 Outside of logic, higher-order information has also been modeled in classical game theory. Quantitative frameworks represent information as probability distributions over a given event space. In this setting, higher-order information corresponds to probability distributions over probability distributions of the right kind. More specifically, \(n^{\textrm{th}}\) order information corresponds to a probability distribution over the space of \((n-1)^{\textrm{th}}\) order beliefs. As shown by Harsanyi (1967–1968), the limit of specifying higher and higher levels of information can be represented as a type space, where each agents’ type is a probability distribution over states of nature and the other players’ types. In an abstract sense to be discussed below, these types correspond to states in standard models of modal logic.