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Continuous game

A continuous game is a mathematical concept, used in game theory, that generalizes the idea of an ordinary game like tic-tac-toe or checkers (draughts). In other words, it extends the notion of a discrete game, where the players choose from a finite set of pure strategies. The continuous game concepts allows games to include more general sets of pure strategies, which may be uncountably infinite.

Formal definition
Define the n-player continuous game G = (P, \mathbf{C}, \mathbf{U}) where ::P = {1, 2, 3,\ldots, n} is the set of n\, players, :: \mathbf{C}= (C_1, C_2, \ldots, C_n) where each C_i\, is a compact set, in a metric space, corresponding to the i\, th player's set of pure strategies, :: \mathbf{U}= (u_1, u_2, \ldots, u_n) where u_i:\mathbf{C}\to \R is the utility function of player i\, : We define \Delta_i\, to be the set of Borel probability measures on C_i\, , giving us the mixed strategy space of player i. : Define the strategy profile \boldsymbol{\sigma} = (\sigma_1, \sigma_2, \ldots, \sigma_n) where \sigma_i \in \Delta_i\, Let \boldsymbol{\sigma}_{-i} be a strategy profile of all players except for player i. As with discrete games, we can define a best response correspondence for player i\, , b_i\ . b_i\, is a relation from the set of all probability distributions over opponent player profiles to a set of player i's strategies, such that each element of :b_i(\sigma_{-i})\, is a best response to \sigma_{-i}. Define :\mathbf{b}(\boldsymbol{\sigma}) = b_1(\sigma_{-1}) \times b_2(\sigma_{-2}) \times \cdots \times b_n(\sigma_{-n}). A strategy profile \boldsymbol{\sigma}* is a Nash equilibrium if and only if \boldsymbol{\sigma}* \in \mathbf{b}(\boldsymbol{\sigma}*) The existence of a Nash equilibrium for any continuous game with continuous utility functions can be proven using Irving Glicksberg's generalization of the Kakutani fixed point theorem. In general, there may not be a solution if we allow strategy spaces, C_i\, 's which are not compact, or if we allow non-continuous utility functions. Separable games A separable game is a continuous game where, for any i, the utility function u_i:\mathbf{C}\to \R can be expressed in the sum-of-products form: : u_i(\mathbf{s}) = \sum_{k_1=1}^{m_1} \ldots \sum_{k_n=1}^{m_n} a_{i\, ,\, k_1\ldots k_n} f_1(s_1)\ldots f_n(s_n), where \mathbf{s} \in \mathbf{C}, s_i \in C_i, a_{i\, ,\, k_1\ldots k_n} \in \R, and the functions f_{i\, ,\, k}:C_i \to \R are continuous. A polynomial game is a separable game where each C_i\, is a compact interval on \R\, and each utility function can be written as a multivariate polynomial. In general, mixed Nash equilibria of separable games are easier to compute than non-separable games as implied by the following theorem: :For any separable game there exists at least one Nash equilibrium where player i mixes at most m_i+1\, pure strategies. Whereas an equilibrium strategy for a non-separable game may require an uncountably infinite support, a separable game is guaranteed to have at least one Nash equilibrium with finitely supported mixed strategies. ==Examples==
Examples
Separable games A polynomial game Consider a zero-sum 2-player game between players X and Y, with C_X = C_Y = \left [0,1 \right ] . Denote elements of C_X\, and C_Y\, as x\, and y\, respectively. Define the utility functions H(x,y) = u_x(x,y) = -u_y(x,y)\, where :H(x,y)=(x-y)^2\, . The pure strategy best response relations are: :b_X(y) = \begin{cases} 1, & \mbox{if }y \in \left [0,1/2 \right ) \\ 0\text{ or }1, & \mbox{if }y = 1/2 \\ 0, & \mbox{if } y \in \left (1/2,1 \right ] \end{cases} :b_Y(x) = x\, b_X(y)\, and b_Y(x)\, do not intersect, so there is no pure strategy Nash equilibrium. However, there should be a mixed strategy equilibrium. To find it, express the expected value, v = \mathbb{E} [H(x,y)] as a linear combination of the first and second moments of the probability distributions of X and Y: : v = \mu_{X2} - 2\mu_{X1} \mu_{Y1} + \mu_{Y2}\, (where \mu_{XN} = \mathbb{E} [x^N] and similarly for Y). The constraints on \mu_{X1}\, and \mu_{X2} (with similar constraints for y,) are given by Hausdorff as: : \begin{align} \mu_{X1} \ge \mu_{X2} \\ \mu_{X1}^2 \le \mu_{X2} \end{align} \qquad \begin{align} \mu_{Y1} \ge \mu_{Y2} \\ \mu_{Y1}^2 \le \mu_{Y2} \end{align} Each pair of constraints defines a compact convex subset in the plane. Since v\, is linear, any extrema with respect to a player's first two moments will lie on the boundary of this subset. Player i's equilibrium strategy will lie on : \mu_{i1} = \mu_{i2} \text{ or } \mu_{i1}^2 = \mu_{i2} Note that the first equation only permits mixtures of 0 and 1 whereas the second equation only permits pure strategies. Moreover, if the best response at a certain point to player i lies on \mu_{i1} = \mu_{i2}\, , it will lie on the whole line, so that both 0 and 1 are a best response. b_Y(\mu_{X1},\mu_{X2})\, simply gives the pure strategy y = \mu_{X1}\, , so b_Y\, will never give both 0 and 1. However b_x\, gives both 0 and 1 when y = 1/2. A Nash equilibrium exists when: : (\mu_{X1}*, \mu_{X2}*, \mu_{Y1}*, \mu_{Y2}*) = (1/2, 1/2, 1/2, 1/4)\, This determines one unique equilibrium where Player X plays a random mixture of 0 for 1/2 of the time and 1 the other 1/2 of the time. Player Y plays the pure strategy of 1/2. The value of the game is 1/4. Non-Separable Games A rational payoff function Consider a zero-sum 2-player game between players X and Y, with C_X = C_Y = \left [0,1 \right ] . Denote elements of C_X\, and C_Y\, as x\, and y\, respectively. Define the utility functions H(x,y) = u_x(x,y) = -u_y(x,y)\, where :H(x,y)=\frac{(1+x)(1+y)(1-xy)}{(1+xy)^2}. This game has no pure strategy Nash equilibrium. It can be shown that a unique mixed strategy Nash equilibrium exists with the following pair of cumulative distribution functions: : F^*(x) = \frac{4}{\pi} \arctan{\sqrt{x}} \qquad G^*(y) = \frac{4}{\pi} \arctan{\sqrt{y}}. Or, equivalently, the following pair of probability density functions: : f^*(x) = \frac{2}{\pi \sqrt{x} (1+x)} \qquad g^*(y) = \frac{2}{\pi \sqrt{y} (1+y)}. The value of the game is 4/\pi. Requiring a Cantor distribution Consider a zero-sum 2-player game between players X and Y, with C_X = C_Y = \left [0,1 \right ] . Denote elements of C_X\, and C_Y\, as x\, and y\, respectively. Define the utility functions H(x,y) = u_x(x,y) = -u_y(x,y)\, where :H(x,y)=\sum_{n=0}^\infty \frac{1}{2^n}\left(2x^n-\left (\left(1-\frac{x}{3} \right )^n-\left (\frac{x}{3}\right)^n \right ) \right ) \left(2y^n - \left (\left(1-\frac{y}{3} \right )^n-\left (\frac{y}{3}\right)^n \right ) \right ). This game has a unique mixed strategy equilibrium where each player plays a mixed strategy with the Cantor singular function as the cumulative distribution function. ==Further reading==
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