  Multi-agent interactions, such as communication, teaching, and bluffing,
often rely on higher-order social inference, i.e., understanding how others
infer oneself. Such intricate reasoning can be effectively modeled through
nested multi-agent reasoning. Nonetheless, the computational complexity
escalates exponentially with each level of reasoning, posing a significant
challenge. However, humans effortlessly perform complex social inferences as
part of their daily lives. To bridge the gap between human-like inference
capabilities and computational limitations, we propose a novel approach:
leveraging neural networks to amortize high-order social inference, thereby
expediting nested multi-agent reasoning. We evaluate our method in two
challenging multi-agent interaction domains. The experimental results
demonstrate that our method is computationally efficient while exhibiting
minimal degradation in accuracy.
