Prisoners dilemma game
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Statistics: Scientific method · Research methods · Experimental design · Undergraduate statistics courses · Statistical tests · Game theory · Decision theory
 This article contains mathematical terminology from game theory, which should not be confused with the common usage.
The Prisoner's Dilemma constitutes a problem in game theory. It was originally framed by Merrill Flood and Melvin Dresher working at RAND in 1950. Albert W. Tucker formalized the game with prison sentence payoffs and gave it the "Prisoner's Dilemma" name (Poundstone, 1992).
In its "classical" form, the prisoner's dilemma (PD) is presented as follows:
 Two suspects are arrested by the police. The police have insufficient evidence for a conviction, and, having separated both prisoners, visit each of them to offer the same deal. If one testifies ("defects") for the prosecution against the other and the other remains silent, the betrayer goes free and the silent accomplice receives the full 10year sentence. If both remain silent, both prisoners are sentenced to only six months in jail for a minor charge. If each betrays the other, each receives a fiveyear sentence. Each prisoner must choose to betray the other or to remain silent. Each one is assured that the other would not know about the betrayal before the end of the investigation. How should the prisoners act?
If we assume that each player prefers shorter sentences to longer ones, and that each gets no utility out of lowering the other player's sentence, and that there are no reputation effects from a player's decision, then the prisoner's dilemma forms a nonzerosum game in which two players may each "cooperate" with or "defect" from (i.e., betray) the other player. In this game, as in all game theory, the only concern of each individual player ("prisoner") is maximizing his/her own payoff, without any concern for the other player's payoff. The unique equilibrium for this game is a Paretosuboptimal solution—that is, rational choice leads the two players to both play defect even though each player's individual reward would be greater if they both played cooperatively.
In the classic form of this game, cooperating is strictly dominated by defecting, so that the only possible equilibrium for the game is for all players to defect. In simpler terms, no matter what the other player does, one player will always gain a greater payoff by playing defect. Since in any situation playing defect is more beneficial than cooperating, all rational players will play defect, all things being equal.
In the iterated prisoner's dilemma the game is played repeatedly. Thus each player has an opportunity to "punish" the other player for previous noncooperative play. Cooperation may then arise as an equilibrium outcome. The incentive to defect is overcome by the threat of punishment, leading to the possibility of a cooperative outcome. So if the game is infinitely repeated, cooperation may be a subgame perfect Nash equilibrium although both players defecting always remains an equilibrium and there are many other equilibrium outcomes.
In casual usage, the label "prisoner's dilemma" may be applied to situations not strictly matching the formal criteria of the classic or iterative games; for instance, those in which two entities could gain important benefits from cooperating or suffer from the failure to do so, but find it merely difficult or expensive, not necessarily impossible, to coordinate their activities to achieve cooperation.
Strategy for the classical prisoner's dilemma
The classical prisoner's dilemma can be summarized thus:
Prisoner B Stays Silent  Prisoner B Betrays  

Prisoner A Stays Silent  Each serves 6 months  Prisoner A: 10 years Prisoner B: goes free 
Prisoner A Betrays  Prisoner A: goes free Prisoner B: 10 years  Each serves 5 years 
In this game, regardless of what the opponent chooses, each player always receives a higher payoff (lesser sentence) by betraying; that is to say that betraying is the strictly dominant strategy. For instance, Prisoner A can accurately say, "No matter what Prisoner B does, I personally am better off betraying than staying silent. Therefore, for my own sake, I should betray." However, if the other player acts similarly, then they both betray and both get a lower payoff than they would get by staying silent. Rational selfinterested decisions result in each prisoner's being worse off than if each chose to lessen the sentence of the accomplice at the cost of staying a little longer in jail himself. Hence a seeming dilemma. In game theory, this demonstrates very elegantly that in a nonzero sum game a Nash Equilibrium need not be a Pareto optimum.
Generalized form
We can expose the skeleton of the game by stripping it of the prisoner framing device. The generalized form of the game has been used frequently in experimental economics. The following rules give a typical realization of the game.
 There are two players and a banker. Each player holds a set of two cards: one printed with the word "Cooperate", the other printed with "Defect" (the standard terminology for the game). Each player puts one card facedown in front of the banker. By laying them face down, the possibility of a player knowing the other player's selection in advance is eliminated (although revealing one's move does not affect the dominance analysis^{[1]}). At the end of the turn, the banker turns over both cards and gives out the payments accordingly.
If player 1 (red) defects and player 2 (blue) cooperates, player 1 gets the Temptation to Defect payoff of 5 points while player 2 receives the Sucker's payoff of 0 points. If both cooperate they get the Reward for Mutual Cooperation payoff of 3 points each, while if they both defect they get the Punishment for Mutual Defection payoff of 1 point. The checker board payoff matrix showing the payoffs is given below.
Cooperate  Defect  

Cooperate  3, 3  0, 5 
Defect  5, 0  1, 1 
In "winlose" terminology the table looks like this:
Cooperate  Defect  

Cooperate  winwin
 lose muchwin much 
Defect  win muchlose much
 loselose

These point assignments are given arbitrarily for illustration. It is possible to generalize them, as follows:
Cooperate  Defect  

Cooperate  R, R  S, T 
Defect  T, S  P, P 
Where T stands for Temptation to defect, R for Reward for mutual cooperation, P for Punishment for mutual defection and S for Sucker's payoff. To be defined as Prisoner's dilemma, the following inequalities must hold:
T > R > P > S
This condition ensures that the equilibrium outcome is defection, but that cooperation Pareto dominates equilibrium play. In addition to the above condition, if the game is repeatedly played by two players, the following condition should be added.^{[2]}
2 R > T + S
If that condition does not hold, then full cooperation is not necessarily Pareto optimal, as the players are collectively better off by having each player alternate between cooperate and defect.
These rules were established by cognitive scientist Douglas Hofstadter and form the formal canonical description of a typical game of Prisoner's Dilemma.
A simple special case occurs when the advantage of defection over cooperation is independent of what the coplayer does and cost of the coplayers defection is independent of one's own action, i.e. T+S = P+R.
Human behavior in the Prisoner's Dilemma
One experiment based on the simple dilemma found that approximately 40% of participants played "cooperate" (i.e., stayed silent).^{[3]}
The iterated prisoner's dilemma
If two players play Prisoner's Dilemma more than once in succession, having memory of at least one previous game, it is called iterated Prisoner's Dilemma. Amongst results shown by Nobel Prize winner Robert Aumann in his 1959 paper, rational players repeatedly interacting for indefinitely long games can sustain the cooperative outcome. Popular interest in the iterated prisoners dilemma (IPD) was kindled by Robert Axelrod in his book The Evolution of Cooperation (1984). In this he reports on a tournament he organized in which participants have to choose their mutual strategy again and again, and have memory of their previous encounters. Axelrod invited academic colleagues all over the world to devise computer strategies to compete in an IPD tournament. The programs that were entered varied widely in algorithmic complexity, initial hostility, capacity for forgiveness, and so forth.
Axelrod discovered that when these encounters were repeated over a long period of time with many players, each with different strategies, greedy strategies tended to do very poorly in the long run while more altruistic strategies did better, as judged purely by selfinterest. He used this to show a possible mechanism for the evolution of altruistic behaviour from mechanisms that are initially purely selfish, by natural selection.
The best deterministic strategy was found to be "Tit for Tat," which Anatol Rapoport developed and entered into the tournament. It was the simplest of any program entered, containing only four lines of BASIC, and won the contest. The strategy is simply to cooperate on the first iteration of the game; after that, the player does what his opponent did on the previous move. Depending on the situation, a slightly better strategy can be "Tit for Tat with forgiveness." When the opponent defects, on the next move, the player sometimes cooperates anyway, with a small probability (around 1%5%). This allows for occasional recovery from getting trapped in a cycle of defections. The exact probability depends on the lineup of opponents.
By analysing the topscoring strategies, Axelrod stated several conditions necessary for a strategy to be successful.
 Nice
 The most important condition is that the strategy must be "nice", that is, it will not defect before its opponent does (this is sometimes referred to as an "optimistic" algorithm). Almost all of the topscoring strategies were nice; therefore a purely selfish strategy will not "cheat" on its opponent, for purely utilitarian reasons first.
 Retaliating
 However, Axelrod contended, the successful strategy must not be a blind optimist. It must sometimes retaliate. An example of a nonretaliating strategy is Always Cooperate. This is a very bad choice, as "nasty" strategies will ruthlessly exploit such players.
 Forgiving
 Successful strategies must also be forgiving. Though players will retaliate, they will once again fall back to cooperating if the opponent does not continue to play defects. This stops long runs of revenge and counterrevenge, maximizing points.
 Nonenvious
 The last quality is being nonenvious, that is not striving to score more than the opponent (impossible for a ‘nice’ strategy, i.e., a 'nice' strategy can never score more than the opponent).
Therefore, Axelrod reached the oxymoronsounding conclusion that selfish individuals for their own selfish good will tend to be nice and forgiving and nonenvious.
The optimal (pointsmaximizing) strategy for the onetime PD game is simply defection; as explained above, this is true whatever the composition of opponents may be. However, in the iteratedPD game the optimal strategy depends upon the strategies of likely opponents, and how they will react to defections and cooperations. For example, consider a population where everyone defects every time, except for a single individual following the TitforTat strategy. That individual is at a slight disadvantage because of the loss on the first turn. In such a population, the optimal strategy for that individual is to defect every time. In a population with a certain percentage of alwaysdefectors and the rest being TitforTat players, the optimal strategy for an individual depends on the percentage, and on the length of the game.
A strategy called Pavlov (an example of WinStay, LoseSwitch) cooperates at the first iteration and whenever the player and coplayer did the same thing at the previous iteration; Pavlov defects when the player and coplayer did different things at the previous iteration. For a certain range of parameters, Pavlov beats all other strategies by giving preferential treatment to coplayers which resemble Pavlov.
Deriving the optimal strategy is generally done in two ways:
 Bayesian Nash Equilibrium: If the statistical distribution of opposing strategies can be determined (e.g. 50% titfortat, 50% always cooperate) an optimal counterstrategy can be derived analytically.^{[4]}
 Monte Carlo simulations of populations have been made, where individuals with low scores die off, and those with high scores reproduce (a genetic algorithm for finding an optimal strategy). The mix of algorithms in the final population generally depends on the mix in the initial population. The introduction of mutation (random variation during reproduction) lessens the dependency on the initial population; empirical experiments with such systems tend to produce TitforTat players (see for instance Chess 1988), but there is no analytic proof that this will always occur.
Although TitforTat is considered to be the most robust basic strategy, a team from Southampton University in England (led by Professor Nicholas Jennings [2] and consisting of Rajdeep Dash, Sarvapali Ramchurn, Alex Rogers, Perukrishnen Vytelingum) introduced a new strategy at the 20thanniversary Iterated Prisoner's Dilemma competition, which proved to be more successful than TitforTat. This strategy relied on cooperation between programs to achieve the highest number of points for a single program. The University submitted 60 programs to the competition, which were designed to recognize each other through a series of five to ten moves at the start. Once this recognition was made, one program would always cooperate and the other would always defect, assuring the maximum number of points for the defector. If the program realized that it was playing a nonSouthampton player, it would continuously defect in an attempt to minimize the score of the competing program. As a result,^{[5]} this strategy ended up taking the top three positions in the competition, as well as a number of positions towards the bottom.
This strategy takes advantage of the fact that multiple entries were allowed in this particular competition, and that the performance of a team was measured by that of the highestscoring player (meaning that the use of selfsacrificing players was a form of minmaxing). In a competition where one has control of only a single player, TitforTat is certainly a better strategy. Because of this new rule, this competition also has little theoretical significance when analysing single agent strategies as compared to Axelrod's seminal tournament. However, it provided the framework for analysing how to achieve cooperative strategies in multiagent frameworks, especially in the presence of noise. In fact, long before this newrules tournament was played, Richard Dawkins in his book The Selfish Gene pointed out the possibility of such strategies winning if multiple entries were allowed, but remarked that most probably Axelrod would not have allowed them if they had been submitted. It also relies on circumventing rules about the prisoner's dilemma in that there is no communication allowed between the two players. When the Southampton programs engage in an opening "ten move dance" to recognize one another, this only reinforces just how valuable communication can be in shifting the balance of the game.
If an iterated PD is going to be iterated exactly N times, for some known constant N, then it is always game theoretically optimal to defect in all rounds. The only possible Nash equilibrium is to always defect. The proof goes like this: one might as well defect on the last turn, since the opponent will not have a chance to punish the player. Therefore, both will defect on the last turn. Thus, the player might as well defect on the secondtolast turn, since the opponent will defect on the last no matter what is done, and so on. For cooperation to emerge between game theoretic rational players, the total number of rounds must be random, or at least unknown to the players. However, even in this case always defect is no longer a strictly dominant strategy, only a Nash equilibrium. The superrational strategy in this case is to cooperate against a superrational opponent, and in the limit of large fixed N, experimental results on strategies agree with the superrational version, not the gametheoretic rational one.
Another odd case is "play forever" prisoner's dilemma. The game is repeated infinitely many times, and the player's score is the average (suitably computed).
The prisoner's dilemma game is fundamental to certain theories of human cooperation and trust. On the assumption that the PD can model transactions between two people requiring trust, cooperative behaviour in populations may be modelled by a multiplayer, iterated, version of the game. It has, consequently, fascinated many scholars over the years. In 1975, Grofman and Pool estimated the count of scholarly articles devoted to it at over 2,000. The iterated prisoner's dilemma has also been referred to as the "PeaceWar game".^{[6]}
Continuous Iterated Prisoner's Dilemma
Most work on the iterated prisoner's dilemma has focused on the discrete case, in which players either cooperate or defect, because this model is relatively simple to analyze. However, some researchers have looked at models of the continuous iterated prisoner's dilemma, in which players are able to make a variable contribution to the other player. Le and Boyd^{[7]} found that in such situations, cooperation is much harder to evolve than in the discrete iterated prisoner's dilemma. The basic intuition for this result is straightforward: in a continuous prisoner's dilemma, if a population starts off in a noncooperative equilibrium, players who are only marginally more cooperative than noncooperators get little benefit from assorting with one another. By contrast, in a discrete prisoner's dilemma, TitforTat cooperators get a big payoff boost from assorting with one another in a noncooperative equilibrium, relative to noncooperators. Since Nature arguably offers more opportunities for variable cooperation rather than a strict dichotomy of cooperation or defection, the continuous prisoner's dilemma may help explain why reallife examples of TitforTatlike cooperation are extremely rare in Nature (ex. Hammerstein^{[8]}) even though TitforTat seems robust in theoretical models.
Learning psychology and game theory
Where game players can learn to estimate the likelihood of other players defecting, their own behaviour is influenced by their experience of the others' behaviour. Simple statistics show that inexperienced players are more likely to have had, overall, atypically good or bad interactions with other players. If they act on the basis of these experiences (by defecting or cooperating more than they would otherwise) they are likely to suffer in future transactions. As more experience is accrued a truer impression of the likelihood of defection is gained and game playing becomes more successful. The early transactions experienced by immature players are likely to have a greater effect on their future playing than would such transactions affect mature players. This principle goes part way towards explaining why the formative experiences of young people are so influential and why, for example, those who are particularly vulnerable to bullying sometimes become bullies themselves.
The likelihood of defection in a population may be reduced by the experience of cooperation in earlier games allowing trust to build up.^{[9]} Hence selfsacrificing behaviour may, in some instances, strengthen the moral fibre of a group. If the group is small the positive behaviour is more likely to feed back in a mutually affirming way, encouraging individuals within that group to continue to cooperate. This is allied to the twin dilemma of encouraging those people whom one would aid to indulge in behaviour that might put them at risk. Such processes are major concerns within the study of reciprocal altruism, group selection, kin selection and moral philosophy.
Douglas Hofstadter's Superrationality
Douglas Hofstadter in his Metamagical Themas proposed that the definition of "rational" that led "rational" players to defect is faulty. He proposed that there is another type of rational behavior, which he called "superrational", where players take into account that the other person is presumably superrational, like them. Superrational players behave identically, and know that they will behave identically. They take that into account before they maximize their payoffs, and they therefore cooperate.
This view of the oneshot PD leads to cooperation as follows:
 Any superrational strategy will be the same for both superrational players, since both players will think of it.
 therefore the superrational answer will lie on the diagonal of the payoff matrix
 when you maximize return from solutions on the diagonal, you cooperate
However, if a superrational player plays against a rational opponent, he will serve a 10year sentence, and the rational player will go free.
Oneshot cooperation is observed in human culture, wherever religious and ethical codes exist.
Superrationality is not studied by academics, as rationality excludes any superrational behavior.
Morality
While it is sometimes thought that morality must involve the constraint of selfinterest, David Gauthier famously argues that cooperating in the prisoners dilemma on moral principles is consistent with selfinterest and the axioms of game theory.^{[How to reference and link to summary or text]} In his opinion, it is most prudent to give up straightforward maximizing and instead adopt a disposition of constrained maximization, according to which one resolves to cooperate in the belief that the opponent will respond with the same choice, while in the classical PD it is explicitly stipulated that the response of the opponent does not depend on the player's choice. This form of contractarianism claims that good moral thinking is just an elevated and subtly strategic version of basic meansend reasoning.
Douglas Hofstadter expresses a strong personal belief that the mathematical symmetry is reinforced by a moral symmetry, along the lines of the Kantian categorical imperative: defecting in the hope that the other player cooperates is morally indefensible.^{[How to reference and link to summary or text]} If players treat each other as they would treat themselves, then they will cooperate.
Reallife examples
These particular examples, involving prisoners and bag switching and so forth, may seem contrived, but there are in fact many examples in human interaction as well as interactions in nature that have the same payoff matrix. The prisoner's dilemma is therefore of interest to the social sciences such as economics, politics and sociology, as well as to the biological sciences such as ethology and evolutionary biology. Many natural processes have been abstracted into models in which living beings are engaged in endless games of Prisoner's Dilemma (PD). This wide applicability of the PD gives the game its substantial importance.
In politics
In political science, for instance, the PD scenario is often used to illustrate the problem of two states engaged in an arms race. Both will reason that they have two options, either to increase military expenditure or to make an agreement to reduce weapons. Neither state can be certain that the other one will keep to such an agreement; therefore, they both incline towards military expansion. The paradox is that both states are acting rationally, but producing an apparently irrational result. This could be considered a corollary to deterrence theory.
In science
In sociology or criminology, the PD may be applied to an actual dilemma facing two inmates. The game theorist Marek Kaminski, a former political prisoner, analysed the factors contributing to payoffs in the game set up by a prosecutor for arrested defendants (cf. References). He concluded that while the PD is the ideal game of a prosecutor, numerous factors may strongly affect the payoffs and potentially change the properties of the game.
In environmental studies, the PD is evident in crises such as global climate change. All countries will benefit from a stable climate, but any single country is often hesitant to curb [[Carbon dioxideTemplate:Co2]] emissions. The benefit to an individual country to maintain current behavior is greater than the benefit to all countries if behavior was changed, therefore explaining the current impasse concerning climate change.^{[10]}
In program management and technology development, the PD applies to the relationship between the customer and the developer. Capt Dan Ward, an officer in the US Air Force, examined The Program Manager's Dilemma in an article published in Defense AT&L, a defense technology journal.^{[11]}
In sports
PD frequently occurs in cycling races, for instance in the Tour de France. Consider two cyclists halfway in a race, with the peloton (larger group) at great distance behind them. The two riders often work together (mutual cooperation) by sharing the tough load of the front position, where there is no shelter from the wind. If neither of the riders makes an effort to stay ahead, the peloton will soon catch up (mutual defection). An oftenseen scenario is one rider doing the hard work alone (cooperating), keeping the two ahead of the peloton. Nearer to the finish (where the threat of the peloton has disappeared), the game becomes a simple zerosum game, with each rider trying to avoid at all costs giving a slipstream advantage to the other rider. If there was a (single) defecting rider in the preceding prisoners' dilemma, it is usually he who will win this zerosum game, having saved energy in the cooperating rider's slipstream. The cooperating rider's attitude may seem extremely naive, but he often has no other choice when both riders have different physical profiles. The cooperating rider typically has an endurance profile, whereas the defecting rider will more likely be a sprinter. When continuously taking the head position of the twosome, the 'cooperating' rider is merely trying to ride away from the defecting sprinter using his endurance advantage over long distance, thus avoiding a sprint duel at the finish, which he would be bound to lose, even if the sprinting rider had cooperated. Just after the escape from the peloton, the endurancesprinter difference is less of importance, and it is therefore at this stage of the race that mutual cooperation PD can usually be observed. Arguably, it is this almost unavoidable presence of PD (and its transition in zerosum games) that (unconsciously) makes cycling an exciting sport to watch.
PD hardly applies to running sports, because of the negligible importance of air resistance (and shelter from it).
In high school wrestling, sometimes participants intentionally lose unnaturally large amounts of weight so as to compete against lighter opponents. In doing so, the participants are clearly not at their top level of physical and athletic fitness and yet often end up competing against the same opponents anyway, who have also followed this practice (mutual defection). The result is a reduction in the level of competition. Yet if a participant maintains their natural weight (cooperating), they will most likely compete against a stronger opponent who has lost considerable weight.
In economics
Advertising is sometimes cited as a real life example of the prisoner’s dilemma. When cigarette advertising was legal in the United States, competing cigarette manufacturers had to decide how much money to spend on advertising. The effectiveness of Firm A’s advertising was partially determined by the advertising conducted by Firm B. Likewise, the profit derived from advertising for Firm B is affected by the advertising conducted by Firm A. If both Firm A and Firm B chose to advertise during a given period the advertising cancels out, receipts remain constant, and expenses increase due to the cost of advertising. Both firms would benefit from a reduction in advertising. However, should Firm B choose not to advertise, Firm A could benefit greatly by advertising. Nevertheless, the optimal amount of advertising by one firm depends on how much advertising the other undertakes. As the best strategy is dependent on what the other firm chooses there is no dominant strategy and this is not a prisoner's dilemma but rather is an example of a stag hunt. The outcome is similar, though, in that both firms would be better off were they to advertise less than in the equilibrium. Sometimes cooperative behaviors do emerge in business situations. For instance, cigarette manufacturers endorsed the creation of laws banning cigarette advertising, understanding that this would reduce costs and increase profits across the industry.^{[9]} This analysis is likely to be pertinent in many other business situations involving advertising.
Without enforceable agreements, members of a cartel are also involved in a (multiplayer) prisoners' dilemma.^{[12]} 'Cooperating' typically means keeping prices at a preagreed minimum level. 'Defecting' means selling under this minimum level, instantly stealing business (and profits) from other cartel members. Ironically, antitrust authorities want potential cartel members to mutually defect, ensuring the lowest possible prices for consumers.
In law
The theoretical conclusion of PD is one reason why, in many countries, plea bargaining is forbidden. Often, precisely the PD scenario applies: it is in the interest of both suspects to confess and testify against the other prisoner/suspect, even if each is innocent of the alleged crime. Arguably, the worst case is when only one party is guilty — here, the innocent one is unlikely to confess, while the guilty one is likely to confess and testify against the innocent.
In the media
In the 2008 edition of Big Brother (UK), the dilemma was applied to two of the housemates. A prize fund of £50,000 was available. If housemates chose to share the prize fund, each would receive £25,000. If one chose to share, and the other chose to take, the one who took it would receive the entire £50,000. If both chose to take, both housemates would receive nothing. The housemates had a minute to discuss their decision, and were given the possibility to lie. Both housemates declared they would share the prize fund, but either could have potentially been lying. When asked to give their final answers by big brother, both housemates did indeed choose to share, and so won £25,000 each.
Multiplayer dilemmas
Many reallife dilemmas involve multiple players. Although metaphorical, Hardin's tragedy of the commons may be viewed as an example of a multiplayer generalization of the PD: Each villager makes a choice for personal gain or restraint. The collective reward for unanimous (or even frequent) defection is very low payoffs (representing the destruction of the "commons"). Such multiplayer PDs are not formal as they can always be decomposed into a set of classical twoplayer games. The commons are not always exploited: William Poundstone, in a book about the Prisoner's Dilemma (see References below), describes a situation in New Zealand where newspaper boxes are left unlocked. It is possible for someone to take a paper without paying (defecting) but very few do, feeling that if they do not pay then neither will others, destroying the system.
Because there is no mechanism for personal choice to influence others' decisions, this type of thinking relies on correlations between behavior, not on causation. Because of this property, those who do not understand superrationality often mistake it for magical thinking. Without superrationality, not only petty theft, but voluntary voting requires widespread magical thinking, since a nonvoter is a free rider on a democratic system.
Related games
Closedbag exchange
Hofstadter^{[13]} once suggested that people often find problems such as the PD problem easier to understand when it is illustrated in the form of a simple game, or tradeoff. One of several examples he used was "closed bag exchange":
 Two people meet and exchange closed bags, with the understanding that one of them contains money, and the other contains a purchase. Either player can choose to honour the deal by putting into his bag what he agreed, or he can defect by handing over an empty bag.
In this game, defection is always the best course, implying that rational agents will never play. However, in this case both players cooperating and both players defecting actually give the same result, so chances of mutual cooperation, even in repeated games, are few.
Friend or Foe?
Friend or Foe? is a game show that aired from 2002 to 2005 on the Game Show Network in the United States. It is an example of the prisoner's dilemma game tested by real people, but in an artificial setting. On the game show, three pairs of people compete. As each pair is eliminated, they play a game of Prisoner's Dilemma to determine how their winnings are split. If they both cooperate (Friend), they share the winnings 5050. If one cooperates and the other defects (Foe), the defector gets all the winnings and the cooperator gets nothing. If both defect, both leave with nothing. Notice that the payoff matrix is slightly different from the standard one given above, as the payouts for the "both defect" and the "cooperate while the opponent defects" cases are identical. This makes the "both defect" case a weak equilibrium, compared with being a strict equilibrium in the standard prisoner's dilemma. If you know your opponent is going to vote Foe, then your choice does not affect your winnings. In a certain sense, Friend or Foe has a payoff model between "Prisoner's Dilemma" and "Chicken".
The payoff matrix is
Cooperate  Defect  

Cooperate  1, 1  0, 2 
Defect  2, 0  0, 0 
This payoff matrix was later used on the British television programmes Shafted and Golden Balls.
See also
 Centipede game
 Conflict resolution research
 Diner's dilemma
 Entrapment games
 Evolutionarily stable strategy
 Folk theorem (game theory)
 Nash equilibrium
 Non zero sum game
 Neuroeconomics
 Price equation
 Reciprocal altruism
 Rendezvous problem
 Superrationality
 Tit for tat
 Tragedy of the commons
 Tragedy of the anticommons
 Traveler's dilemma
 Trust (sociology)
 Social trap
 War of attrition (game)
 Zerosum
Notes
 ↑ A simple "tell" that partially or wholly reveals one player's choice — such as the Red player playing their Cooperate card faceup — does not change the fact that Defect is the dominant strategy. When one is considering the game itself, communication has no effect whatsoever. However, when the game is being played in real life considerations outside of the game itself may cause communication to matter. It is a point of utmost importance to the full implications of the dilemma that when we do not need to take into account external considerations, singleinstance Prisoner's Dilemma is not affected in any way by communications. Even in singleinstance Prisoner's Dilemma, meaningful prior communication about issues external to the game could alter the play environment, by raising the possibility of enforceable side contracts or credible threats. For example, if the Red player plays their Cooperate card faceup and simultaneously reveals a binding commitment to blow the jail up if and only if Blue Defects (with additional payoff 11,10), then Blue's Cooperation becomes dominant. As a result, players are screened from each other and prevented from communicating outside of the game.
 ↑ Dawkins, Richard (1989). The Selfish Gene, Oxford University Press. ISBN 0192860925. Page: 204 of Paperback edition
 ↑ Tversky, Amos (2004). Preference, Belief, and Similarity: Selected Writings, MIT Press.
 ↑ For example see the 2003 study “Bayesian Nash equilibrium; a statistical test of the hypothesis” for discussion of the concept and whether it can apply in real economic or strategic situations (from Tel Aviv University).
 ↑ The 2004 Prisoner's Dilemma Tournament Results show University of Southampton's strategies in the first three places, despite having fewer wins and many more losses than the GRIM strategy. (Note that in a PD tournament, the aim of the game is not to “win” matches — that can easily be achieved by frequent defection). It should also be pointed out that even without implicit collusion between software strategies (exploited by the Southampton team) titfortat is not always the absolute winner of any given tournament; it would be more precise to say that its long run results over a series of tournaments outperform its rivals. (In any one event a given strategy can be slightly better adjusted to the competition than titfortat, but titfortat is more robust). The same applies for the titfortatwithforgiveness variant, and other optimal strategies: on any given day they might not 'win' against a specific mix of counterstrategies.An alternative way of putting it is using the Darinian ESS simulation. In such a simulation TitforTat will almost always come to dominate, though nasty strategies will drift in and out of the population because a TitforTat population is penetratable by nonretaliating nice strategies which in turn are easy prey for the nasty strategies. Richard Dawkins showed that here no static mix of strategies form a stable equilibrium and the system will always oscillate between bounds.
 ↑ Shy, O., 1996, Industrial Organization: Theory and Applications, Cambridge, Mass.: The MIT Press.
 ↑ Le, S. and R. Boyd (2007) "Evolutionary Dynamics of the Continuous Iterated Prisoner's Dilemma" Journal of Theoretical Biology, Volume 245, 258–267.
 ↑ Hammerstein, P. (2003). Why is reciprocity so rare in social animals? A protestant appeal. In: P. Hammerstein, Editor, Genetic and Cultural Evolution of Cooperation, MIT Press. pp. 83–94.
 ↑ ^{9.0} ^{9.1} This argument for the development of cooperation through trust is given in The Wisdom of Crowds , where it is argued that longdistance capitalism was able to form around a nucleus of Quakers, who always dealt honourably with their business partners. (Rather than defecting and reneging on promises — a phenomenon that had discouraged earlier longterm unenforceable overseas contracts). It is argued that dealings with reliable merchants allowed the meme for cooperation to spread to other traders, who spread it further until a high degree of cooperation became a profitable strategy in general commerce
 ↑ The Economist (2007) [1].
 ↑ Ward, D. (2004) The Program Manager's Dilemma The Program Manager's Dilemma (Defense AT&L, Defense Acquisition University Press).
 ↑ Nicholson, Walter (2000), Intermediate Microeconomics (8th ed.), Harcourt
 ↑ Hofstadter, Douglas R. (1985). Metamagical Themas: questing for the essence of mind and pattern, Bantam Dell Pub Group. ISBN 0465045669.  see Ch.29 The Prisoner's Dilemma Computer Tournaments and the Evolution of Cooperation.
References
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 Robert Aumann, “Acceptable points in general cooperative nperson games”, in R. D. Luce and A. W. Tucker (eds.), Contributions to the Theory 23 of Games IV, Annals of Mathematics Study 40, 287–324, Princeton University Press, Princeton NJ.
 Axelrod, R. (1984). The Evolution of Cooperation. ISBN 0465021212
 Bicchieri, Cristina (1993). Rationality and Coordination. Cambridge University Press
 Kenneth Binmore, Fun and Games.
 David M. Chess (1988). Simulating the evolution of behavior: the iterated prisoners' dilemma problem. Complex Systems, 2:663–670.
 Dresher, M. (1961). The Mathematics of Games of Strategy: Theory and Applications PrenticeHall, Englewood Cliffs, NJ.
 Flood, M.M. (1952). Some experimental games. Research memorandum RM789. RAND Corporation, Santa Monica, CA.
 Kaminski, Marek M. (2004) Games Prisoners Play Princeton University Press. ISBN 0691117217 http://webfiles.uci.edu/mkaminsk/www/book.html
 Poundstone, W. (1992) Prisoner's Dilemma Doubleday, NY NY.
 Greif, A. (2006). Institutions and the Path to the Modern Economy: Lessons from Medieval Trade. Cambridge University Press, Cambridge, UK.
 Rapoport, Anatol and Albert M. Chammah (1965). Prisoner's Dilemma. University of Michigan Press.
 S. Le and R. Boyd (2007) "Evolutionary Dynamics of the Continuous Iterated Prisoner's Dilemma" Journal of Theoretical Biology, Volume 245, 258–267. Full text
 A. Rogers, R. K. Dash, S. D. Ramchurn, P. Vytelingum and N. R. Jennings (2007) “Coordinating team players within a noisy iterated Prisoner’s Dilemma tournament” Theoretical Computer Science 377 (13) 243259. [3]
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Further reading
Books
 Bicchieri, Cristina and Mitchell Green (1997) "Symmetry Arguments for Cooperation in the Prisoner's Dilemma", in G. HolmstromHintikka and R. Tuomela (eds.), Contemporary Action Theory: The Philosophy and Logic of Social Action, Kluwer.
Papers
 Plous, S. (1993). Prisoner's Dilemma or Perceptual Dilemma? Journal of Peace Research, Vol. 30, No. 2, 163179.
Dissertations
External links
 Prisoner's Dilemma (Stanford Encyclopedia of Philosophy)
 Effects of Tryptophan Depletion on the Performance of an Iterated Prisoner's Dilemma Game in Healthy Adults  Nature Neuropsychopharmacology
 Is there a "dilemma" in Prisoner's Dilemma by Elmer G. Wiens
 "Games Prisoners Play"  gametheoretic analysis of interactions among actual prisoners, including PD.
 Play an iterated prisoner's dilemma game.
 Another version of the iterated prisoner's dilemma game
 Iterated prisoner's dilemma game applied to Big Brother TV show situation.
 The Bowerbird's Dilemma The Prisoner's Dilemma in ornithology — mathematical cartoon by Larry Gonnick.
 Examples of Prisoners' dilemma
 Multiplayer game based on prisoner dilemma Play Prisoner's Dilemma over IRC or internet — by Axiologic Research.
 The Edge cites Robert Axelrod's book and discusses the success of U2 following the principles of IPD.
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