Source Grounding for Multi-Agent Simulations

Grounding starts before prompt writing. Define the pressure event, affected actors, decision window, geography, and outcome of interest. These boundaries determine which documents are relevant and prevent a large source dump from creating the appearance of rigor without a coherent question.

The goal is not to force every agent statement to quote a document. It is to show how durable traits, current beliefs, institutional constraints, and event-specific information entered the simulation. The ODD protocol's emphasis on documenting purpose, entities, process, and initialization provides a useful model for this discipline.

A product scenario might combine a launch brief, customer interviews, pricing research, support themes, competitive claims, and regulatory requirements. A public-policy scenario might require the proposed text, implementation guidance, stakeholder statements, demographic data, and prior responses to comparable changes.

Use secondary sources to find context and disagreements, then trace material claims to their origin where possible. Record dates because current-state claims decay. A timeless research method may remain valid for years, while an executive role, price, law, or public narrative can change before the simulation is reviewed.

Build a ledger with claim ID, source ID, affected agents, interpretation, confidence, and reviewer. One source can support several claims, but each claim should have a specific purpose. Avoid copying a broad report into every agent; shared context can erase the informational differences that create realistic disagreement.

Interview-grounded agent research illustrates a high-context approach: agents were built from structured interviews and evaluated against held-out behavioral measures. For organizational simulations, teams can adopt the principle without claiming population representation—use deep evidence for important actors and reserve independent material for evaluation.

First classify the conflict. Sources may cover different dates, populations, jurisdictions, definitions, or incentives. Some conflicts disappear when scope is aligned. When disagreement remains credible, create scenario branches or assign distinct beliefs to actors that would reasonably hold them.

Document which decision changes across the branches. If the same mitigation works under both interpretations, the decision may be robust even though the evidence is unsettled. If the decision reverses, the disputed fact becomes a priority for research or monitoring.

Large language models contain broad background knowledge. A prompt can request bounded behavior, but that does not guarantee perfect isolation. Review agents for knowledge that postdates the scenario, confidential facts they were not given, or polished explanations that depend on outside context.

Keep a held-out evaluation set where feasible. If the same interviews define agents and validate every output, the review measures memorization and narrative fit more than generalization. Held-out evidence, expert review, and counterfactual tests provide independent pressure.

Readiness does not mean completeness. The team should know which missing evidence could reverse the decision and which gaps are acceptable for exploration. Assign an owner to every high-impact gap and decide whether to research it, model it as a variant, or exclude the associated claim.

Archive the packet with identifiers and dates. Future runs should be able to distinguish a changed model from changed evidence. That separation is essential when the real world evolves and the team needs to explain why a forecast moved.