**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:**

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

In cultural evolution, solutions have always been found by humans and transmitted to other humans. Advances in artificial intelligence have created systems that appear to bypass human cultural evolution. AlphaZero beat Go masters without learning from human play. Consequently, the behavior demonstrated by such algorithms can avoid the biases shown by humans and maintained via social learning. In this work, we explore the hypothesis that humans can learn novel strategies from algorithms via social learning and that these strategies can be further culturally transmitted to other humans. 
We ran a preregistered online transmission chain experiment where we mix human and machine players and measure the performance of human-only and hybrid chains. We use a planning task where humans are known to show a suboptimal decision strategy. Participants solved the task in chains, with each participant seeing the solution of the previous participants. In half of the chains, we replaced a single participant with an algorithm that used a strategy opposite to humans. We found that people immediately playing after the algorithm benefited from it, but people further down the chain reversed back to their biased strategy and lost what was gained via hybrid social learning. We discuss the boundaries of successful hybrid social learning and the possible mechanisms behind our findings.
We address with this work a novel hypothesis in cultural evolution, namely that artificial agents can actively contribute to the discovery of new solutions and we present transmission chains as a tool to investigate hybrid cultural transmission.

Hybrid social learning in human-algorithm cultural transmission

Please note that for those who submitted a pre-recorded video, we have posted it along with a live recording.

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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#### Durham Cultural Evolution Research Centre (DCERC) 
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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.

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**Paper ID:** AB00100        
**Paper title:** Apparently complex re-combinative culture can arise from simple mechanisms in wild birds        
**Author:** Lucy Margaret Aplin      
**Time:** 15:10 - 15:20 (CEST)  

**Paper ID:** AB00194      
**Paper title:** Serial Reproduction of Narratives: Happiness and Sadness Survive While Coherence Suffers       
**Author:** Fritz Breithaupt       
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**Paper ID:** AB00073      
**Paper title:** Biases or balancing act? Investigating the flexibility of conformity      
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**Paper ID:** AB00087      
**Paper title:** Hybrid social learning in human-algorithm cultural transmission        
**Author:** Levin Brinkmann        
**Time:** 15:40 - 15:50 (CEST)  

**Paper ID:** AB00038       
**Paper title:** Cultural evolution of football tactics: strategic social learning in managers’ choice of formation       
**Author:** Alex Mesoudi       
**Time:** 15:50 - 16:00 (CEST)  

Q&A session 01: Social learning

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Levin Brinkmann

4

4

SHORT BIO

Presentations

LIVE - Machine culture

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

Hybrid social learning in human-algorithm cultural transmission

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