Problem
Research questions and friction points this paper is trying to address.
social simulation
military decision-making
verifiable rules engine
replay-checkable mechanics
private-channel negotiation
No One Wins in Nuclear War: A Social Simulation of Military Decision-making
🤖 AI Summary
Existing social simulation environments struggle to simultaneously achieve behavioral fidelity and mechanistic verifiability, particularly lacking support for private-channel negotiation and decision replay. To address this gap, this work proposes the WOPR social simulation framework, which leverages wargaming as a foundation by modeling factions as collective command-and-control systems and introducing a standardized decision-point contract mechanism. The framework integrates a deterministic, replayable rule engine, a four-tier news and communication gradient model, the Concordia agent architecture, and a private single-recipient channel protocol, enabling high-fidelity, auditable strategic decision-making simulations. The system has successfully replicated the rules of the card game *Nuclear War*, with all code, configurations, and replay data publicly released.
📝 Abstract
WOPR is a social-simulation environment for studying how organizations make high-stakes decisions, built on a deterministic, replay-validated rules engine and using wargames as the vehicle. We instantiate it first with the published card game Nuclear War, traced against its published rules. We start with military decision-making because of its safety implications and because it needs further study, but the design is not specific to it: the decision-point contract that exposes the engine to agents is reusable across verifiable rule systems. Existing social-simulation work emphasizes persona fidelity and synthetic opinion, but lacks a verifiable rules engine with replay-checkable mechanics and private-channel negotiation. WOPR supplies that engine, and its contract makes every strategic choice an explicit agent decision. The method is agnostic to social-simulation frameworks; we adopt Concordia as the default harness for driving the game. On the same engine, WOPR layers a four-rung press ladder from silence to private single-recipient channels with structured commitments, and instantiates each faction as a collective command-and-control system rather than a single agent. We make all code, example configurations, and replay data publicly available at https://github.com/eilab-gt/wopr.
Innovation
Methods, ideas, or system contributions that make the work stand out.
social simulation
verifiable rules engine
decision-point contract
replay validation
collective command-and-control
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