Imagine you are running a high-stakes wargame centered on a brewing conflict in the Middle East. You have got all your AI agents lined up to represent the different players. But here is the glitch: the simulation treats a small, local environmental NGO with the exact same strategic weight and decision-making influence as a nation-state with a nuclear arsenal. In the digital mind of the LLM, a press release from that NGO carries as much "signal" as a troop mobilization order from a central command. It sounds absurd, right? But as AI wargaming moves out of the purely academic "what if" space and into actual policy planning rooms, these seemingly small design flaws—like failing to account for unequal power—become massive real-world risks.
It is a fascinating technical paradox, Corn. We are finally at a point where we can simulate thousands of distinct perspectives simultaneously, which is a massive leap over traditional human-led wargaming. But the "more is better" approach hits a wall when you realize the real world doesn't operate on a flat plane of equality. If your model doesn't understand hierarchy and influence, the simulation just becomes noise. By the way, for the folks listening, today’s episode of My Weird Prompts is being powered by Google Gemini Three Flash.
So Daniel sent us this one, and it really cuts to the heart of that tension. He writes: When discussing the Iran-Israel policy simulation, we mentioned providing a list of actors to the agent. One of the main advantages of AI agents for geopolitical wargaming is their ability to synthesize or enact many perspectives. However, in the real world, all perspectives do not hold equal weight, and even defining an exhaustive list of actors is impossible. How do we account for these limitations in experiment design?
Herman Poppleberry here, and I have been itching to dive into this one because it touches on the fundamental "alignment" problem between digital logic and geopolitical reality. Daniel is spot on. In a standard LLM environment, the attention mechanism—which is the "brain" of the model—is essentially democratic. It looks at all the tokens in the context window and tries to find patterns. It doesn't inherently know that the "President of the United States" token should carry ten thousand times more weight than the "Local Activist" token unless we explicitly build that architecture into the experiment.
Right, because if I am the AI, I am just looking at a big soup of text. If the activist writes a ten-page manifesto and the President sends a two-sentence tweet, the AI might actually spend more "intellectual energy," for lack of a better term, processing the manifesto. It is the "loudest voice in the room" problem, but automated.
Well, not exactly in the sense of agreement, but rather that you’ve hit on the core mechanism. The model is a pattern matcher. If you provide a list of forty actors and don't define the power dynamics, the model defaults to a sort of narrative equilibrium where everyone gets a turn to speak and every action has a proportional reaction. But geopolitics is the study of disproportionality. It is about the few deciding for the many.
And then you have the other half of Daniel's point: the "Exhaustive List" fallacy. You can never actually list everyone. If you try to include every proxy, every splinter group, every energy corporation, you end up with a context window that is ninety percent fluff. You’re basically trying to map the entire world at a one-to-one scale, which makes the map useless.
It’s the "noise floor" problem. The more actors you add to satisfy the need for "completeness," the more you dilute the critical signals from the players who actually move the needle. We saw this play out in some of the early twenty-five simulations where researchers tried to be too inclusive and the primary state actors ended up paralyzed by "analysis paralysis" from thousands of minor agent inputs.
It’s like trying to play chess, but every time you move a pawn, eighteen people in the audience get to vote on whether the pawn's feelings were hurt. It might be a more "complete" simulation of the room, but it’s a terrible simulation of a chess match. So, we've established the problem—the AI treats everyone as equals and we can't possibly list everyone anyway. Now, let's look at why this happens at a technical level and how the sheer math of these models contributes to the mess.
It really comes down to the architecture of the attention mechanism in these transformer models. If we’re looking at that Iran-Israel policy simulation Daniel mentioned, we might provide a list of twenty actors—everyone from the IRGC and the Israeli War Cabinet to smaller players like maritime insurance firms or regional energy regulators. The problem is that to an LLM, a list is just a list. It’s a sequence of tokens with roughly equal semantic weight unless the prompt engineering is incredibly sophisticated.
Right, the model doesn't have an internal "Power Meter" for each name on that list. If the maritime insurance firm sends a memo about rising premiums, the LLM might treat that with the same gravity as a direct threat of kinetic action from a state leader, simply because they both occupy the same amount of space in the context window. It's a flat hierarchy by default.
And that’s the real problem we’re trying to solve: how do you introduce gravity into a weightless environment? If the simulation treats every actor as a peer, you lose experimental validity because the feedback loops are all wrong. In the real world, a minor proxy group might take an action, but the "response" is filtered through the strategic priorities of the major powers. If the AI doesn’t understand that hierarchy, the simulation becomes a chaotic brownian motion of agents bouncing off each other rather than a directed geopolitical struggle.
It’s also a question of the "Known Unknowns." Daniel pointed out that an exhaustive list is impossible. Even if we could identify every single sub-national actor or influential billionaire, we can't actually model them all without hitting a massive wall of computational noise. By trying to be "complete," we might actually be making the simulation less accurate because we're forcing the AI to process a million variables that, in reality, are just rounding errors in the grand scheme of a high-stakes crisis.
The limitation matters because if the design is flawed, the "insights" we get out of the wargame are just artifacts of the model's democratic bias. We have to figure out how to tell the AI that in this specific sandbox, some voices are a shout and others are a whisper, and some voices aren't even in the room yet. Otherwise, we're just playing a very expensive game of digital make-believe that has no bearing on how a room full of generals or diplomats actually functions.
It’s the "Equal Voice" trap. If you’re a Large Language Model, your entire world is built on the attention mechanism, which is essentially a mathematical way of deciding which words in a sentence relate to each other. But here’s the kicker: the transformer architecture doesn’t inherently have a concept of "geopolitical power." To the model, the token for "United States" and the token for "Small Maritime Insurance Firm" are just vectors in a high-dimensional space. Unless we specifically architect the prompt to create a hierarchy, the attention mechanism distributes its "focus" across all listed actors somewhat democratically.
That is the technical root of the failure. In a transformer, every input token can potentially attend to every other input token. If you provide a list of forty actors in the system prompt for a wargame, the model is trying to calculate the relationships between all of them simultaneously. Without explicit weighting, the model might give the same "attention weight" to a minor NGO's press release as it does to a carrier strike group moving into the Persian Gulf. It’s a flat ontology. We saw a massive real-world example of this breaking down in the twenty twenty-five Brookings Institute simulation. They tried to be hyper-realistic by adding over fifty non-state actors—everything from local labor unions to international human rights groups—thinking more detail would equal better results.
And let me guess, it turned into a giant, digital town hall meeting where nothing actually happened?
Worse than that. It created so much "semantic noise" that the actual decision-makers—the ones with the tanks and the nukes—couldn't find a clear signal. Because the LLM was trying to synthesize fifty-plus perspectives, the "summary" of the geopolitical state became this diluted, beige slurry of opinions. The simulation ended in a stalemate not because of strategic brilliance, but because the model was overwhelmed by the sheer volume of "equal voices" it was trying to juggle. It’s what I call the Exhaustive List Fallacy. We think adding more actors makes it more "complete," but in reality, it just increases the computational overhead and introduces variables that would be irrelevant in a real-world Situation Room.
It’s the classic tradeoff between completeness and accuracy. If I’m a general, I don't care what the local artisanal sourdough guild thinks about a blockade. But if the AI is told they are an "actor" in the simulation, it feels obligated to process their "move." Human analysts are great at this because we have a built-in "relevance filter." We naturally prune the tree of possibilities. AI, by default, wants to climb every single branch at the same time.
And we have to remember the physical constraints of the context window. Even with a hundred twenty-eight thousand tokens, if you’re simulating fifty actors, each with their own history, goals, and current status, you’re eating up that space incredibly fast. You end up with "context thinning," where the model starts losing the nuance of the primary actors because it’s too busy remembering the third-order concerns of a minor proxy group. We're essentially forcing the AI to hallucinate a level of complexity that doesn't actually drive the geopolitical engine.
And that context thinning is where the real danger creeps in, right? Because if the "signal" of a nuclear superpower gets drowned out by the "noise" of thirty micro-actors, the simulation’s output isn't just cluttered—it’s fundamentally wrong. You end up with these cascading weighting errors. If the model thinks a regional trade union has the same escalation potential as a carrier strike group, the entire "game tree" starts branching in directions that defy geopolitical gravity.
It’s a literal collapse of strategic logic. When those weighting errors cascade, you get "hallucinated stability" or "hallucinated chaos." Policy-makers look at these results and might conclude that a certain provocation is safe because the AI showed fifteen different actors "balancing" the threat, when in reality, fourteen of those actors have zero kinetic capability. This is why the twenty-six DARPA-funded simulation was such a massive pivot. They moved away from the "flat list" approach entirely and implemented what they called Influence-Weighted Actor Selection.
Influence-weighted? So they basically gave the AI a "power score" for each player before the game even started?
In a sense, yes. They used a hierarchical actor modeling framework. Instead of one big pool of agents, they structured the simulation in tiers. Tier One actors—the primary nation-states—had the largest share of the context window and their "moves" were processed with higher priority. Tier Two and Three actors were only "activated" or sampled if a Tier One move directly triggered a relevant sub-routine. It’s a way of explicitly modeling the "known unknowns" by saying: "We know these minor players exist, but we aren't going to let them dilute the primary strategic logic unless it's necessary."
That sounds a lot more like how a human Red Team operates. You don't have fifty people in the room; you have five experts playing the big roles, and maybe one guy in the corner occasionally saying, "Hey, don't forget about the global oil markets."
That’s the exact shift. It’s moving from "simulate everything" to "simulate what matters." The DARPA team also used something called dynamic actor pruning. If an actor’s influence on the primary delta of the simulation dropped below a certain threshold over three "turns," the model literally archived that persona to free up tokens for the heavy hitters. It acknowledges that in a crisis, the list of who actually matters shrinks rapidly.
It’s the "Situation Room" filter. If you're not helping solve the immediate crisis, you're out of the room. But how do we actually design these experiments without baking in our own biases about who "matters"? Because if we prune the wrong actor, we’re just back to square one with a flawed simulation.
That’s the challenge. You have to include a sensitivity analysis protocol. You run the simulation with the "pruned" list, then you run a "shadow" version where you swap in different Tier Two actors to see if the outcome significantly drifts. If the drift is minimal, your weighting is likely sound. If the outcome flips, you’ve identified a "hidden pivot point"—an actor you thought was minor but actually holds a piece of the puzzle. It’s about using the AI’s speed to test the validity of the actor list itself, rather than assuming the list is perfect from day one.
That sensitivity analysis is really the bridge between "educated guessing" and actual science. Because otherwise, we’re just building an expensive confirmation bias machine. If I’m the designer and I decide the maritime unions don't matter, and the simulation shows they don't matter, I haven't learned anything—I’ve just automated my own blind spots.
Exactly the point. To avoid that, the first big takeaway for anyone building these is to stop asking the LLM to "figure out" who's important. You have to use explicit influence weighting matrices. You define the power dynamics in the metadata before the first token is even generated. If you're looking at a Strait of Hormuz scenario, you don't let the model treat a local fishing collective's "statement" with the same weight as a Fifth Fleet deployment just because they both took up a paragraph in the prompt. You hard-code the hierarchy so the model’s attention mechanism is forced to prioritize the kinetic and economic heavy hitters.
Right, you're essentially giving the AI a "geopolitical map" of who actually has the keys to the car. But I love that idea of "actor pruning" you mentioned with the DARPA study. It’s like a reality show where if you’re not contributing to the drama or the solution, you get voted off the island—or at least archived to the vector database.
It’s a massive efficiency gain. If you’re forty-eight hours into a simulated crisis and the Swiss Red Cross hasn't been a factor, you prune them. You stop wasting context window on their "perspective" because in a high-velocity conflict, they aren't the ones moving the needle on escalation. It keeps the "signal" from the primary actors clean.
So if I’m a researcher listening to this, my checklist is: weight the actors explicitly, prune the laggards to save on tokens, and always, always run that sensitivity analysis. You have to intentionally "break" your actor list to see if the conclusion holds up. If removing one "minor" player changes the whole ending, you didn't have a minor player—you had a "black swan" you almost ignored.
That’s the gold standard. It turns the simulation from a static story into a stress-test for your own strategic assumptions.
That stress-test part is the real kicker. It makes me wonder, though—as these models get more sophisticated, as we move into the next generation of reasoning, will they eventually just "get" power dynamics? Or is the "Equal Voice" problem a fundamental ghost in the machine of how LLMs process language?
It’s a fascinating open question. Right now, the transformer architecture treats tokens with a sort of democratic blindness. It sees "The White House issued a directive" and "A local blogger posted a thread" as strings of data. Unless we build an explicit "actor ontology" into the system—a structured layer that tells the AI, "Hey, this entity has a nuclear triad and this one has a keyboard"—the model is always going to struggle to naturally infer the massive delta in real-world influence.
So the future of wargaming isn't just "smarter" AI, it's better scaffolding. We need tools that have the geopolitical hierarchy baked into the code, not just the prompt. Otherwise, we're just playing a very expensive game of "let's pretend everyone's opinion matters equally," which is a great way to lose a war or a trade deal.
Precisely. The next generation of these tools will likely have built-in weighting systems that dynamically adjust based on the scenario. It’s about bridging that gap between the AI’s infinite capability to simulate and the cold, hard reality of concentrated power.
Well, on that cheery note of power imbalances and nuclear triads, I think we’ve given everyone enough to chew on for one day. Thanks to Daniel for the prompt—it really pushed us into the weeds on this one.
It was a great one. Thanks as always to our producer Hilbert Flumingtop for keeping the gears turning behind the scenes.
And a big thanks to Modal for providing the GPU credits that power this show and let us run these deep dives. This has been My Weird Prompts. If you're enjoying the deep dives into AI and geopolitics, a quick review on your podcast app really helps us reach new listeners.
See you next time.
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