Abstract In social interactions, people often reason about the beliefs, goals and intentions of others. Thistheory of mindallows them to interpret the behavior of others, and predict how they will behave in the future. People can also use this ability recursively: they usehigher-order theory of mindto reason about the theory of mind abilities of others, as in “he thinks that I don’t know that he sent me an anonymous letter”. Previous agent-based modeling research has shown that the usefulness of higher-order theory of mind reasoning can be useful across competitive, cooperative, and mixed-motive settings. In this paper, we cast a new light on these results by investigating how the predictability of the environment influences the effectiveness of higher-order theory of mind. Our results show that the benefit of (higher-order) theory of mind reasoning is strongly dependent on the predictability of the environment. We consider agent-based simulations in repeated one-shot negotiations in a particular negotiation setting known as Colored Trails. When this environment is highly predictable, agents obtain little benefit from theory of mind reasoning. However, if the environment has more observable features that change over time, agents without the ability to use theory of mind experience more difficulties predicting the behavior of others accurately. This in turn allows theory of mind agents to obtain higher scores in these more dynamic environments. These results suggest that the human-specific ability for higher-order theory of mind reasoning may have evolved to allow us to survive in more complex and unpredictable environments.

Theory of mind refers to the ability to reason explicitly about unobservable mental content of others, such as beliefs, goals, and intentions. People often use this ability to understand the behavior of others as well as to predict future behavior. People even take this ability a step further, and use higher-order theory of mind by reasoning about the way others make use of theory of mind and in turn attribute mental states to different agents. One of the possible explanations for the emergence of the cognitively demanding ability of higher-order theory of mind suggests that it is needed to deal with mixed-motive situations. Such mixed-motive situations involve partially overlapping goals, so that both cooperation and competition play a role. In this paper, we consider a particular mixed-motive situation known as Colored Trails, in which computational agents negotiate using alternating offers with incomplete information about the preferences of their trading partner. In this setting, we determine to what extent higher-order theory of mind is beneficial to computational agents. Our results show limited effectiveness of first-order theory of mind, while second-order theory of mind turns out to benefit agents greatly by allowing them to reason about the way they can communicate their interests. Additionally, we let human participants negotiate with computational agents of different orders of theory of mind. These experiments show that people spontaneously make use of second-order theory of mind in negotiations when their trading partner is capable of second-order theory of mind as well.