In game theory, iterated strategic games are considered harder to analyze than repeated ones. However, iterated games are in many cases more fit to describe the situation of artificial agents than repeated games. The reason being that they take into account previous actions of others, rather than just assigning each possible action a certain probability. We introduce the notion of Characteristic Distributions and discuss how they can be used to simplify and structure the analysis of strategies. This do not only provide a good basis for the agents to choose strategies for future interactions, but also helps in the design of environments that support certain types of agent behavior.