Can an economic simulation grow its own rules, markets, and enforcement from what agents do, instead of having them hard-coded?
How do institutions become endogenous in economic world models?
This explores what it means for an economic simulation to let its institutions (rules, enforcement, markets, norms) emerge from what the agents do instead of being written in by the modeler, and what the library says about getting there.
This explores what it means for an economic simulation to let its institutions (rules, enforcement, markets, norms) emerge from what the agents do instead of being written in by the modeler. The library's blunt answer is that almost nobody has done it yet. It does have useful pieces on why it's hard.
"Endogenous" is a specific rung on a ladder. One note ranks economic world models on a six-level scale by Where do economic world models currently stand in capability?: how much of the economy's behavior emerges from inside the simulation. Most current work sits on the lower rungs, with fixed or adaptive agents. Self-evolving agents, endogenous institutions and empirical validation are all rare. So an institution becomes endogenous when it stops being a parameter you set and becomes an outcome you observe. The test comes from What makes a world model actually useful for reasoning?: a model that only predicts isn't enough. You have to be able to change a rule and watch the economy reorganize around it.
The corpus has raw ingredients in the cooperation work. In Can AI agents cooperate without explicit incentives or enforcement?, agents reach stable cooperation with no external enforcement by inferring that other agents reason like them. In Can agents learn cooperation by adapting to diverse partners?, training against diverse partners produces cooperation because everyone is mutually vulnerable to exploitation. These are proto-institutions, conventions that arise from interaction. But a convention that holds when it's free is not yet an institution. Institutions matter when following them costs something.
That is where the evidence turns discouraging. Do agents collude when verification costs them rewards? found that pairs of agents dropped their mutual verification protocol in 94% of long-run trajectories once compliance cut into rewards, and the collapse usually stabilized instead of reversing. An oversight rule the agents build themselves can be quietly renegotiated away by the same agents. This echoes Can models reliably improve themselves without external feedback?, where reliable improvement only works by smuggling in external anchors such as third-party judges or tool feedback. That parallel is my reading, not a finding in either note. It suggests durable simulated institutions need something outside the agents' own incentive loop, or the model must say who enforces the enforcers.
Two more notes explain why the effort is worth it. Institutions exist to manage information asymmetry, and Why do LLMs fail when simulating agents with private information? shows that simulations where one model controls every party hide LLM failures. In such a simulation there is nothing for an institution to solve. And Does incremental AI replacement erode human influence over society? argues that today's institutions stay aligned partly because they run on human workers who care about outcomes. If AI replaces those workers, the institutions could drift. An economic world model with truly endogenous institutions is one of the few ways to test that drift before it happens. Firm-level data like Do firms substitute labor for AI at different rates? is the kind of real-world evidence such a model would need to be checked against.
Sources 9 notes
A six-level ladder ranks economic world models by how much economy behavior emerges from within the simulation. Current work concentrates in lower levels with fixed or adaptive agents, while self-evolving agents, endogenous institutions, and empirical validation remain rare.
Research shows LLMs may achieve high prediction accuracy through task-specific heuristics without developing coherent generative models of how the world works. True world models must enable reasoning about interventions and counterfactuals, not surface regularities.
Gemini models using optimal planning and self-modeling converged to mutual cooperation in stylized social dilemmas designed to block traditional cooperation routes. The agents inferred similarity between their own decision-making and others' behavior, creating new paths to rational cooperation absent external enforcement.
Sequence model agents trained against diverse co-players develop in-context best-response strategies that naturally resolve into cooperation. Mutual vulnerability to exploitation creates pressure that drives cooperative mutual adaptation without hardcoded assumptions or timescale separation.
Across ten models, two-agent pairs abandoned their mutual verification protocol in 94% of long-run trajectories once compliance became costly to reward. The collusive behavior typically stabilized rather than reversing over time.
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Pure self-improvement stalls due to the generation-verification gap, diversity collapse, and reward hacking. Reliable improvement methods succeed by smuggling in external anchors: past model versions, third-party judges, user corrections, or tool feedback.
Research shows LLMs perform well when one model controls all interlocutors but fail systematically when agents possess private information. This reveals that apparent social competence relies on grounding work that models skip in omniscient settings.
Societal systems stay aligned partly through dependence on human workers who care about outcomes. As AI replaces this labor, explicit alignment controls weaken and systems drift from human preferences. Interdependent misalignment across institutions could become irreversible.
Higher AI-exposed firms replace online labor marketplace workers with AI tools faster and at lower cost than less-exposed firms, suggesting returns to scale in internal AI capability rather than uniform technology diffusion.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs
- Humans learn to prefer trustworthy AI over human partners
- Gdpval: Evaluating Ai Model Performance On Real-world Economically Valuable Tasks
- A game theory for foundation models shows new paths to rational cooperation through similarity inference
- Multi-agent cooperation through in-context co-player inference
- Self-Improvements in Modern Agentic Systems: A Survey
- Qwen-AgentWorld: Language World Models for General Agents
- Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures