Graphical game theory

In game theory, the common ways to describe a game are the normal form and the extensive form. The graphical form is an alternate compact representation of a game using the interaction among participants.

Consider a game with ${\displaystyle n}$ players with ${\displaystyle m}$ strategies each. We will represent the players as nodes in a graph in which each player has a utility function that depends only on him and his neighbors. As the utility function depends on fewer other players, the graphical representation would be smaller.

A graphical game is represented by a graph ${\displaystyle G}$, in which each player is represented by a node, and there is an edge between two nodes ${\displaystyle i}$ and ${\displaystyle j}$ iff their utility functions are dependent on the strategy which the other player will choose. Each node ${\displaystyle i}$ in ${\displaystyle G}$ has a function ${\displaystyle u_{i}:\{1\ldots m\}^{d_{i}+1}\rightarrow \mathbb {R} }$, where ${\displaystyle d_{i}}$ is the degree of vertex ${\displaystyle i}$. ${\displaystyle u_{i}}$ specifies the utility of player ${\displaystyle i}$ as a function of his strategy as well as those of his neighbors.

For a general ${\displaystyle n}$ players game, in which each player has ${\displaystyle m}$ possible strategies, the size of a normal form representation would be ${\displaystyle O(m^{n})}$. The size of the graphical representation for this game is ${\displaystyle O(m^{d})}$ where ${\displaystyle d}$ is the maximal node degree in the graph. If ${\displaystyle d\ll n}$, then the graphical game representation is much smaller.

In case where each player's utility function depends only on one other player:

• 
  The graphical form of the described game

The maximal degree of the graph is 1, and the game can be described as ${\displaystyle n}$ functions (tables) of size ${\displaystyle m^{2}}$. So, the total size of the input will be ${\displaystyle nm^{2}}$.

Finding Nash equilibrium in a game takes exponential time in the size of the representation. If the graphical representation of the game is a tree, we can find the equilibrium in polynomial time. In the general case, where the maximal degree of a node is 3 or more, the problem is NP-complete.

• Michael Kearns (2007) "Graphical Games". In Vazirani, Vijay V.; Nisan, Noam; Roughgarden, Tim; Tardos, Éva (2007). Algorithmic Game Theory (PDF). Cambridge, UK: Cambridge University Press. ISBN 0-521-87282-0.
• Michael Kearns, Michael L. Littman and Satinder Singh (2001) "Graphical Models for Game Theory".