Wargaming as a Research Method for AI Safety: Finding Productive Applications
Portia Murray
The AI safety research community faces a significant challenge: how do we study potential futures that haven't happened yet? As AI capabilities advance, we need robust methods to explore complex scenarios and their implications. At the AI Objectives Institute, we've been examining how wargaming methodologies can contribute to this crucial work. This post explores when wargaming is most effective as a research tool for AI safety, and which types of problems are best suited to this methodology.
When to Use Wargaming in AI Safety Research
Understanding when to employ wargaming methodology is crucial for productive research. As demonstrated through extensive empirical studies (Avin et al., 2020), wargaming proves most valuable when studying problems with specific characteristics that make traditional analysis insufficient. Its greatest strength lies in exploring scenarios characterized by radical uncertainty – where we face not just unknown probabilities, but unknown possibilities.
The development of advanced AI systems presents exactly this type of challenge. We face profound uncertainty not just about when certain capabilities might emerge, but about what forms they might take and how they could transform society. In such contexts, individual forecasters typically default to familiar patterns and assumptions, often unconsciously avoiding scenarios that seem too divergent from current conditions.
Wargaming offers a unique solution to this challenge. When players embody different entities with competing incentives and goals, their interactions can generate possibilities that no single analyst would likely conceive. Through structured role-play, participants can construct plausible pathways between our present circumstances and radically different future states, helping us understand how transformative changes might actually unfold. Perhaps most importantly, these interactions can surface potential "black swan" events – low-probability but high-impact scenarios that traditional analysis often overlooks.
Multi-Agent Dynamics
Wargaming excels at exploring scenarios where multiple actors with different incentives interact. This makes it particularly valuable for studying AI race dynamics, competitive deployments, and regulatory responses. For instance, understanding how different AI labs might respond to evidence of concerning capabilities in their systems requires modeling complex institutional behaviors that game theory alone struggles to capture. Research from Intelligence Rising demonstrates that race dynamics frequently emerge between technology companies when research is made public, leading to accelerated development timelines and decreased focus on safety considerations (Gruetzemacher et al., 2024). This matches theoretical predictions but provides empirical evidence of how these dynamics manifest in practice.
Time-Pressured Decision Making
Some of the most critical AI safety scenarios involve rapid response requirements. Wargaming helps us understand how people make decisions under pressure, revealing potential failure modes in crisis response plans. These insights are difficult to generate through other research methods, as they emerge from the dynamic interaction between time pressure and human psychology. Based on extensive wargaming research, even participants well-versed in AI progress often struggle to adapt to the accelerated timelines of AI development. As noted by Gruetzemacher et al. (2024), "the unprecedented pace of technological progress in foundation models presents novel challenges that make it very difficult for experts and non-experts alike to develop a bigger picture perspective on this progress."
Incomplete Information Environments
Real-world AI development and deployment decisions often occur with partial information. Wargaming naturally incorporates this uncertainty, helping researchers understand how different actors might behave when working with limited or potentially incorrect information about AI system capabilities and risks.
Future Research Directions
Governance Structure Testing
One promising application is using wargames to test proposed AI governance frameworks before implementation. These games can reveal unexpected weaknesses in oversight mechanisms and help design more robust governance structures.
Research from Intelligence Rising demonstrates the value of this approach - their analysis revealed how seemingly robust governance structures can fail under realistic pressures (Gruetzemacher et al., 2024).
Several key governance challenges emerged consistently in their research:
Verification Challenges: Even when international agreements on AI development were established, games revealed critical weaknesses in verification mechanisms. Players frequently found ways to pursue secret research programs while appearing compliant with oversight requirements.
Coordination Problems: Games showed that governance frameworks often struggled to handle the mismatch between corporate incentives and national security concerns. For instance, when U.S. companies developed concerning capabilities, government teams often lacked effective tools to intervene without resorting to extreme measures like nationalization.
Crisis Response Failures: When AI systems demonstrated unexpected capabilities or concerning behaviors, governance structures frequently proved too slow or bureaucratic to respond effectively. The lag between detection, decision-making, and implementation often allowed problems to escalate.
Safety vs. Competition Tensions: Governance frameworks repeatedly struggled to balance safety requirements with competitive pressures. Games showed that even well-designed safety protocols often degraded under race dynamics between companies or nations.
Future research could systematically evaluate different governance proposals by testing them against these common failure modes. For example:
How different verification regimes perform under various deception attempts
What mechanisms most effectively align corporate and national security interests
Which governance structures best handle rapid technological surprises
What combinations of incentives and oversight maintain safety standards under competitive pressure
This empirical testing of governance proposals could provide crucial evidence for policy discussions, helping identify robust approaches before real-world implementation becomes necessary.
Capability Deployment Dynamics
As AI systems become more capable, understanding the dynamics of their deployment becomes increasingly crucial. Research from over 40 strategic games conducted through Intelligence Rising has revealed important patterns in how deployment decisions affect development trajectories (Gruetzemacher et al., 2024).
The research documented several key dynamics that warrant further study through wargaming. First, they observed that race dynamics consistently emerge between technology companies when research becomes public, leading to accelerated development timelines. These competitive pressures often result in companies publicly emphasizing safety considerations while internally deprioritizing them under pressure to maintain competitive advantage.
Second, the research showed that races frequently become destabilizing, particularly when they involve competing national blocs. In multiple documented cases, the "runner-up" in technological development would resort to increasingly aggressive actions to prevent a competitor from achieving a decisive advantage through advanced AI deployment. This suggests that deployment decisions cannot be considered in isolation from broader geopolitical dynamics.
Third, the study revealed that even when safety measures and international agreements were in place, the pressure of competitive deployment often led to defections from these agreements during critical phases. This highlights the importance of studying not just the technical aspects of safe deployment, but also the institutional and competitive pressures that might compromise safety protocols.
These findings underscore why wargaming provides unique value for studying deployment dynamics - it captures the complex interplay between technical capabilities, organizational incentives, and competitive pressures that shape how advanced AI systems are likely to be deployed in practice.
Cross-Cultural Decision Making
The global nature of AI development necessitates understanding how different cultural and institutional contexts might influence AI-related decisions. Intelligence Rising's research has documented significant variations in how different national and organizational cultures approach AI development and governance (Gruetzemacher et al., 2024). For example:
Their research showed that Chinese firms typically align closely with state objectives, while U.S. companies often act more independently and sometimes in opposition to government preferences
Games revealed distinct differences in how nationalization of AI capabilities plays out across cultures - being more readily accepted and implemented in China compared to Western democracies where public-private partnerships are the more common approach
Decision-making timelines and processes varied significantly between political systems, with U.S. election cycles creating periodic disruptions to long-term AI strategy, while China maintained more consistent policy approaches
Cultural differences emerged in approaches to safety and regulation, with some cultures prioritizing rapid development and others emphasizing careful oversight
Future wargaming research could further explore these dynamics by:
Examining how different regulatory philosophies affect AI development trajectories
Understanding how varying approaches to public-private cooperation influence safety outcomes
Investigating how cultural differences in risk perception shape strategic decisions about AI deployment
Modeling how different political systems respond to and recover from AI-related crises
This cross-cultural dimension is particularly crucial as AI development becomes increasingly global, with different regions potentially pursuing divergent approaches to governance and safety.
Methodological Considerations
Validation Approaches
A key challenge is developing validation approaches tailored to specific objectives, such as:
Prediction accuracy of technological developments
Understanding stakeholder behavior under pressure
Testing governance framework effectiveness
Training decision-makers
Identifying potential failure modes
Scenario Design
The quality of insights from wargaming depends heavily on scenario design. Future work should focus on developing more sophisticated approaches to scenario construction, perhaps incorporating:
Historical analysis of technology deployment patterns
Expert elicitation methods for identifying crucial considerations
Effective scenario design requires carefully balancing multiple tradeoffs:
Fidelity versus number of actors represented
Time duration versus complexity of actions available
Stochastic elements versus deterministic outcomes
Player engagement versus serious analysis
Timeframe modeled versus granularity of turns
Improving Research Quality
To ensure wargaming contributes meaningfully to AI safety research, two methodological improvements deserve attention:
Structured Observation
Developing better methods for observing and recording game dynamics could help capture subtle interactions that might otherwise be missed. This could involve new tools for tracking decision patterns, recording participant reasoning, and analyzing emergent behaviors.
Outcome Analysis
Creating more rigorous frameworks for analyzing game outcomes would help translate insights into actionable recommendations. This includes developing methods for:
Identifying robust patterns across multiple game iterations
Distinguishing artifacts of game design from genuine insights
Tracking the influence of different variables on outcomes
When Not to Use Wargaming
Understanding the limitations of wargaming is crucial for effective research design. Wargaming is likely not the optimal approach for:
Technical AI alignment problems focused purely on mathematical or computational challenges
Short-term technical security issues better addressed through systematic red-teaming
Problems requiring precise quantitative predictions or measurements
Research questions that can be effectively answered through controlled experiments
Situations where the primary goal is to optimize existing systems rather than explore potential futures
Moving Forward
The future of AI safety research requires a diverse methodological toolkit. Wargaming, when appropriately applied, can provide unique insights into complex socio-technical challenges. As we develop this methodology, maintaining rigorous standards and clear understanding of its limitations will be crucial.
For researchers considering wargaming as a methodology, we recommend starting with well-defined problems that clearly exhibit the characteristics where wargaming excels. Building expertise in scenario design and game facilitation takes time, but the insights gained can significantly contribute to our understanding of AI safety challenges.
The effectiveness of wargaming for AI safety research has been demonstrated through structured observation and analysis. Studies show that role-play scenarios consistently surface non-linear effects and edge cases that other methods miss. However, success requires careful attention to participant selection and preparation - games generate the most valuable insights when participants have familiarity with both AI technologies and their assigned roles' real-world contexts.
We invite researchers interested in exploring these methodologies to engage with the broader community. Sharing experiences, methods, and results will help develop more robust approaches to studying these crucial questions about our future with AI.
To reach out to AOI about our wargaming efforts, or to learn more about the groups and individuals working on AI wargaming, email portia@objective.is.
