Game theory

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Game theory is a branch of applied mathematics that studies strategic situations where players choose different actions in an attempt to maximize their returns. First developed as a tool for understanding economic behavior, game theory is now used in many diverse academic fields ranging from biology to philosophy. Game theory saw substantial growth during the cold war because of its application to military strategy, most notably to the concept of mutually assured destruction. Beginning in the 1970s game theory has been applied to animal behavior, including species' development by natural selection. Because of interesting games like the Prisoner's dilemma, where mutual self interest hurts everyone, game theory has been used in ethics and philosophy. Finally, game theory has recently drawn attention from computer scientists because of its use in artificial intelligence and cybernetics.

In addition to its academic interest, game theory has received some attention in popular culture. An important figure in game theory, John Nash was the subject of a 2001 film, A Beautiful Mind. Several game shows have adopted game theoretic situations, including the game show Friend or Foe.[1]

Although similar to decision theory, game theory studies decisions that are made in an environment where various players interact. In other words, game theory studies choice of optimal behavior when costs and benefits of each option are not fixed, but depend upon the choices of other individuals.

Representation of games

The games studied by game theory are well defined mathematical objects. A game consists of a set of players, set of moves (or strategies) available to those players, and a specification of payoffs for each strategy profile. There are two ways of representing games that are common in the literature.

Normal form

A normal form game
Player 2 chooses left Player 2 chooses right
Player 1 chooses top 4, 3 -1, -1
Player 1 chooses bottom 0, 0 3, 4

Template:Seemain The normal (or strategic form) game is a matrix which shows the players, strategies, and payoffs (see the example to the right). Here there are two players, one chooses the row and the other chooses the column. Each player has two strategies, which is specified by the number of rows and the number of columns. The payoffs are provided in the interior. The first number is the payoff received by the row player (Player 1 in our example) the second is the payoff for the column player (Player 2 in our example). Suppose that Player 1 plays top and that Player 2 plays left. Then Player 1 gets 4 and Player 2 gets 3.

When a game is presented in normal form, it is presumed that each player acts simultaneously or, at least, without knowing the actions of the other. If players have some information about the choices of other players, the game is usually presented in extensive form.

Extensive form


An extensive form game

Extensive form games attempt to capture games with some important order. Games here are presented as trees (as pictured to the left). Here each vertex (or node) represents a point of choice for a player. The player is specified by a number listed by the vertex. The lines out of the vertex represents a possible action for that player. The payoffs are specified at the bottom of the tree.

In the game pictured here, there are two players. Player 1 moves first and chooses either F or U. Player 2 sees Player 1's move and then chooses A or R. Suppose that Player 1 chooses U and then Player 2 chooses A, then Player 1 gets 8 and Player 2 gets 2.

Extensive form games can also capture simultaneous move games as well. Either a dotted line or circle is drawn around two different vertices to represent them as being part of the same information set (i.e. the players do not know at which point they are).

Types of games

Symmetric and asymmetric


An asymmetric game
E 1, 2 0, 0
F 0, 0 1, 2

A symmetric game is a game where the payoffs for playing a particular strategy depend only on the other strategies employed, not on who is playing them. If one can change the identities of the players without changing the payoff to the strategies, then a game is symmetric. Many of the commonly studied 2x2 games are symmetric. The standard representations of Chicken, the Prisoner's Dilemma, Battle of the sexes, and the Stag hunt are all symmetric games. [2]

Most commonly studied asymmetric games are games where there are not identical strategy sets for both players. For instance, the Ultimatum game and similar Dictator game have different strategies for each player. It is possible, however, for a game to have identical strategies for both players, yet be asymmetric. For example, the game pictured to the right is asymmetric despite having identical strategy sets for both players.

Zero sum and non-zero sum


A Zero-Sum Game
A 2, -2 -1, 1
B -1, 1 3, -3

In zero-sum games the total benefit to all players in the game, for every combination of strategies, always adds to zero (or more informally put, a player benefits only at the expense of others). Poker exemplifies a zero-sum game, because one wins exactly the amount one's opponents lose. Other zero sum games include the Ultimatum game, Matching pennies, Go, and Chess. In fact, any game where one player wins and another loses is an example of a zero sum game. Most games studied by game theorists (including the famous Prisoner's Dilemma) are non-zero-sum games, because some outcomes have net results greater or less than zero. Informally, a gain by one player does not necessarily correspond with a loss by another.

It is possible to transform any game into a zero-sum game by adding an additional dummy player (often called "the board"), whose losses compensate the players' net winnings.

Simultaneous and sequential


Simultaneous games are games where both players move simultaneously, or if they don't move simultaneously, the later players are unaware of the earlier players' actions (making them effectively simultaneous). Sequential games (or dynamic games) are games where later players have some knowledge about earlier actions. This need not be perfect knowledge about every action of earlier players; it might be very little information. For instance, a player may know that an earlier player did not perform one particular action, while she does not know which of the other available actions the first player actually performed.

The difference between simultaneous and sequential games is captured in the different representations discussed above. Normal form is used to represent simultaneous games and extensive form is used to represent sequential ones.

Perfect information and imperfect information

File:PD with outside option.png
A game of imperfect information (the dotted line represents ignorance on the part of player 2)


An important subset of sequential games consists of games of perfect information. A game is a game of perfect information if all players know the moves made by all other players before. Thus, only sequential games can be games of perfect information, since in simultaneous games not every player knows the actions of the others. Most games studied in game theory are imperfect information games, although some interesting games are games of perfect information including the Ultimatum Game and Centipede Game.

Perfect information is often confused with complete information, which is a similar concept. Complete information requires that every player know the strategies and payoffs of the other players but not necessarily the actions.

Infinitely long games


For obvious reasons, games as studied by economists and real-world game players are generally finished in finitely many moves. Pure mathematicians are not so constrained, and set theorists in particular study games that last for infinitely many moves, with the winner (or other payoff) not known until after all those moves are completed.

The focus of attention is usually not so much on what is the best way to play such a game, but simply on whether one or the other player has a winning strategy. (It can be proved, using the axiom of choice, that there are games--even with perfect information, and where the only outcomes are "win" or "lose"--for which neither player has a winning strategy.) The existence of such strategies, for cleverly designed games, has important consequences in descriptive set theory.

Uses of game theory

Games in one form or another are widely used in many different academic disciplines.

Economics and business

The primary focus of game theory in economics is analysis of particular sets of strategies known as equilibria in games. These "solution concepts" are usually based on what is required by norms of rationality. The most famous of these is the Nash equilibrium. A set of strategies is a Nash equilibrium if each represents a best response to the other strategies. So, if all the players are playing the strategies in a Nash equilibrium, they have no incentive to deviate, since their strategy is the best they can do given what others are doing.

The payoffs of the game are generally taken to represent the utility of individual players. Often in modelling situations the payoffs represent money, which one presumes corresponds to an individual's utility. This assumption, however, can be faulty.

A prototypical paper on game theory in economics begins by presenting a game that is an abstraction of some particular economic situation. One or more solution concepts are chosen and the author demonstrates which strategy sets in the presented game are equilibria of the appropriate type. Naturally one might wonder to what use should this information be put. Economists and business professors suggest two primary uses.


File:Centipede game.png
A three stage Centipede Game

The first use is to inform us about how actual human populations behave. Some scholars believe that by finding the equilibria of games they can predict how actual human populations will behave when confronted with situations analogous to the game being studied. This particular view of game theory has come under recent criticism. First, it is criticized because the assumptions made by game theorists are often violated. Game theorists may assume players always act rationally to maximize their wins (the Homo economicus model), but real humans often act either irrationally, or act rationally to maximize the wins of some larger group of people (altruism). Game theorists respond by comparing their assumptions to those used in physics. Thus while their assumptions do not always hold, they can treat game theory as a reasonable scientific ideal akin to the models used by physicists. However, additional criticism of this use of game theory has been levied because some experiments have demonstrated that individuals do not play equilibrium strategies. For instance, in the Centipede game, Guess 2/3 of the average game, and the Dictator game people regularly do not play the Nash equilibrium. There is an ongoing debate regarding the importance of these experiments. [3]

Alternatively, some authors claim that Nash equilibria do not provide predictions for human populations, but rather provide an explanation for why populations that play Nash equilibria remain in that state. However, the question of how populations reach those points remain open.

Some game theorists have turned to evolutionary game theory in order to resolve these worries. These models presume either no rationality or bounded rationality on the part of players. Despite the name, evolutionary game theory does not necessarily presume natural selection in the biological sense. Evolutionary game theory includes both biological as well a cultural evolution and also models of individual learning (for example fictitious play dynamics).


The Prisoner's Dilemma
Cooperate Defect
Cooperate 2, 2 0, 3
Defect 3, 0 1, 1

On the other hand, some scholars see game theory not as a predictive tool for the behavior of human beings, but as a suggestion for how people ought to behave. Since a Nash equilibrium of a game constitutes ones best response to the actions of the other players, playing a strategy that is part of a Nash equilibrium seems appropriate. However, this use for game theory has also come under criticism. First, in some cases it is appropriate to play a non-equilibrium strategy if one expects others to play non-equilibrium strategies as well. For an example, see Guess 2/3 of the average.

Second, the Prisoner's Dilemma presents another potential counter-example. In the Prisoner's Dilemma each player pursuing his own self-interest leads both players to be worse off than had they not pursued their own self-interests. Some scholars believe that this demonstrates the failure of game theory as a recommendation for behavior.


Hawk Dove
Hawk (V-C)/2, (V-C)/2 V, 0
Dove 0, V V/2, V/2

Unlike economics, the payoffs for games in biology are often interpreted as corresponding to fitness. In addition, the focus has less been on equilibria that correspond to a notion of rationality, but rather on ones that would be maintained by evolutionary forces. The most well known equilibrium in biology is know as the Evolutionary stable strategy or (ESS), and was first introduced by John Maynard Smith (described in his 1982 book). Although its initial motivation did not involve any of the mental requirements of the Nash equilibrium, every ESS is a Nash equilibrium.

In biology, game theory has been used to understand many different phenomena. It was first used to explain the evolution (and stability) of the approximate 1:1 sex ratios. Ronald Fisher (1930) suggested that the 1:1 sex ratios are a result of evolutionary forces acting on individuals who could be seen as trying to maximize their number of grandchildren.

Additionally, biologists have used evolutionary game theory and the ESS to explain the emergence of animal communication (Maynard Smith & Harper, 2003). The analysis of signaling games and other communication games, has provided some insight into the evolution of communication among animals.

Finally, biologists have used the Hawk-Dove game (also known as Chicken) to analyze fighting behavior and territoriality.

Computer science and logic

Game theory has come to play an increasingly important role in logic and in computer science. Several logical theories have a basis in game semantics. In addition, computer scientists have used games to model interactive computations. Computability logic attempts to develop a comprehensive formal theory (logic) of interactive computational tasks and resources, formalising these entities as games between a computing agent and its environment.


Game theory has been put to several uses in philosophy. Responding to two papers by W.V.O. Quine (1960, 1967), David Lewis (1969) used game theory to develop a philosophical account of convention. In so doing, he provided the first analysis of common knowledge and employed it in analyzing play in coordination games. In addition, he first suggested that one can understand meaning in terms of signaling games. This later suggestion has been pursued by several philosophers since Lewis (Skyrms 1996, Grim et al. 2004).

The Stag Hunt
Stag Hare
Stag 3, 3 0, 2
Hare 2, 0 2, 2

In ethics, some authors have attempted to pursue the project, begun by Thomas Hobbes, of deriving morality from self-interest. Since games like the Prisoner's Dilemma present an apparent conflict between morality and self-interest, explaining why cooperation is required by self interest is an important component of this project. This general strategy is a component of the general social contract view in political philosophy (for examples, see Gauthier 1987 and Kavka 1986). [4]

Finally, other authors have attempted to use evolutionary game theory in order to explain the emergence of our attitudes about morality. These authors look at several games including the Prisoner's Dilemma, Stag hunt, and the Nash bargaining game as providing an explanation for the emergence of attitudes about morality (see e.g. Skyrms 1996, 2004; Sober and Wilson 1999).

History of game theory

Though touched on by earlier mathematical results, modern game theory became a prominent branch of mathematics in the 1940s, especially after the 1944 publication of The Theory of Games and Economic Behavior by John von Neumann and Oskar Morgenstern. This profound work contained the method for finding optimal solutions for two-person zero-sum games. During this time period, work on game theory was primarily focused on cooperative game theory. This type of game theory analyzed optimal strategies for groups of individuals, presuming that they can enforce agreements between them about proper strategies.

Around 1950, John Nash developed a definition of an "optimum" strategy for multi-player games where no such optimum was previously defined, known as Nash equilibrium. This equilibrium was sufficiently general, allowing for the analysis of non-cooperative games in addition to cooperative ones. Reinhard Selten with his solution concept of trembling hand perfect and subgame perfect equilibria further refined this concept. The two won The Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel (also known as The Nobel Prize in Economics) in 1994 for their work on game theory, along with John Harsanyi who developed the analysis of games of incomplete information.

A different line of development, and one applied to typical recreational games, is that growing from the analysis of Nim. This is now a separate area described as combinatorial game theory.

In 2005, the game theorists Thomas Schelling and Robert Aumann won the Nobel Prize in Economics. Schelling worked on dynamic models, early examples of evolutionary game theory. Aumann contributed more to the equilibrium school, developing an equilibrium refinement correlated equilibrium and developing extensive analysis of the assumption of common knowledge.


  1. ^ has an extensive list of references to game theory in popular culture.
  2. ^  Some scholars would consider certain asymmetric games as examples of these games as well. However, the most common payoffs for each of these games are symmetric.
  3. ^  Experimental work in game theory goes by many names, experimental economics, behavioral economics, and behavioral game theory are several. For a recent discussion on this field see Camerer 2003.
  4. ^  For a more detailed discussion of the use of Game Theory in ethics see the Stanford Encyclopedia of Philosophy's entry game theory and ethics.


Textbooks and general reference texts
  • Gibbons, Robert (1992) Game Theory for Applied Economists, Princeton University Press ISBN 0691003955 (readable; suitable for advanced undergraduates. Published in Europe by Harvester Wheatsheaf (London) with the title A primer in game theory)
  • Ginits, Herbert (2000) Game Theory Evolving Princeton University Press ISBN 0691009430
  • Osborne, Martin and Ariel Rubinstein: A Course in Game Theory, MIT Press, 1994, ISBN 0-262-65040-1 (modern introduction at the introductory graduate level)
  • Fudenberg, Drew and Jean Tirole: Game Theory, MIT Press, 1991, ISBN 0262061414 (the definitive reference text)
Historically important texts
Other print references
  • Camerer, Colin (2003) Behavioral Game Theory Princeton University Press ISBN 0691090394
  • Gauthier, David (1987) Morals by Agreement Oxford University Press ISBN 0198249926
  • Grim, Patrick, Trina Kokalis, Ali Alai-Tafti, Nicholas Kilb, and Paul St Denis (2004) "Making meaning happen." Journal of Experimental & Theoretical Artificial Intelligence 16(4): 209-243.
  • Kavka, Gregory (1986) Hobbesian Moral and Political Theory Princeton University Press. ISBN 069102765X
  • Lewis, David (1969) Convention: A Philosophical Study
  • Maynard Smith, J. and Harper, D. (2003) Animal Signals. Oxford University Press. ISBN 0198526857
  • Quine, W.v.O (1967) "Truth by Convention" in Philosophica Essays for A.N. Whitehead Russel and Russel Publishers. ISBN 0846209705
  • Quine, W.v.O (1960) "Carnap and Logical Truth" Synthese 12(4):350-374.
  • Skyrms, Brian (1996) Evolution of the Social Contract Cambridge University Press. ISBN 0521555833
  • Skyrms, Brian (2004) The Stag Hunt and the Evolution of Social Structure Cambridge University Press. ISBN 0521533929.
  • Sober, Elliot and David Sloan Wilson (1999) Unto Others: The Evolution and Psychology of Unselfish Behavior Harvard University Press. ISBN 0674930479

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