An entropy-based measure is used to calculate the complexity of discourse patterns. This measure of complexity takes into account both the number of patterns (possibly generated), as well as the frequency of each pattern (actually instantiated). The discourse patterns are modelled as random walks in a multi-dimensional and value-weighted space. The multiple dimensions are theoretically specifiable within the framework of conversational analysis (e.g. number of moves, number of participants, types of adjacency pairs, etc.). And the weighted values are empirically measurable within a corpus of texts (e.g. relative frequency participants take the floor, relative frequency first pair-part is a command versus a question, relative frequency an embedding occurs, etc.). The way complexity correlates with various social and discursive functions is described. And the way this method may be extended to analyse successively more complicated patterns is detailed.