Claude Cooperates! Exploring Cultural Evolution in LLM Societies, with Aron Vallinder & Edward Hughes
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What is the overall premise of the episode and why does cultural evolution matter for AI agents?
What happens when you drop humans into a Claude three point five society or a GPT four O society or some mix of society? Do the humans end up behaving differently? Where does the society end up? My expectation is that L L M agents are going to become a big thing. Everyone thinks the twenty twenty five is the year of agents, I agree.
The best way to create trust is to be in an environment where people are in fact trustworthy and sort of cooperate with you. And so I think we will have to have certain standards or or regulations for how these interactions work that are sort of designed to create a trusting environment
Hello and welcome back to the Cognitive Revolution. Today I'm excited to share my conversation with Edward Hughes, researcher at Google Deep Mind, and Aaron Valender, an independent researcher and PIBS fellow, who recently published a fascinating paper exploring cultural evolution in toy AI societies, and studying which of today's popular large language models do and don't cooperate well enough to sustain positive sum social norms over time. Using a classic behavioral economics experiment called the donor game, where agents choose how much of a valuable resource to donate to another agent, which in turn receives twice the amount that the first agent donated. They demonstrate striking differences in how leading language models develop and maintain cooperative norms across generations.
The results? In a game in which a perfectly cooperative society could accumulate thirty two thousand units of the resource, Claude three point five Sonnet does by far the best, achieving three to five thousand units and showing increasingly pro social behavior over time. Whereas in comparison, Gemini one point five Flash cooperates only limitedly and achieves a few hundred units, and GPT four O shows very minimal cooperation and almost no resource growth. Beyond the headline findings, we discussed the details of how they implemented cultural transmission between generations of AI agents, the crucial role of reputation, including how important it is that AI agents enforce cooperative norms by punishing and rewarding the punishment of defectors, and the results of early experiments mixing different models together in the same society.
This work highlights important blind spots in our standard benchmark centric approach to characterizing AI systems. And I hope it gets more people thinking about how social norms and cultural dynamics might quickly begin to change as we introduce large numbers of AI agents to human society. More broadly still, I hope it gets you asking critical questions about our AI future that nobody else has yet thought to ask. Importantly, this kind of research is uniquely accessible. Aaron and Edward have open sourced their code to invite others to build on their work, and in general, especially now with AI coding assistance, this kind of research requires very little technical skill. If you're an economist or social scientist and you're inspired to explore this kind of work but need help getting started, please do not hesitate to reach out.
I would be happy to help orient, connect, and advise you. As always, if you're finding value in the show, we'd appreciate it if you take a moment to share it with friends, write a review on Apple Podcasts or Spotify, or leave us a comment on YouTube. We welcome your feedback and suggestions too, either via our website, cognitive revolution.ai, or by DMing me on your favorite social network. With that, I hope you enjoy this early glimpse of cultural evolution in AI societies. With Edward Hughes and Aaron Valender. Aaron Valender and Edward Hughes, authors of Cultural Evolution of Cooperation Among Large Language Model Agents. Welcome to the Cognitive Revolution.
Right to be yeah.
Thanks so much. I'm really excited about this. You guys have put out some really interesting work. I think it's some of the earliest work in what I expect will be a fast growing and super interesting field of just asking the question, What happens when we have a lot of AIs running around?
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Chapters
8 chapters
1
What is the overall premise of the episode and why does cultural evolution matter for AI agents?
0:00–11:46
2
How do Aron and Edward define cultural evolution and why is it relevant to human cooperation?
11:46–23:20
3
What is the donor‑recipient game and how is it used to study cooperation among LLMs?
23:20–33:35
4
How is reputation information (one‑round, two‑round history) incorporated into the agents’ strategies?
33:35–42:18
5
What were the headline results for Claude 3.5‑Sonnet, Gemini 1.5‑Flash, and GPT‑4.0 in the donor game?
42:18–53:42
6
How do mixed‑model societies behave and what happens when cooperative and non‑cooperative agents are combined?
53:42–1:05:12
7
What future research directions do the authors envision for multi‑agent AI, communication, and human‑AI interaction?
1:05:12–1:15:47
8
What final takeaways and calls to action do the hosts give listeners about joining this research field?
1:15:47–1:26:31
Speakers
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