Evaluating Deterministic Policies in Two-player Iterated Games

author: Rui Dilão, NonLinear Dynamics Group, Instituto Superior Técnico, Universidade de Lisboa
published: Nov. 29, 2007,   recorded: October 2007,   views: 3274


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


We construct a statistical ensemble of games, where in each independent subensemble we have two players playing the same game. We derive the mean payoffs per move of the representative players of the game, and we evaluate all the deterministic policies with finite memory. In particular,we show that if one of the players has a generalized tit-for-tat policy,the mean payoff per move of both players is the same, forcing the equalization of the mean payoffs per move of both players. In the case of symmetric, non-cooperative and dilemmatic games, we show that generalized tit-for-tat or imitation policies together with the condition of not being the first to defect, leads to the highest mean payoffs per move for the players. Within this approach, it can be decided which policies perform better than others.The Prisoner's Dilemma and the Hawk-Dove games have been analyzed,and the equilibrium states of the infinitely iterated games have been determined. The infinitely iterated Prisoner's Dilemma game can have Nash solutions only if players have deterministic policies.

See Also:

Download slides icon Download slides: eccs07_dilao_edp_01.ppt (339.0 KB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: