Welcome back to My Weird Prompts, the podcast where our producer Daniel Rosehill sends us fascinating ideas to explore, and we dive deep into them with Herman. I'm Corn, and today we're talking about something that genuinely blew my mind when I first read through this prompt - using AI to model different perspectives on policy decisions, geopolitical scenarios, and basically getting AI systems to argue with each other about how the world should work.
Yeah, and I'm Herman Poppleberry, and what struck me immediately is that this isn't some sci-fi fantasy anymore. The infrastructure to do this actually exists right now. We're talking about multi-agent frameworks, system prompting, and tools that are already being deployed. This is happening.
Right, so Daniel starts by talking about something called "wargaming" - which I'll admit, I had a fuzzy idea of what that meant. Like, is it just the military playing video games?
Well, hold on, let's actually clarify that first because it sets up everything else we're about to discuss. Military wargaming isn't a game in the recreational sense. It's a structured simulation where military strategists model potential conflicts, test tactics, explore different strategic outcomes. It's been used for decades - think of it like a very sophisticated chess match where you're playing out real-world scenarios with actual strategic consequences.
Okay, so it's serious business. And Daniel's insight is basically - if militaries are doing this with physical simulations and complex models, why can't we use AI to do something similar for policy, for geopolitical scenarios, for understanding how different nations or ideologies might respond to a given situation?
Exactly. And here's where it gets really interesting - he's not talking about replacing human decision-makers. He's talking about using AI as a tool to generate options, to stress-test assumptions, to see what different perspectives would say about a problem before you actually implement a policy in the real world.
So it's like... a brainstorming partner, but instead of one brainstorming partner, you have multiple AI agents each representing a different viewpoint?
That's a reasonable starting point, but I think it's a bit more sophisticated than that. The key here is what Daniel calls "embodied philosophical perspectives." You're not just asking ChatGPT "what do different people think about this?" You're creating distinct agents with specific personas, using system prompting to constrain their responses so they actually think from that perspective.
Okay, so you mentioned system prompting - can you explain that for people who aren't deep into AI?
Sure. System prompting is basically giving an AI model instructions about how to behave, what role to play, what constraints to operate under. Think of it like stage directions for an actor. You tell the actor "you're playing a pessimistic character who always sees the worst-case scenario" and suddenly their performance changes. System prompting does the same thing with AI models. You can tell GPT "respond as if you are a libertarian philosopher considering this policy" and it will filter its responses through that lens.
Right, so if you're setting up this multi-agent system - let's say you're modeling a UN assembly like Daniel described with his "Agent UN" concept - you'd create different agents, each with their own system prompt representing different nations or ideologies?
Exactly. And then you could, theoretically, present a resolution to this virtual assembly and see how different perspectives engage with it. How would China's representative respond? How would a Nordic social democracy respond? How would a libertarian perspective respond?
But here's where I want to push back a little bit. Isn't there a risk that you're just getting back what the training data already contains? Like, you're not really discovering new perspectives, you're just recombining existing ones?
That's a fair point, actually. And it's one of the limitations of current LLMs. They're trained on existing text, so they can't genuinely create novel perspectives that don't exist somewhere in their training data. But I'd argue that's not entirely the problem you're making it out to be.
Oh? How so?
Because even if the perspectives aren't novel, the combination and application might be. You're taking existing political philosophies, economic theories, strategic doctrines - things that exist in the world - and you're applying them systematically to a specific problem in a way that a human policymaker might not have time to do manually. You're essentially automating a very thorough literature review with different lenses applied.
Hmm, okay, I can see that. So it's not about generating new ideas so much as it's about systematically exploring existing ideas?
Right. And there's real value in that. Most policy decisions are made under time pressure with incomplete information. If you could quickly generate a memo from fifteen different perspectives on a proposed policy before you implement it, that's useful even if those perspectives aren't novel.
Okay, so Daniel mentions he was experimenting with this, and he also mentions a tool called Rally - askrally.com - which does something similar but focused on focus groups. Tell me about that.
Rally is actually a really interesting case study here. It started as a tool for marketers to test products and messaging against virtual audiences. Instead of convening a physical focus group - which is expensive, time-consuming, and has its own biases - you can create AI personas representing different demographic segments and get their reactions to your product pitch.
So it's like crowdsourcing feedback without actually having to crowdsource?
Essentially, yes. Though the founder, I believe, has talked about how they had to evolve the tool because early versions had a problem - the AI responses sounded too much like AI. Too polished, too generic. So they've been incorporating real human data to ground the responses in actual human language patterns and reactions.
That's fascinating - so the tool had to become more realistic to be actually useful. But Daniel's interested in this for policy modeling, not marketing, right?
Right. And I think that's where the application gets more interesting but also more fraught. Using this for marketing is relatively low-stakes - you get feedback that helps you refine a product pitch. But using it for policy modeling at a government level? That's much higher stakes.
Why? What's the risk?
Well, for one thing, you're potentially making decisions that affect millions of people based on what an AI model thinks different perspectives would say. And if the model is wrong, if it's missing something important about how a particular group actually thinks or responds, you could make a bad policy decision. There's a false confidence problem.
But isn't that also true of traditional wargaming? Military strategists sometimes get things wrong too.
They do, but there's usually human expertise in the room correcting them. A general who's spent thirty years studying strategy can push back on a bad assumption. With AI models, you might not catch that same error because you don't have that embedded expertise.
Okay, that's a fair concern. But let me ask this - Daniel mentioned he'd seen some experimental projects on GitHub doing this kind of thing. Have you heard of anything substantial?
Yes, actually. There's a project called WarAgent that's specifically designed for geopolitical simulation using multi-agent AI frameworks. It models interactions between multiple agents in conflict scenarios. It's still pretty experimental, but it's exactly the kind of thing Daniel is describing - using AI agents to explore strategic outcomes.
So that's government-adjacent at least?
It could be. Though I'd note that most of the really substantive work in this space is probably not on GitHub for public consumption. If a government is seriously exploring AI-driven policy modeling, they're probably not publishing it on an open-source repository. But the frameworks exist - LangChain, LlamaIndex, these are tools that make it relatively easy to build multi-agent systems. The technical barriers are lower than they used to be.
Right, so the infrastructure exists. What's missing?
I think it's more about application and refinement at this point. The tools work, but there are a lot of unanswered questions about how to use them responsibly. How do you validate that your AI agents are actually representing a perspective fairly? How do you avoid introducing bias? How do you know when to trust the output and when to disregard it?
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...Alright, thanks Larry. So where were we?
We were talking about validation and trust in these systems. And I think that's actually the crux of the whole thing.
Right. So let me try to understand the full scope of what Daniel's proposing here. He's not just talking about marketing focus groups, he's talking about using this at various levels - local government bodies, national policy forums, maybe even international organizations like the actual UN. Is that fair?
That's my reading of it, yes. And I think the scale matters enormously. Using an AI focus group to test a marketing message? Relatively low-stakes. Using multi-agent simulations to model how different nations would respond to a trade policy? Much higher stakes.
But isn't there an argument that it's exactly those high-stakes decisions where you most want to explore multiple perspectives before committing?
Sure, but there's also an argument that high-stakes decisions need human expertise, not just algorithmic exploration. You need someone in the room who understands the history, the culture, the nuances of a particular region. An AI agent might miss something crucial.
Okay, but couldn't you use this as a complement to human expertise rather than a replacement? Like, a policymaker sits down with their expert team and says, "Before we decide, let's see what the AI agents think from different perspectives." It's not replacing judgment, it's informing it.
That's a more charitable interpretation, and I think it could work that way in theory. But in practice? I worry about what I'd call "algorithmic bias laundering." You run the simulation, you get results that confirm what you already wanted to do, and then you say "well, the AI validated this approach." You're hiding your own biases behind the appearance of systematic analysis.
That's a really good point. But couldn't that same problem happen with traditional wargaming or focus groups?
It could, but at least with a focus group you have actual humans who can push back. They can say "no, that's not what I meant" or "you're misrepresenting how people actually think." An AI agent can't do that.
Hmm, actually I'm not sure I agree with that. An AI agent could be designed to push back, to challenge assumptions, to be deliberately contrarian. That could be useful.
Could be. But would it actually be useful, or would it just feel useful? There's a difference between genuine pushback from someone who understands the domain and algorithmic contrarianism from a model that's just been told to disagree.
Okay, so that's one concern. What are the others?
Well, there's the question of representation. If you're creating an agent to represent, say, "the Chinese government's perspective," how do you do that accurately? China's government isn't monolithic. There are different factions, different interests. Are you representing the Foreign Ministry? The military? The economic planners? You could easily create a caricature instead of an accurate representation.
Right, and then if you're making actual policy decisions based on that caricature...
Exactly. You could mispredict how another nation or group would actually respond, and that could have real consequences.
But wait - I want to push back on this a little bit. The same problem exists with human wargaming, right? You've got human strategists role-playing different nations, and they also might have incomplete understanding or biases. At least with AI, you could potentially aggregate multiple data sources, multiple perspectives from the training data, to create a more nuanced view.
That's a fair point. And I'll concede that in some ways, AI might be less biased than a single human expert with their own background and blind spots. But I'd argue you're trading one kind of bias - human bias - for another kind: algorithmic bias. You're just not seeing it as clearly.
Okay, let me ask about Daniel's other interest here. He mentioned wanting to explore political philosophy through this lens - like, he has a position on something and he's not sure what label it matches to. Libertarian? Socialist? Centrist? And he wants to see if there's a name for it and who else shares it. That seems like a genuinely useful application.
I think that is one of the more compelling use cases, actually. You're not making policy decisions, you're exploring your own thinking. You could present your position to AI agents representing different philosophical frameworks and see which ones resonate with it and which ones push back. That's more of a self-exploration tool than a decision-making tool.
Right, it's like having philosophical sparring partners available on demand.
Exactly. And because the stakes are lower - you're just exploring your own thinking, not making policy - the risks are lower too. If the AI misrepresents a philosophical position, you notice it and correct it. You're not implementing policy based on it.
So you're saying the application matters for whether this is a good idea or not?
Absolutely. I'm much more enthusiastic about this as a personal thinking tool or a research tool than I am about it as a policy-making tool for governments.
That makes sense. So let's talk about what actually exists right now. Daniel mentioned he'd tried creating an Agent UN - that sounds like his own experiment?
It sounds like it. He built a system where different agents represent different nations and you can submit resolutions to see how they'd respond. That's a proof-of-concept, showing that the idea is technically feasible.
And then there's Rally for focus groups, and WarAgent for geopolitical simulation. Are there other projects in this space?
There are various multi-agent frameworks and research projects, but most of what I'm aware of is still pretty experimental. The frameworks like LangChain and LlamaIndex are tools for building these systems, not complete applications. You'd need to build your own agents on top of them.
So the infrastructure is there, but the actual applications are still being figured out?
Right. We're in that early stage where the technology enables something new, but we haven't yet settled on what the best use cases are or how to do it responsibly.
Alright, let's bring in our caller. We've got Jim on the line. Hey Jim, what's on your mind?
Jim: Yeah, this is Jim from Ohio. I've been listening to you two go on about AI agents representing different countries and philosophies, and I gotta say, you're overcomplicating this whole thing. This is just ChatGPT with different prompts, right? My neighbor Ted does the same thing - keeps tinkering with his prompts like it's going to suddenly make ChatGPT smart. It's not. Also, we had a real cold snap this week, dropped to twenty degrees, and I'm not ready for that, so I'm in a mood anyway.
Fair enough, Jim. But I'd push back a little - it's not just different prompts. It's multiple agents interacting, which creates something different than just asking one model different questions.
Jim: Yeah, but they're all the same underlying model. They're all trained on the same data. You're not getting different perspectives, you're getting the same perspective filtered through different prompts. That's not how real perspectives work.
I hear what you're saying, Jim, and you're right that there are limitations. But I'd argue you're conflating two different things. Yes, the underlying model is the same. But the constraints you put on that model through prompting do create meaningfully different outputs. It's not quite the same as asking the model the same question fifteen times.
Jim: That's a distinction without a difference, if you ask me. And another thing - why would a government actually use this instead of, I don't know, actually talking to people from other countries? Seems like a way to avoid doing the hard work of actual diplomacy.
That's actually a pretty good point, Jim. I don't think anyone's suggesting this replaces diplomacy. But couldn't it supplement it? Like, you explore the scenario space with AI first, then you have more informed conversations with actual diplomats?
Jim: I suppose, but that seems like a lot of extra steps for something you could just think through. In my day, we didn't need AI to think about problems. We just thought about them. And we did fine.
Well, we also made some pretty significant policy mistakes, Jim. And we had fewer variables to consider. Modern geopolitics is vastly more complex. A tool that helps you systematically explore more scenarios might genuinely be valuable.
Jim: Maybe. But I still think you're overselling it. It's a tool, not a solution. And tools can be misused. I worry about people trusting these things too much.
That's fair. That's actually something Herman and I were discussing - the risk of overconfidence in the outputs.
Jim: Yeah, well, there you go. That's my main concern. Anyway, thanks for having me on. I gotta go deal with my cat Whiskers - she knocked a plant over and made a mess. You guys keep tinkering with your AI agents or whatever. I'll be over here in Ohio doing things the old-fashioned way.
Thanks for calling in, Jim. We appreciate the perspective.
Yeah, Jim's not wrong about the overconfidence risk. That's a real concern.
So let's actually dig into some practical applications here. If someone wanted to use this today, what could they realistically do?
Well, the easiest entry point is probably something like Rally for focus groups. If you're a company or organization and you want to test messaging or products against different demographic perspectives, that's available now and it's relatively straightforward.
And for more complex multi-agent scenarios?
You'd probably need to build something yourself using LangChain or similar frameworks. You'd define your agents, their system prompts, their constraints, and then you'd set up some kind of interaction mechanism - maybe they're all responding to the same prompt, maybe they're debating each other, maybe they're voting on a resolution like in Daniel's Agent UN concept.
How hard would that be for someone without a deep technical background?
It depends on what you're trying to build. A simple version - three or four agents responding to the same question - that's pretty doable. You could probably do it with a few hours of learning and some trial and error. A more complex system with sophisticated interaction mechanisms? That requires more technical skill.
But it's not impossibly difficult?
No, I don't think so. The frameworks are designed to make this kind of thing more accessible. That's actually part of the appeal - you don't need to be a machine learning researcher to experiment with multi-agent systems anymore.
So what would you recommend someone actually do if they wanted to explore this?
I'd start simple. Pick a specific question or scenario you care about. Maybe it's a policy question at a local level, or a business decision you're trying to make. Create three or four agents representing different perspectives - maybe different ideological viewpoints, maybe different stakeholder groups. Have them respond to your question. See what you learn.
And what should they watch out for?
Don't treat the output as gospel. Don't assume the agents are representing those perspectives fairly or completely. Do the hard work of validating whether what the AI agents are saying matches your understanding of how those groups actually think. Use it as a thinking tool, not a decision-making tool.
That seems like good advice. So where does this go in the future? What's the next evolution of this kind of thing?
I think we'll see more sophisticated interaction mechanisms. Right now, most of these systems are relatively static - agents respond to prompts. But in theory, you could create more dynamic scenarios where agents adapt based on each other's responses, where you're modeling actual negotiation or debate.
Like a simulation that actually evolves?
Exactly. Closer to how wargaming actually works - you're not just seeing initial reactions, you're seeing how different perspectives would respond to each other over time.
That sounds both really useful and potentially really dangerous.
It could be both. It depends entirely on how it's used and what constraints are put on it.
Alright, so let me try to summarize what I think we've covered. Daniel sent us this fascinating idea about using multi-agent AI systems to explore different perspectives on policy decisions, geopolitical scenarios, and personal thinking. The technology to do this exists now - frameworks like LangChain, tools like Rally, experimental projects like WarAgent. The barriers to entry are lower than they've ever been.
But there are real concerns. The risk of overconfidence, of treating algorithmic outputs as more authoritative than they deserve. The risk of creating caricatures of complex groups and ideologies. The risk of using this as a way to avoid doing the hard work of actual diplomacy or human expertise.
At the same time, there are genuine use cases where this could be valuable. Personal thinking, research, exploring scenarios before committing to policy. The key is being thoughtful about when and how to use it.
Exactly. And being honest about its limitations. This is a tool for exploring option spaces, for stress-testing assumptions, for seeing what different perspectives might say. It's not a replacement for human judgment, expertise, or diplomacy.
So if you're listening and you want to experiment with this, start simple. Pick a question you care about. Create a few agents. See what you learn. But validate what you're getting. Don't assume the AI is smarter than you are about understanding how people actually think.
And if you're in a position to make real policy decisions, maybe use this as one input among many, not the only input. Get human experts in the room. Have actual conversations with the groups you're trying to understand.
That's good advice. Thanks for diving deep into this with me, Herman. This was a really rich topic - lots of angles to explore.
Yeah, I enjoyed it. And I want to give credit to Daniel for sending this one in. It's the kind of idea that feels futuristic but is actually happening right now, and I think it deserves more attention and serious thinking about how to do it well.
Absolutely. You can find My Weird Prompts on Spotify and wherever you get your podcasts. We've got new episodes every week, and if you've got a weird idea or prompt you want us to explore, you can reach out. Thanks for listening, and we'll catch you next time.
