MERMADE: $K$-shot Robust Adaptive Mechanism Design via Model-Based Meta-Learning

Mechanism design (MD) studies how rules and rewards shape the behavior of intelligent agents, e.g., in auctions or the economy. Simulations with AI agents are powerful tools for MD, but real-world agents may behave and learn differently than simulated agents under a given mechanism. Also, the mechanism designer may not fully observe an agent's learning strategy or rewards, and executing a mechanism may be costly, e.g., enforcing a tax might require extra labor. Hence, it is key to design robust adaptive mechanisms that generalize well to agents with unseen (learning) behavior, are few-shot adaptable, and are cost-efficient. Here, we introduce MERMADE, a model-based meta-learning framework to learn mechanisms that can quickly adapt when facing out-of-distribution agents with different learning strategies and reward functions. First, we show that meta-learning allows adapting to the theoretically known and appropriate Stackelberg equilibrium in a simple matrix game at meta-test time, with few interactions with the agent. Second, with bandit agents, we show empirically that our approach yields strong meta-test time performance against agents with various unseen explore-exploit behaviors. Finally, we outperform baselines that separately use either meta-learning or agent behavior modeling to learn a cost-effective mechanism that is $K$-shot adaptable with only partial information about the agents.