NAME
Game::Theory::TwoPersonMatrix - Analyze a 2 person matrix game
VERSION
version 0.2207
SYNOPSIS
use Game::Theory::TwoPersonMatrix;
# zero-sum game
my $g = Game::Theory::TwoPersonMatrix->new(
1 => { 1 => 0.2, 2 => 0.3, 3 => 0.5 },
2 => { 1 => 0.1, 2 => 0.7, 3 => 0.2 },
payoff => [ [-5, 4, 6],
[ 3,-2, 2],
[ 2,-3, 1] ]
);
$g->col_reduce;
$g->row_reduce;
my $x = $g->saddlepoint;
$x = $g->oddments;
$x = $g->expected_payoff;
my $player = 1;
$x = $g->counter_strategy($player);
$x = $g->play;
# non-zero-sum game
$g = Game::Theory::TwoPersonMatrix->new(
1 => { 1 => 0.1, 2 => 0.2, 3 => 0.7 },
2 => { 1 => 0.1, 2 => 0.2, 3 => 0.3, 4 => 0.4 },
# Payoff table for the row player
payoff1 => [ [5,3,8,2], # 1st strategy
[6,5,7,1], # 2
[7,4,6,0] ], # 3
# Payoff table for the column player (opponent)
# 1 2 3 4th strategy
payoff2 => [ [2,0,1,3],
[3,4,4,1],
[5,6,8,2] ],
);
$x = $g->mm_tally;
$x = $g->pareto_optimal;
$x = $g->nash;
$x = $g->expected_payoff;
$x = $g->counter_strategy($player);
$x = $g->play;DESCRIPTION
Game::Theory::TwoPersonMatrix analyzes a two person matrix game of player numbers, strategies and utilities ("payoffs").
Players 1 and 2 are the "row" and "column" players, respectively. This is due to the tabular format of a matrix game:
Player 2
--------
Strategy 0.5 0.5
Player | 0.5 1 -1 < Payoff
1 | 0.5 -1 1 <A non-zero-sum game is represented by two payoff profiles, as in the SYNOPSIS and the prisoner's dilemma.
A prisoner's dilemma, where Blue is the row player, Red is the column player, and T > R > P > S is:
\ Red | |
\ | Cooperate | Defect
Blue \ | |
--------------------------------
| \ R2 | \ T2
Cooperate | \ | \
| R1 \ | S1 \
--------------------------------
| \ S2 | \ P2
Defect | \ | \
| T1 \ | P1 \And in this implementation that would be:
$g = Game::Theory::TwoPersonMatrix->new(
%strategies,
payoff1 => [[ -1, -3 ], [ 0, -2 ]], # Blue: [[ R1, S1 ], [ T1, P1 ]]
payoff2 => [[ -1, 0 ], [ -3, -2 ]], # Red: [[ R2, T2 ], [ S2, P2 ]]
);The two player strategies are to either cooperate (1) or defect (2). This is given by a hash for each of the two players, where the values are Boolean 1 or 0:
%strategies = (
1 => { 1 => $cooporate1, 2 => $defect1 }, # Blue
2 => { 1 => $cooporate2, 2 => $defect2 }, # Red
);See the eg/tournament program in this distribution for examples that exercise strategic variations of the prisoner's dilemma.
METHODS
new
$g = Game::Theory::TwoPersonMatrix->new(
1 => { 1 => 0.5, 2 => 0.5 },
2 => { 1 => 0.5, 2 => 0.5 },
payoff => [ [1,0],
[0,1] ]
);
$g = Game::Theory::TwoPersonMatrix->new(
payoff1 => [ [2,3],
[2,1] ],
payoff2 => [ [3,5],
[2,3] ],
);Create a new Game::Theory::TwoPersonMatrix object.
Player strategies are given by a hash reference of numbered keys - one for each strategy. The values of these are assumed to add to 1. Otherwise YMMV.
Payoffs are given by array references of lists of outcomes. For zero-sum games this is a single payoff list. For non-zero-sum games this is given as two lists - one for each player.
The number of row and column payoff values must equal the number of the player and opponent strategies, respectively.
expected_payoff
$x = $g->expected_payoff;Return the expected payoff of a game. This is the sum of the strategic probabilities of each payoff.
s_expected_payoff
$g = Game::Theory::TwoPersonMatrix->new(
1 => { 1 => '(1 - p)', 2 => 'p' },
2 => { 1 => 1, 2 => 0 },
payoff => [ ['a','b'], ['c','d'] ]
);
$x = $g->s_expected_payoff;
# (1 - p) * 1 * a + (1 - p) * 0 * b + p * 1 * c + p * 0 * dReturn the symbolic expected payoff expression for a non-numeric game.
Using real payoff values, we solve the resulting expression for p in the eg/ examples.
counter_strategy
$x = $g->counter_strategy($player);Return the expected payoff for a given player, of either a zero-sum or non-zero-sum game, given pure strategies.
saddlepoint
$x = $g->saddlepoint;Return the saddlepoint of a zero-sum game, or undef if there is none.
A saddlepoint is simultaneously minimum for its row and maximum for its column.
oddments
$x = $g->oddments;Return each player's "oddments" for a 2x2 zero-sum game with no saddlepoint.
row_reduce
$g->row_reduce;Reduce a zero-sum game by identifying and eliminating strictly dominated rows and their associated player strategies.
col_reduce
$g->col_reduce;Reduce a zero-sum game by identifying and eliminating strictly dominated columns and their associated opponent strategies.
mm_tally
$x = $g->mm_tally;For zero-sum games, return the maximum of row minimums and the minimum of column maximums. For non-zero-sum games, return the maximum of row and column minimums.
pareto_optimal
$x = $g->pareto_optimal;Return the Pareto optimal outcomes for a non-zero-sum game.
nash
$x = $g->nash;Identify the Nash equilibria.
Given payoff pair (a,b), a is maximum for its column and b is maximum for its row.
play
$x = $g->play;
$x = $g->play(%strategies);Return a single outcome for a zero-sum game, or a pair for a non-zero-sum game as a hashref keyed by each strategy chosen and with values of the payoff(s) earned.
An optional list of player strategies can be provided. This is a hash of the same type of strategies that are given to the constructor.
SEE ALSO
The eg/ and t/ scripts in this distribution.
"A Gentle Introduction to Game Theory" from which this API is derived:
http://www.amazon.com/Gentle-Introduction-Theory-Mathematical-World/dp/0821813390
http://books.google.com/books?id=8doVBAAAQBAJ
https://en.wikipedia.org/wiki/Prisoner%27s_dilemma
AUTHOR
Gene Boggs <gene@cpan.org>
COPYRIGHT AND LICENSE
This software is copyright (c) 2022 by Gene Boggs.
This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.