To successfully navigate its environment, an agent must construct and maintain representations of the other agents that it encounters. Such representations are useful for many tasks, but they are not without cost. As a result, agents must make decisions regarding how much information they choose to store about the agents in their environment. Using selective social learning as an example task, we motivate the problem of finding agent representations that optimally trade off between downstream utility and information cost, and illustrate two example approaches to resource-constrained social representation.

翻译：为成功适应环境，智能体需构建并维护其所遇其他智能体的表征。此类表征虽对诸多任务有所助益，却并非毫无代价。因此，智能体必须就其环境中其他智能体所需存储的信息量做出决策。以选择性社会学习为示例任务，我们论证了寻找能够在下游效用与信息成本之间实现最优权衡的智能体表征的问题，并阐述了两种面向资源受限社交表征的示例方法。