Prisoner's Dilemma Models

Here's a typical market place interaction: turtles A and B meet. A agrees to purchase an item from B. There are four possibilities:

I.   A pays B and B gives A the item

II.  A pays B with a bad check and B gives A the item

III. A pays and B gives A a defective item

IV.  A pays B with a bad check and B gives A a defective item

Clearly scenario II works the best for A and the worst for B, while scenario III works the best for B and the worst for A. A and B both benefit somewhat in scenario II, while neither benefits in scenario IV.

This is the classical Prisoner's Dilemma game. Imagine A and B are prisoners being interrogated separately about a crime. There are four possibilities:

I.   A and B both confess

II.  A denies and B confesses

III. A confesses and B denies

IV.  A and B both deny

In scenario I A and B both receive 3 year sentences. In scenario II B receives a 5 year sentence while A goes free, while the sentences are reversed in scenario III. In scenario IV both receive a 1 year sentence.

It's easy to see that the best strategy is to deny. If the probability that your opponent denies is p, then your expected jail term is:

3p + (1 – p) = 2p + 1

If you confess, then your expected jail term is:

5p + 3(1 – p) = 4p + 3

Models/Social Science/Unverified/Prisoner's Dilemma/PD Two-Person Iterated

In this variation of PD A (the human) and B (the computer) are awarded points according to the following payoff table:

               B cooperates   B defects

A cooperates   A: 3, B: 3     A: 0, B: 5

A defects      A: 5, B: 0     A: 3, B: 3

A and B play each other repeatedly. This is called iterated prisoner's dilemma. The goal is to maximize the average payoff.

Try different strategies against a random computer or a computer that always defects.

Models/Social Science/Ethnocentrism

Models/Social Science/Unverified/Prisoner's Dilemma/PD N-Person Iterated

Suckers always cooperate. Cheaters always defect. Impulsives cooperate randomly. Reciprocators always do what their opponents did previously. Try my version of this:

ipd.nlogo

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Evolutionary PDT

Vindictiveness