Successfully navigating the social world requires reasoningabout both high-level strategic goals, such as whether to co-operate or compete, as well as the low-level actions neededto achieve those goals. While previous work in experimentalgame theory has examined the former and work on multi-agentsystems has examined the later, there has been little work in-vestigating behavior in environments that require simultaneousplanning and inference across both levels. We develop a hierar-chical model of social agency that infers the intentions of otheragents, strategically decides whether to cooperate or competewith them, and then executes either a cooperative or competi-tive planning program. Learning occurs across both high-levelstrategic decisions and low-level actions leading to the emer-gence of social norms. We test predictions of this model inmulti-agent behavioral experiments using rich video-game likeenvironments. By grounding strategic behavior in a formalmodel of planning, we develop abstract notions of both co-operation and competition and shed light on the computationalnature of joint intentionality
