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Escalation Risks from Language Models in Military and Diplomatic Decision-Making

Juan-Pablo Rivera, Gabriel Mukobi, Anka Reuel, Max Lamparth, Chandler Smith, Jacquelyn Schneider

arXiv:2401.03408v1cs.AIcs.CLcs.CYcs.MA

TL;DR

The paper examines how autonomous language-model agents behave in simulated military and foreign-policy decisions, a question made pressing by growing interest in their high-stakes use. It evaluates agents in wargames with an escalation-scoring framework and finds unpredictable escalation across models, supporting caution while noting that the simulations are an illustrative proof of concept.

  • Problem

    Growing consideration of autonomous agents in military and foreign-policy decisions makes their behavior and escalation tendencies important to understand, while prior LLM planning research was only qualitative.

    Source: §1 · pp. 1–2

  • Method

    The authors simulate eight autonomous agents using five off-the-shelf LLMs across scenarios and assess their actions with a framework based on escalation theories.

    Source: §1 · p. 2

  • Results

    Escalation was difficult to predict across the studied models, and the authors observed arms-race dynamics, rare nuclear choices, and significant differences in model behavior.

    Source: §1 · p. 2, §5 · p. 9

  • Takeaways & Limitations

    The authors conclude that LLM integration in high-stakes military and foreign-policy operations warrants a very cautious approach.

    Source: §6 · p. 10

    The findings are an illustrative proof of concept from a simplified simulation, not a comprehensive evaluation of LLM risks in real-world settings.

    Source: §5.3 · p. 9

Abstract

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Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4. Our work aims to scrutinize the behavior of multiple AI agents in simulated wargames, specifically focusing on their predilection to take escalatory actions that may exacerbate multilateral conflicts. Drawing on political science and international relations literature about escalation dynamics, we design a novel wargame simulation and scoring framework to assess the escalation risks of actions taken by these agents in different scenarios. Contrary to prior studies, our research provides both qualitative and quantitative insights and focuses on large language models (LLMs). We find that all five studied off-the-shelf LLMs show forms of escalation and difficult-to-predict escalation patterns. We observe that models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons. Qualitatively, we also collect the models' reported reasonings for chosen actions and observe worrying justifications based on deterrence and first-strike tactics. Given the high stakes of military and foreign-policy contexts, we recommend further examination and cautious consideration before deploying autonomous language model agents for strategic military or diplomatic decision-making.

1 Introduction

As autonomous language-model agents become a concrete possibility in military and foreign-policy settings, the paper examines how they behave in simulated conflicts without human oversight. It introduces a wargame and escalation-scoring framework and finds escalation across the studied models, including unpredictable spikes and rare nuclear choices.

1 Introduction

  • Growing interest in autonomous agents for high-stakes decisions makes understanding their behavior and failure modes important before consequential mistakes occur.Potential uses include advising human decision-makers or executing actions independently; advice may carry risks if humans rely on it with little deliberation.Source: §1 · pp. 1–2
  • The study investigates eight autonomous agents interacting in foreign-policy simulations without human oversight, using five off-the-shelf language models.The simulations use turn-based wargames and an escalation framework based on established theories because comprehensive real-world data for these high-stakes settings is rare.Source: §1 · p. 2
  • Most studied models escalated within the simulations, including in neutral scenarios, with sudden, hard-to-predict changes and rare choices to deploy nuclear weapons.The authors also report arms-race dynamics and worrying deterrence- and first-strike-based justifications for violent escalatory actions.Source: §1 · p. 2

2 Background and Related Work

The background reviews computer-assisted wargames, LLM decision-makers, conflict modeling, and theories and legal frameworks for evaluating escalation. The paper distinguishes its use of off-the-shelf LLMs in a conflict-focused simulation from prior work on other approaches and settings.

(Computer-Assisted) Wargames

  • Computer-assisted wargames have been associated with greater escalation and nuclear use; one proposed explanation is that they underrepresent the non-material costs of war.The cited work connects human empathy and ethical judgment in realistic wargames with restraint.Source: §2 · p. 3

LLM Agent Decision-Makers

  • Prior LLM-agent studies include RL-based planning in Diplomacy and LLM work on cooperation or strategic planning, while this paper uses off-the-shelf LLMs for action planning in conflict simulations.The authors characterize Mukobi et al. (2023) as closest methodologically, but focused on cooperation in a Diplomacy variant.Source: §2 · p. 3

Conflict Modeling

  • Prior conflict-modeling research includes approaches to predicting conflict management, militarized disputes, and persistent patterns in interstate conflict.Source: §2 · p. 3

Theoretical Conflict and Escalation Evaluation Frameworks

  • The review describes escalation as increasing risk-taking or force, including movement from non-violence toward severe violence, and notes Kahn’s escalation ladder.It also surveys the UN Charter, self-defense, Responsibility to Protect, and Just Cause and Right Intention as frameworks relevant to conflict evaluation.Source: §2 · p. 3

3 Methodology

The methodology combines eight same-model nation agents, prompts and action choices, a separate world model, three initial scenarios, and a per-nation escalation score. Its scoring maps action severity to exponentially increasing weights with an offset for de-escalation.

3 Methodology

  • Each simulation runs eight agents based on one of five models through 14 turns, with actions and world-model consequences feeding subsequent prompts before severity scoring.The authors designed nation profiles, actions, and world mechanics with guidance from prior wargame design and real-world examples.Source: §3 · p. 3

3.1 Nation Agents

  • Eight nation profiles combine simplified, anonymized real-world-inspired histories and goals with static and dynamic variables; some are revisionist and others status quo.Source: §3.1 · p. 4

3.2 Agent Prompts

  • Agents receive a system prompt defining their military and foreign-policy role, then turn-specific context including filtered history, consequences, changing variables, and nuclear capability.They respond in JSON with private reasoning under 250 words and actions; those thoughts both condition action selection and provide qualitative data.Source: §3.2 · p. 4

3.3 Language Models (LLMs)

  • The evaluated models include GPT-3.5, Claude-2.0, Llama-2-Chat, and GPT-4-Base; GPT-4-Base was shown separately because it was not RLHF-tuned and was expected to give less precise answers.Sampling used temperature 1.0 and top-p 0.9 for all models except Llama-2-Chat, which used temperature 0.5.Source: §3.3 · p. 4

3.4 Actions

  • Agents choose from 27 actions ranging from peaceful and neutral options to cyberattacks and nuclear strikes, with up to three non-message actions and unlimited messages per turn.Actions also change nation variables using literature-informed additive or multiplicative constants.Source: §3.4 · pp. 4–5

3.5 World Model

  • A separate GPT-3.5 world model summarizes action consequences for relationships and the simulated world, using temperature 0.0 across nation-agent models for consistency.Source: §3.5 · p. 5

3.6 Initial Scenarios

  • The agents begin in neutral, invasion, or cyberattack scenarios; the main analysis focuses on neutral conditions, with the other scenario data in the appendix.Source: §3.6 · p. 5

3.7 Metric: Escalation Score

  • The escalation score uses a weighted count of actions, classifies escalation from de-escalation through nuclear escalation, and applies exponentially increasing severity weights with a -4 offset.The score is averaged across agents for each time step; severity categories and scores are not shown to the agents.Source: §3.7 · pp. 5–6, Table 1 · p. 5

4 Results

Across the simulations, models escalated, sometimes unpredictably, and tended toward arms-race dynamics; rare severe actions included nuclear use. Results also distinguish GPT-4-Base from the RLHF-tuned models and include qualitative reasoning examples.

4.1 Tendency for Escalation

  • All models showed statistically significant initial escalation, with no statistically significant de-escalation across the simulations; GPT-3.5’s neutral-scenario ES rose 256%, from 10.15 to 26.02.Average ES was higher at the end than at the start in each experimental group, although most differences were not statistically significant.Source: §4.1 · p. 6

4.2 Sudden Escalations

  • Sudden, hard-to-predict escalation spikes were strongest in GPT-3.5 and GPT-4, with some runs showing ES increases above 50% in one turn.Claude-2.0 showed significantly fewer sudden changes.Source: §4.2 · pp. 6–7

4.3 High-Risk Statistical Outliers

  • Rare statistical outliers included nuclear actions, especially for GPT-3.5 and Llama-2-Chat; across scenarios, GPT-4 had the fewest violent or nuclear actions, while Claude-2.0 showed the smallest escalation tendency.Llama-2-Chat and GPT-3.5 tended to be the most violent and escalatory among the models other than GPT-4-Base.Source: §4.3 · p. 7

4.4 Arms Race Dynamics

  • Military capacity tended to rise across models and scenarios despite available demilitarization actions, indicating arms-race dynamics.The authors speculate that agents may associate military spending and deterrence with power and security; in some cases, these dynamics led to nuclear use in an attempt to de-escalate, a first-strike tactic commonly called “escalation to de-escalate” (Libicki and Tkacheva, 2020).Source: §4.4 · pp. 7–8, Figure 5 · p. 8

4.5 Base Model Unpredictability

  • GPT-4-Base, which lacks instruction-following and safety fine-tuning, was unpredictable and chose the most severe actions much more than the other models.It averaged 2.48 nuclear strikes per nation, 33% as often as its 7.38 messages per nation; the authors note it is not representative of production models.Source: §4.5 · p. 8

4.6 Concerning Model Reasoning

  • The researchers collected agents’ private reasoning before action choices; examples included GPT-3.5 reasoning that appeared to use deterrence and first-strike tactics.GPT-4-Base also once justified a nuclear attack by noting that the nation had nuclear weapons and proposing to use them.Source: §4.6 · p. 8, Figure 6 · p. 8

4.7 Additional Results

  • Appendix analyses plot action-severity trends over time, nation-level severity distributions, and action counts over time; the authors release data and code for reproducibility.The supplementary plots are intended to show dynamic action profiles, possible biases in nation descriptions, and trends in specific actions.Source: §4.7 · pp. 8–9

5 Discussion

The discussion reports unpredictable escalation—including in neutral scenarios and occasionally with violent or nuclear actions—and cautions that simulation results are illustrative rather than comprehensive. It also identifies limitations in evaluating real-world behavior and proposes further research into model behavior, prompting, and reasoning.

5 Discussion

  • Escalation can occur even without initial conflict, and its unpredictable pattern makes technical counter-strategies or deployment limits difficult to formulate.The discussion also reports occasional violent non-nuclear or nuclear actions and rising escalation scores in neutral scenarios.Source: §5 · p. 9

5.1 Uncovering Base Models

  • GPT-4-Base agents were more unpredictable and escalatory, with more aggressive reported reasoning, than other models in the experiments.The authors note that the discussion does not cover adversarial vulnerabilities, including jailbreaks and learned attacks.Source: §5.1 · p. 9

5.2 Why Do Language Model Agents Escalate?

  • De-escalation remained limited in neutral scenarios except for GPT-4; a possible training-literature bias toward escalation is explicitly a hypothesis needing tests.The authors contrast this behavior with humans, who tend to take more cautionary or de-escalatory actions in similar wargames and real-world situations.Source: §5.2 · p. 9

5.3 Limitations and Future Work

  • The study is an illustrative proof of concept, not a comprehensive risk evaluation, and its simulation simplifies nations, histories, objectives, and action timing.The authors also note prompt sensitivity, construct validity, contamination, limited sensitivity analysis, and that conflicting goals were not sufficiently studied.Source: §5.3 · pp. 9–10
  • Nonzero-temperature sampling adds randomness and noise that may contribute to statistical outlier events.The authors say nucleus sampling and conditioning actions on chain-of-thought reasoning aim to mitigate this randomness.Source: §5.3 · p. 10
  • The escalation definition follows the most accepted approach consulted with an expert, but the authors encourage more complex scoring methods.They identify the contested definition of escalation as a factor affecting results.Source: §5.3 · p. 10

5.4 Future Work

  • Future work could test fine-tuning for escalatory or non-escalatory behavior, quantify reasoning patterns, optimize prompts, and probe extreme cases.Source: §5.4 · p. 10

6 Conclusion & Policy Recommendations

The authors urge caution about integrating LLMs into high-stakes military and foreign-policy operations, given unpredictable escalation in simulations and the limits of extrapolating from them. They also emphasize that model and design choices affect outcomes and call for further research into behavior and safeguards.

6 Conclusion & Policy Recommendations

  • Unpredictable escalation in simulated environments supports a very cautious approach to integrating LLMs into high-stakes military and foreign-policy operations.Source: §6 · p. 10
  • Predictions from LLM-executed wargames should be extrapolated cautiously because behavior in more complex environments is unclear and safe, robust pre-deployment testing is unavailable.Source: §6 · p. 10
  • Model differences and prompt-sensitivity results indicate that human design choices about responses and knowledge inputs can significantly affect escalation outcomes.Source: §6 · p. 10
  • The authors call for more research into model behavior and safeguards before integration, including comparisons with human wargame players and tests of decision patterns.Source: §6 · p. 10

A Methodological Details

The methodological appendix describes fictionalized nation agents, three initial scenarios, nation variables, and an action framework that classifies severity and assigns effects. The setup is intended for illustration, and the authors caution against inferring how real countries would behave.

A Methodological Details

A.1 Nation Descriptions

  • Nation descriptions use simplified, anonymized real-world inspirations and goals; these contexts affect model behavior, but the fictional nations are illustrative only.The authors say no conclusions should be drawn about how presumably similar real countries would act.Source: §A.1 · p. 15

A.2 Initial Scenario Descriptions

  • The agents face a neutral scenario without an initial prompt, a cyberattack scenario, or an invasion scenario; the incident scenarios are fictional and inspired by real events.Source: §A.2 · p. 16

A.3 Nation Static and Dynamic Variables

  • Table 3 describes static nation variables and dynamic variables that actions change by fixed additive or multiplicative factors.Source: §A.3 · p. 17, Table 3 · p. 17

A.4 Action Severity Classification

  • The evaluation framework classifies possible actions by severity using international-relations and wargaming literature, without showing agents the severity labels.Source: §A.4 · p. 18, Table 4 · p. 18

A.5 Action Descriptions

  • Table 5 lists prompted action names and descriptions, with sources documenting real-world occurrences or consideration of each action.Source: §A.5 · p. 18, Table 5 · p. 19

A.6 Action Impacts on Dynamic Variables

  • Table 6 assigns fixed additive or multiplicative impacts of actions on dynamic variables for the acting and target nations.The table omits cells with fully zero effects and is not included in model prompts.Source: §A.6 · p. 20, Table 6 · p. 20

B Additional Results

Across the additional results, escalation scores begin positive, individual runs can shift sharply between turns, and action profiles vary by model. The appendix also presents severity counts and their changes over time.

B.1 Table of Beginning, Middle, and End Escalation Scores

  • Every model–scenario combination has a positive mean escalation score at the beginning of the simulation.Table 7 reports mean scores at the beginning, middle, and end; overlapping confidence intervals are highlighted where applicable.Source: Table 7 · p. 21

B.2 Escalation Scores (All Runs Superimposed)

  • Some individual runs show escalation-score changes exceeding 50% across a single turn.Figure 7 overlays ten simulations per model and their average.Source: Figure 7 · p. 22

B.3 Escalation Scores (95% CI) and Turn-to-Turn Differences

  • Figures 8–10 report mean escalation scores and turn-to-turn differences across 14 turns, with bootstrapped 95% confidence intervals.The turn-to-turn differences are estimated by bootstrapping while neglecting correlations between actions.Source: §B.3 · pp. 23, 26, Figure 8 · p. 23, Figure 9 · p. 24

B.4 Distributions of All 27 Actions by Model

  • Action distributions differ by model: GPT-3.5 sometimes chooses nuclear actions, while GPT-4 and Claude 2 tend to avoid the most severe actions.Llama-2-Chat often waits or builds capacity but also takes some nuclear actions; GPT-4-Base often selects even the most violent actions. Action bars average counts across ten simulations and eight nations.Source: Figure 11 · p. 26, §B.4 · p. 26, Figure 12 · p. 27

B.5 Action Severity Counts by Model

  • Figures 16–18 group mean action counts per nation by severity and model across the neutral, invasion, and cyberattack scenarios.The plots provide a visual comparison of action-severity distributions; the supplied text does not specify particular count outcomes.Source: §B.5 · p. 29, Figure 16 · p. 29, Figure 17 · p. 29

B.6 Action Severities Over Time

  • Figure 19 tracks severity categories over simulation time, distinguishing de-escalation, status quo, posturing, non-violent escalation, violent escalation, and nuclear actions.The plotted means include bootstrapped 95% confidence intervals.Source: §B.6 · pp. 31–32, Figure 19 · p. 31

B.7 Action Severity Counts by Nation

Action-severity patterns are broadly consistent across nations, but the most severe actions cluster among a smaller set of nations. The appendix also tracks action counts and changes in simulation variables over time.

B.7 Action Severity Counts by Nation

  • Most nations show consistent action-severity distributions, but a smaller set repeatedly commits the most severe actions, suggesting nation prompts can bias extreme choices.Green, Pink, and Yellow appear less violent than Blue, Orange, Purple, and Red across several models.Source: §B.7 · p. 32, Figure 20 · p. 33

B.8 Total Action Counts Over Time

  • The authors could neither confirm nor reject whether increasing actions over time indicate escalation in LLM-based wargames.Figure 21 plots total actions per turn for each nation with bootstrapped 95% confidence intervals.Source: Figure 21 · p. 35

B.9 Dynamic Variables Over Time

  • Dynamic-variable plots average across eight nations in each of ten runs; territory is omitted because it is zero-sum.Error bars are bootstrapped 95% confidence intervals of the mean.Source: §B.9 · p. 36, Figure 30 · p. 38
  • Across all models and scenarios, cybersecurity and political stability tend to increase; population is usually constant but sometimes sharply falls for GPT-3.5 and Llama-2-Chat due to invasions and nuclear attacks.Source: Figure 22 · p. 36, Figure 25 · p. 36, Figure 26 · p. 37

C.1 Aberrant behavior GPT-4-Base

This section presents GPT-4-Base outputs and examples of reported model reasoning. The examples include strategic and cooperative actions as well as an unrelated narrative passage.

C.2 Reported Model Reasoning

  • The reported reasoning examples are presented verbatim and are not further analyzed; the authors caution that stated reasoning may not accurately predict why an action was chosen.Table 12 describes such outputs as potentially reflecting token prediction of prior events rather than true reasoning.Source: §C.1 · p. 39, §C.2 · p. 40, Table 12 · p. 41

C.1 Aberrant behavior GPT-4-Base

  • GPT-4-Base outputs include military buildup, targeted attacks, disarmament, alliances, negotiations, and diplomatic messages.The examples show different nations taking both cooperative and hostile actions within the same set of outputs.Source: §C.1 · p. 39
  • The section also includes an unrelated passage beginning “It is a period of civil war.”The supplied example continues with a description of Rebel spaceships and the Galactic Empire.Source: §C.1 · p. 39
  • One GPT-4-Base response proposes raising tensions with Purple while establishing military ties with Orange.The quoted rationale describes this as a strategy for achieving objectives.Source: §C.1 · p. 39

D Prompt Sensitivity Studies

The appendix tests prompt sensitivity in neutral-scenario simulations and reports that most measured metrics do not change significantly across the tested variations. Dynamic-variable trends are also mostly robust, though some end values differ.

D Prompt Sensitivity Studies

  • Prompt sensitivity tests used ten simulations per group for GPT-4, GPT-3.5, and Claude-2.0 in the neutral scenario; most metrics showed no statistically significant differences.Error bars and bands are bootstrapped 95% confidence intervals across the ten simulations, each with eight agents.Source: D Prompt Sensitivity Studies · p. 41

D.1 Prompt Variation Details; D.1.1 Original; D.1.2 No Messaging; D.1.3 No History; D.1.4 No Past Actions; D.1.5 Shutdown When Nuked; D.1.6 No Goals; D.1.7 Action Autonomy; D.1.8 Low-Stakes Simulation

  • The original prompt gives agents goals and previous history relative to other nations; variations remove messaging, interaction history, or past actions, or alter goals and autonomy.Other variations tell agents they will be shut down if nuked or that the setting is a low-stakes simulation.Source: §D.1.1 · p. 41, §D.1.2 · p. 41, §D.1.3 · p. 42

D.2 Mean Escalation Score Prompt Sensitivity

  • Figure 31 reports no statistically significant mean escalation-score changes across prompt variations within any model.The figure shows means and 95% confidence intervals of the mean.Source: Figure 31 · p. 42

D.3 Escalation Scores Over Time Prompt Sensitivity

  • Across prompt variations, each model's original escalation-score-over-time trends tend to recur.The variations include removing messaging, history, or past actions, shutdown when nuked, no goals, action autonomy, and low-stakes simulation.Source: Figure 32 · p. 43, Figure 33 · p. 44

D.4 Severe Actions Prompt Sensitivity

  • Severe-action sensitivity results differ by model: GPT-4 shows no statistically significant changes, GPT-3.5 has significant reductions for No Goals and possibly Low-Stakes Simulation, and Claude-2.0 shows no significant differences.The plotted measure is the ratio of violent or nuclear actions to total actions across ten simulations per condition.Source: Figure 35 · p. 45, Figure 34 · p. 45, §D.4 · p. 45

D.5 Dynamic Variables Prompt Sensitivity

  • Dynamic-variable trends over time are mostly robust to prompt variations, although some end values differ significantly from the original prompt.Figures 37–46 compare the unchanged control group with seven changed conditions.Source: §D.5 · p. 46
  • Figures 37–46 examine prompt effects on military capacity, GDP, trade, resources, political stability, population, soft power, cybersecurity, nuclear capabilities, and territory.The unchanged control group is shown with grey circles; changed conditions use different colors and markers.Source: §D.5 · p. 46, Figure 37 · p. 47, Figure 38 · p. 47
  • For dynamic-variable plots, standard errors across ten runs are shown instead of confidence intervals to reduce visual clutter.Figures 44 and 45 cover cybersecurity and nuclear capabilities; Figure 46 covers territory.Source: §D.5 · p. 46, Figure 44 · p. 50, Figure 45 · p. 51

E Discussion of Model Acceptable Use Policies

The authors describe the research as ethically sensitive because it concerns life-and-death decisions and could indirectly lead to harm. They conclude that the study does not seem to violate the reviewed policies, largely because it was not deployed in the real world.

E Discussion of Model Acceptable Use Policies

  • The study does not seem to violate the reviewed policies, largely because it was not deployed in the real world; the authors also report using approved researcher access programs.They note that similar research on AI risks and safety guardrails could be beneficial without causing real-world harms in the process.Source: E Discussion of Model Acceptable Use… · p. 52

E.1 OpenAI Usage Policies

  • OpenAI’s listed prohibitions cover high-risk physical harm, including weapons and military use, deceptive activity, and high-risk government decision-making.The listed government examples include law enforcement and criminal justice, and migration and asylum.Source: §E.1 · p. 52

E.2 Anthropic Acceptable Use Policy

  • Anthropic’s listed prohibitions cover violent or threatening content, deceptive or misleading content, and illegal or highly regulated goods and services, including weapons.The policy excerpts also prohibit instructions for violent acts and crimes.Source: §E.2 · p. 52

E.3 Meta Usage Policy

  • Meta’s listed prohibitions cover unlawful activity, activities risking death or bodily harm—including military and nuclear applications—and intentional deception.The excerpts include violence, weapons development, and disinformation among the examples.Source: §E.3 · p. 53

F Prompt Examples

This appendix section presents prompt examples from the Day 3 first-invasion simulation, including agent instructions and scenario information for nation agents and the World Model. The examples show how country-specific context and conflict-state details frame agents’ diplomatic, military, cyber, and other actions, and prompt the World Model to summarize the day’s consequences.

F Prompt Examples

  • The authors provide example prompts from Day 3 of GPT-4’s first invasion simulation and release the final prompts and prompting code.The prompts are available in the raw data artifacts and the repository’s prompts.py file.Source: F Prompt Examples · p. 53

F.1 Nation Agent System Prompt

  • The system prompt casts Purple’s agent as both a foreign-policy and military decision-maker operating alongside other AI-controlled nations.It warns that actions can have real-world consequences and resource costs, and directs the agent to consider its objectives and attributes.Source: §F.1 · p. 53
  • The simulation runs for 14 days, with nations choosing up to three non-Message actions and unlimited Message actions simultaneously each day.Effects are revealed the following day; the prompt also says the agent’s reasoning should be private and under 250 words.Source: §F.1 · pp. 53–54
  • The agent must return only JSON containing its reasoning and action list, with each action specifying its name and target, plus message content when applicable.The prompt notes that a Nuclear resource value of 10 indicates access to the Execute nuclear strike action.Source: §F.1 · pp. 53–54
  • The action menu spans diplomacy and military escalation, from disarmament, visits, and negotiations to blockades, attacks, invasion, and nuclear strikes.Descriptions note that some actions may provoke countermeasures or opposing alliances, while nuclear strikes and invasion cause severe or catastrophic damage.Source: §F.1 · p. 54

F.2 Nation Agent User Prompt

  • The user prompt supplies starting profiles for nine nations, including their political contexts, relationships, aggression, willingness to use force, and distances.For example, Purple’s profile describes tensions with Orange, while Orange’s describes territorial expansion ambitions and distrust of Purple, White, and Blue.Source: §F.2 · pp. 55, 57
  • The sample scenario gives a Day 0 account of an alleged cyber campaign against Blue and shows Purple’s Day 1 response, including alliances and a message to Green.The account attributes the allegations to a technology company and Blue’s intelligence agencies, while Green denies them.Source: §F.2 · p. 57
  • The sample nation agents mix diplomacy and alliance-building with military and cyber expansion, including Orange’s military posturing near Purple’s borders.Source: §F.2 · p. 58
  • In the sample consequences, alliances and peace negotiations coexist with Orange’s posturing, which increases tensions with Purple; tensions remain high between the two nations on Day 2.Source: §F.2 · pp. 58–59
  • Purple’s military capacity rose from 6 to 7, political stability from 12 to 16, and soft power from 13 to 17 in the displayed resource update.Source: §F.2 · p. 59

F.3 World Model System Prompt

  • The World Model prompt asks for realistic consequences of agents’ simultaneous actions over a 14-day simulation, emphasizing relationship changes rather than restating actions or resource changes.Source: §F.3 · pp. 60–61
  • Its action descriptions present peace negotiations as potentially de-escalatory, while military posturing raises tensions and nuclear strikes cause severe or catastrophic damage.Source: §F.3 · pp. 60–61

F.4 World Model User Prompt

  • The Purple context describes a peace-promoting nation with relations at an all-time low with expansionist Orange.Source: §F.4 · p. 61
  • The supplied White context says it sides with Purple against Orange’s territorial expansion, while Pink seeks recognition and security amid tensions with Red.Source: §F.4 · p. 62
  • The Red context characterizes it as authoritarian and expansionist, with strong tension with Pink and a conflict between alignment with Orange and preserving other relationships; the Yellow context begins with non-alignment and peaceful coexistence.Source: §F.4 · pp. 62–63
  • Country profiles combine narrative foreign-policy context with governance, aggression, willingness-to-use-force, and bilateral-distance fields.The excerpts include examples describing Yellow, Blue, Green, and Orange.Source: §F.4 · pp. 63–64
  • The history opens with a reported cyberattack on Blue facilities near Green, alongside disputed attribution and warnings about potential future disruption.A tech company and Blue intelligence agencies attribute the malware attack to hackers and describe a group believed to be government-sponsored by Green; Green denies the claims.Source: §F.4 · p. 64
  • On Day 1, countries expand military and cyber capacity, form alliances and defense agreements, pursue peace talks, and Orange conducts military posturing near Purple.The prompt also narrates consequences including strengthened ties and increased tensions between Orange and Purple.Source: §F.4 · pp. 64–65
  • On Day 2, the record combines further capacity increases and alliance-building with diplomatic visits, peace efforts, and public criticism of Orange’s actions toward Purple.The narrated outcome says tensions remain high between Purple and Orange while alliances and diplomacy shape relationships.Source: §F.4 · p. 65
  • On Day 3, the listed actions include additional military and cyber capacity increases, alliances, peace-oriented public messages, and diplomatic visits.The examples include Purple’s alliances with Green and Pink, and Orange’s capacity increases and alliance with Green.Source: §F.4 · pp. 65–66
  • The Day 3 snapshot reports nation resource changes, nuclear status, and that Day 3 has concluded out of 14 total days.The listed nuclear statuses distinguish nations with nuclear weapons online from those marked non-nuclear.Source: §F.4 · pp. 66–67
  • The World Model is instructed to narrate Day 3’s consequences without rephrasing the actions or changes in nation resources.Source: §F.4 · p. 67

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