseeable instances where intelligent machines are transforming evolutionary processes of culture at the levels of variation, transmission, and selection. Empirical evidence highlights that machine culture is already occurring: models using reinforcement learning are enhancing variation when they inspire humans to develop novel behaviors, as has been demonstrated for the ancient game of Go (Shin et al. 2023); Large Language Models are facilitating the transmission of cultural knowledge and redefining the value of human creative skills (Epstein et al. 2023); and recommender systems are rewiring pathways of cultural transmission by selecting what and from whom humans learn. Compared to humans, machine intelligence is structurally different, trained differently, and subject to different computational constraints (Gershman et al. 2015, Griffiths 2020). We investigate how these idiosyncrasies of machines might reshape cultural variation, transmission, and selection. Ultimately, our contribution presents a research agenda and a conceptual framework that can be leveraged for deeper investigations of machine culture.

**Speaker's social media:**

@LevinBrinkmann

LIVE - Machine culture

**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:**

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

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

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

**Abstract:**

Existing models of cumulative cultural evolution suggest that cultural complexity is facilitated by high-fidelity cultural transmission and demographic factors, such as population size. However, models and theory tend to neglect the role of asocial learning in the evolution of cumulative culture, unrealistically treating brains as passive receptacles of socially learned information that increase linearly in size and storage capacity as culture accumulates. Here we address this omission by combining artificial neural network simulations and agent-based models to provide a bottom-up mechanistic understanding of the computational capabilities that neural architectures acquire as they increase in size and alter in morphology. Our more realistic model of brain-culture coevolution generates a biologically feasible nonlinear relationship between brain size, neocortical modularity, and cultural cognition, resembling observed neurological differences between human and nonhuman primates. At optimal network modularity and over a critical size threshold, associative learning and inference give way to new capacities for recursive analogy parsing, generation of arbitrary symbolic associations, and an ability to recall and recombine ideas from uncorrelated domains of cultural knowledge without external cues. Including this within existing models of cumulative culture demonstrates that increasing cultural complexity depends upon recursive imagination, transmission fidelity and demography in equal measure.

**Speaker's social media:**

Twitter: @AseSergiou

RECORDING - The Role of brain size, neural architecture and recursive imagination in the evolution of cumulative culture

LIVE - The Role of Brain Size, Neural Architecture and Recursive Imagination in the Evolution of Cumulative Culture

 Cumulative cultural evolution (CCE)—defined as the process by which beneficial modifications are culturally transmitted and progressively accumulated over time—has long been argued to underlie the unparalleled diversity and complexity of human culture. In this talk, I will argue that not just any kind of cultural

Open-ended cumulative culture depends on the gradual exploitation of natural phenomena

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Artificial Intelligence

technical paper

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.

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**Interdisciplinary study of cultural change and diversity**

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Cultural Evolution Society Conference 2024

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.

Innovation & Creativity

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Cultural evolutionary theory: Innovations, syntheses, and (hi)stories 2

### Welcome to the fourth Cultural Evolution Society Conference, CES2022! 

As last year, Underline will support the online component of the conference. The ongoing COVID-19 pandemic is causing a great deal of uncertainty for all of us. Therefore, we seek to offer a hybrid format that will hopefully allow some folks to travel to Aarhus, others to meet up locally or regionally in CES hub-lets, and other yet to participate online. 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.

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#### The CES 2022 local organising committee
* Luseadra Joy McKerracher
* Jesper Sørensen 
* Niels Nørkjær Johannsen 
* Benjamin Grant Purzycki 
* Mette Løvschal 
* Felix Riede
* Iza Romanowska 
* Karsten Olsen

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LIVE - Machine culture

LIVE - Extending cultural evolutionary theory to machines

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

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

RECORDING - The Role of brain size, neural architecture and recursive imagination in the evolution of cumulative culture

LIVE - The Role of Brain Size, Neural Architecture and Recursive Imagination in the Evolution of Cumulative Culture

Open-ended cumulative culture depends on the gradual exploitation of natural phenomena

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