  In recent years, agents have become capable of communicating seamlessly via
natural language and navigating in environments that involve cooperation and
competition, a fact that can introduce social dilemmas. Due to the interleaving
of cooperation and competition, understanding agents' decision-making in such
environments is challenging, and humans can benefit from obtaining
explanations. However, such environments and scenarios have rarely been
explored in the context of explainable AI. While some explanation methods for
cooperative environments can be applied in mixed-motive setups, they do not
address inter-agent competition, cheap-talk, or implicit communication by
actions. In this work, we design explanation methods to address these issues.
Then, we proceed to demonstrate their effectiveness and usefulness for humans,
using a non-trivial mixed-motive game as a test case. Lastly, we establish
generality and demonstrate the applicability of the methods to other games,
including one where we mimic human game actions using large language models.
