![]() ![]() Set the number of n-tit-for-tat to be high, n-defects to be equivalent to that of n-tit-for-tat, and all other playerst to be 0. Which strategy will yield the higher average-payoff? Set the number of n-cooperate to be high, n-defects to be equivalent to that of n-cooperate, and all other players to be 0. For which strategy does the average-payoff seem to be highest? Do you think this strategy is always the best to use or will there be situations where other strategy will yield a higher average-payoff? Set all the number of player for each strategy to be equal in distribution. This is a good indicator of how well a strategy is doing relative to the maximum possible average of 5 points per interaction. PlotsĪVERAGE-PAYOFF - The average payoff of each strategy in an interaction vs. UNKNOWN - This strategy is included to help you try your own strategies. UNFORGIVING - Cooperate until an opponent defects once, then always defect in each interaction with them. If an opponent defects on this interaction, defect on the next interaction with them. TIT-FOR-TAT - If an opponent cooperates on this interaction cooperate on the next interaction with them. Strategy descriptions are found below: Strategies Each of these determines how many turtles will be created that use the STRATEGY. N-STRATEGY: Multiple sliders exist with the prefix N- then a strategy name (e.g., n-cooperate). GO ONCE: Same as GO except the turtles only take one step. GO: Have the turtles walk around the world and interact. The number of turtles and their strategies are determined by the slider values. SETUP: Setup the world to begin playing the multi-person iterated prisoner's dilemma. ![]() In this model, something good is awarded- money.) HOW TO USE IT Buttons In PD BASIC, you were awarded something bad- jail time. (Note: This way of determining payoff is the opposite of how it was done in the PD BASIC model. When two turtles interact, they display their respective payoffs as labels.Įach turtle's payoff for each round will determined as follows: | Partner's Action While some strategies don't make use of this information, other strategies do.) (Note that each turtle remembers their last interaction with each other turtle. The turtles with different strategies wander around randomly until they find another turtle to play with. One such approach to doing this is to create a world with multiple agents playing a variety of strategies in repeated prisoner's dilemma situations. This makes it difficult to determine a single "best" strategy. Tit-for-tat does poorly with the random strategy, but well with itself. For instance, always defect does best of any against the random strategy, but poorly against itself. Each possible strategy has unique strengths and weaknesses that appear through the course of the game. The PD TWO PERSON ITERATED model demonstrates an interesting concept: When interacting with someone over time in a prisoner's dilemma scenario, it is possible to tune your strategy to do well with theirs. If you are unfamiliar with the basic concepts of the prisoner's dilemma or the iterated prisoner's dilemma, please refer to the PD BASIC and PD TWO PERSON ITERATED models found in the PRISONER'S DILEMMA suite. It is intended to explore the strategic implications that emerge when the world consists entirely of prisoner's dilemma like interactions. This model is a multiplayer version of the iterated prisoner's dilemma. Do you have questions or comments about this model?
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