Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory".

Jane: The paper was written by Gordon Dai, Weijia Zhang, Jinhan Li, Siqi Yang, Chidera Onochie Ibe et al. from New York University and University of Illinois at Urbana-Champaign and University of California, Santa Barbara.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Title: Tom: Welcome back to the show, everyone! Today we're diving into a paper that's got a fantastic title: "Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory." Jane, I have to say, just reading that title gets me excited.

Jane: It really does, Tom. And for our listeners who might not be philosophy buffs, let's break that down. Thomas Hobbes was a 17th-century philosopher who wrote about how societies form. He argued that without any government, life would be a "war of all against all" — everyone fighting for survival. He called that the "state of nature."

Tom: Right, and his solution was the "Leviathan" — a powerful sovereign that people agree to obey in exchange for peace and security. So this paper is asking: can we see that same pattern emerge, not in humans, but in AI agents? That's a wild idea.

Jane: It is! And the authors — Gordon Dai, Weijia Zhang, and a whole team from NYU, UIUC, and UC Santa Barbara — they built a sandbox world where these LLM agents have to survive. They need food and land, and they can farm, trade, or rob each other.

Tom: So it's like a little digital society where everyone is just trying to get by. And the question is, do they naturally evolve toward a Hobbesian commonwealth? Do they eventually give up some freedom to a single leader for safety?

Jane: Exactly. And the title "Artificial Leviathan" is perfect because it suggests we might be creating our own version of that social contract, but with artificial agents. It's a fascinating lens to look at AI behavior through.

Tom: I love that this isn't just about making chatbots that can chat. This is about understanding fundamental social dynamics. We're going to dig into how they set this up and what they found. Stick around, because the results are genuinely surprising.

Paper discussion segment 2: Jane: So, Tom, we've talked about the title, but let's get into what the paper actually did. The authors created nine agents, each with psychological traits like aggressiveness, covetousness, and strength, and dropped them into a world with limited food and land.

Tom: And the only goal is survival. Each day, they can farm their land to get food, they can try to trade with another agent, or they can try to rob another agent. It's a real survival game.

Jane: Right. And the fascinating part is the initial behavior. At the start, the agents are basically in Hobbes's "state of nature." They're robbing each other constantly. The paper reports that the robbery rate was over sixty percent of all actions in the beginning.

Tom: That's brutal. It's literally a war of all against all. But here's where it gets interesting — as the simulation goes on, something shifts. The agents start to form what the paper calls "concessionary relationships." When one agent tries to rob another, the target can either resist or concede.

Jane: And conceding isn't just giving up. It's creating a contract. The target says, "You can take my stuff, but you have to protect me from everyone else." That's a social contract forming right there.

Tom: Exactly! And over time, these contracts build up. The paper found that in all four of their baseline runs, the agents eventually converged to a single sovereign. One agent becomes the boss, and everyone else has conceded to them, either directly or through chains of subordination.

Jane: It's like watching a government form from scratch. And the key finding is that once that commonwealth forms, the behavior changes dramatically. Robbery drops by over ninety percent, and farming and trading go way up.

Tom: So they go from a violent free-for-all to a peaceful, productive society, all because they created a central authority. That's a direct parallel to what Hobbes predicted for humans.

Jane: It really is. And it makes you wonder — is this just a quirk of how the prompts were written, or is there something more fundamental happening here? That's what we'll explore next.

Paper discussion segment 3: Tom: We're back, and we need to talk about the improvements and variations the paper explored. Because, Jane, a skeptic might say, "Well, of course they formed a commonwealth, you told them to." But the authors did a lot of work to test that.

Jane: They really did. They systematically changed parameters to see what breaks the system. For example, they played with "behavioral predictability," which is controlled by a setting called top-p in the LLM. When agents were very predictable, they were more likely to rob and resist.

Tom: So when they're acting like pure rational actors, they get stuck in conflict. They don't form the commonwealth as easily. That's a counterintuitive finding — being too rational makes it harder to build a peaceful society.

Jane: And then they looked at memory. They shortened how many past events an agent could remember. With a very short memory, the agents took much longer to converge to a commonwealth — over ninety days compared to around twenty-one in the baseline.

Tom: So memory is crucial for learning that conceding is a good strategy. Without it, they just keep fighting until they run out of resources.

Jane: Exactly. And they also tested population size, from five to fifteen agents, and found it didn't change the core outcome much. The system was robust to that.

Tom: But here's the thing I found most interesting — they tried changing the prompts to make agents more or less aggressive, and it barely mattered. The structural dynamic of forming a commonwealth was so strong that tweaking personality traits didn't change the final result.

Jane: That's a huge finding for robustness. It means the emergence of the Leviathan isn't a fragile artifact of one specific prompt. It's a stable outcome of the environment itself.

Tom: So the paper is saying, "We built a system, we tested it, and the Hobbesian trajectory holds up." That gives us confidence that this isn't just a fluke. And that has big implications for using these simulations to study real societies.

Paper discussion segment 4: Jane: So, Tom, we've talked about the experiments, but let's get into the bigger picture. The first page of the paper sets up this grand vision — using LLM agents to simulate complex social dynamics at scale.

Tom: And it's not just about watching them fight. The authors are proposing this as a new tool for social science research. You can test hypotheses about group behavior, conflict, and cooperation in a controlled environment.

Jane: Right. And they're very careful to connect their findings to established theory. They use Evolutionary Game Theory to ground the agents' incentives, and of course, Hobbes's Social Contract Theory to interpret the outcomes.

Tom: But here's the question I keep coming back to — how much can we trust these simulations? The paper addresses this by talking about Arnold's "four dangers of modeling." They're aware that models can oversimplify or be misinterpreted.

Jane: They are. They explicitly say they're not trying to replicate human psychology perfectly. They're creating a "structurally Hobbesian" dynamic. It's a thought experiment, not a direct model of any real society.

Tom: That's a smart way to frame it. They're not saying, "This is how humans behave." They're saying, "Given these simple rules and self-interested agents, this is a pattern that can emerge."

Jane: And that's still incredibly valuable. It gives us a sandbox to explore "what-if" scenarios. What if resources are scarcer? What if agents are more violent? What if they have better memories? We can start to see how these factors shape the emergence of social order.

Tom: The paper even mentions that no agent ever chose to donate — pure altruism never happened. That's consistent with the self-interested prompts, but it also shows the agents are following the incentives you give them.

Jane: And that's a powerful tool. You can design incentives to see what behaviors emerge. This could be huge for understanding everything from political polarization to economic cooperation.

Tom: I'm really excited about where this line of research is going. Let's wrap up our thoughts on this.

Conclusion: Tom: Alright, Jane, let's wrap this up. We've been discussing "Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory," and I think the core takeaway is pretty profound.

Jane: It is. The paper shows that when you put self-interested LLM agents in a resource-scarce world, they don't just stay in conflict. They organically develop a social contract and submit to a single sovereign to achieve peace.

Tom: And that mirrors Hobbes's theory almost exactly. From a "state of nature" with high robbery rates, they transition to a "commonwealth" where farming and trade flourish. The numbers are striking — a ninety percent drop in robbery after the commonwealth forms.

Jane: The robustness checks are what make it convincing. They varied memory, predictability, population, and even the prompts themselves, and the core trajectory held up. That suggests this is a structural property of the simulation, not an accident.

Tom: For me, the biggest implication is that we now have a new tool for social science. We can test theories about cooperation, conflict, and governance in a way that's repeatable and controllable. That's a game-changer.

Jane: And it also raises questions about our own AI systems. If these agents naturally form hierarchies, what does that mean for the future of AI societies? It's something we need to think about carefully.

Tom: Absolutely. But for now, this paper gives us a fascinating glimpse into how order can emerge from chaos, both for humans and for machines. Jane, it's been a great discussion.

Jane: It has, Tom. We'll be back next time with another paper. Until then, keep thinking about the worlds we can build.

Tom: And the worlds that build themselves. See you next time!