 An individual learning rule (or, to use Sandholm’s terminology, a revision protocol) can be represented as a function which takes these two pieces of information as arguments, mapping them to a matrix of the conditional switch rates between strategies. That is, the \(ij^{th}\)-entry of the matrix contains the rate at which followers of strategy \(S_i\) will switch to the strategy \(S_j\). It is called the conditional switch rate because the \(S_i\to S_j\) switch rate will typically depend on — that is, be conditional on — both the state of the population and the vector of expected payoffs. Note that the function may not actually use all of the information contained in its arguments: some learning rules may be more sophisticated than others.