MatrixGamePayoff
✖
MatrixGamePayoff
gives the expected payoff for each player in the matrix game mgame with strategy profile {s1,…,sn}.
gives the expected payoff for each named player in the matrix game mgame with strategy profile {s1,…,sn} using an association.
Details

- MatrixGamePayoff is also known as expected payoff or expected utility.
- MatrixGamePayoff is typically used to evaluate expected payoffs for players given strategies for each of the players.
- The strategy profile
is a list of strategies for the
players. Strategy
for player
is the vector of probabilities of taking each different action
.
- The expected payoff for player
is given by:
or
where:
-
probability for player to take action
payoff for player when player
takes action
strategy for player payoff array for player - The payoff properties "prop" include:
-
"Expectation" the average payoff for each player "MarginalDistributions" the distribution of payoffs for each player "MultivariateDistribution" the multivariate distribution of payoffs for all players "Simulation" a randomly generated list of payoffs for a round of the game {"Simulation", n} a list of payoffs for rounds of the game
"Variance" the variance of payoff for each player
Examples
open allclose allBasic Examples (4)Summary of the most common use cases
Generate a 2-player matrix game:

https://wolfram.com/xid/0ixbikw9ixiknab-mhjkqv

Find the expected payoffs for a given strategy:

https://wolfram.com/xid/0ixbikw9ixiknab-bldck2

Generate the 3-coordination game:

https://wolfram.com/xid/0ixbikw9ixiknab-fl8sn7

Find the expected payoff when the first action is preferred by all players:

https://wolfram.com/xid/0ixbikw9ixiknab-etp47

Find the expected payoffs for a Prisoner's Dilemma game where both prisoners prefer cooperating:

https://wolfram.com/xid/0ixbikw9ixiknab-bihmun

Generate the 3-coordination game:

https://wolfram.com/xid/0ixbikw9ixiknab-k8bn7f

Find the expected payoff when the first two players collaborate:

https://wolfram.com/xid/0ixbikw9ixiknab-f68smg

Scope (5)Survey of the scope of standard use cases
Generate a 2-player matrix game:

https://wolfram.com/xid/0ixbikw9ixiknab-btmwhz

Find the expected payoffs for a given strategy:

https://wolfram.com/xid/0ixbikw9ixiknab-2dwcp

Consider a Traveler's Dilemma game for a given strategy:

https://wolfram.com/xid/0ixbikw9ixiknab-bfjqre

https://wolfram.com/xid/0ixbikw9ixiknab-l8g3ny

Find the variances of the payoffs:

https://wolfram.com/xid/0ixbikw9ixiknab-c2q4bd

Find the marginal distributions:

https://wolfram.com/xid/0ixbikw9ixiknab-g92b

Find the multivariate distribution:

https://wolfram.com/xid/0ixbikw9ixiknab-of5jfy

Generate a Guess Two-Thirds Average game:

https://wolfram.com/xid/0ixbikw9ixiknab-e0iyb9

Find the expected payoff when the third player is most likely to bet the highest:

https://wolfram.com/xid/0ixbikw9ixiknab-di6cxu

Find the expected payoff of the 3-coordination game when the first action is preferred:

https://wolfram.com/xid/0ixbikw9ixiknab-z9uxw


https://wolfram.com/xid/0ixbikw9ixiknab-b29fhn

Find the expected payoff when the strategies of two groups of two players are correlated:

https://wolfram.com/xid/0ixbikw9ixiknab-jatmng

Find the variances of these correlated strategies:

https://wolfram.com/xid/0ixbikw9ixiknab-j3gaa6

Find the distributions of these correlated strategies:

https://wolfram.com/xid/0ixbikw9ixiknab-e1gjfm

Find the multivariate distribution of these correlated strategies:

https://wolfram.com/xid/0ixbikw9ixiknab-eccs36

Simulate 10 rounds repeatedly using these correlated strategies:

https://wolfram.com/xid/0ixbikw9ixiknab-da8gfl

Applications (7)Sample problems that can be solved with this function
Social Games (2)
The Volunteer's Dilemma describes a situation where each player can either volunteer or defect. If at least one player volunteers, all other players marginally benefit from defecting. If no player volunteers, all players have a very low payoff. Generate a Volunteer's Dilemma game with a volunteer and a detractor:

https://wolfram.com/xid/0ixbikw9ixiknab-mvvxle

Find the expected payoffs with a likely volunteer and an unlikely volunteer:

https://wolfram.com/xid/0ixbikw9ixiknab-g2pkzw

Find the expected payoffs with likely volunteers:

https://wolfram.com/xid/0ixbikw9ixiknab-lbzr3b

Find the expected payoffs with unlikely volunteers:

https://wolfram.com/xid/0ixbikw9ixiknab-lhxjpw

The Discoordination game is a hybrid form of coordination and anti-coordination games, where one player's incentive is to coordinate, while the other player tries to avoid this. Generate a Volunteer's Dilemma game with a volunteer and a detractor:

https://wolfram.com/xid/0ixbikw9ixiknab-cj3syo

Find the expected payoffs with cooperative players:

https://wolfram.com/xid/0ixbikw9ixiknab-i3f890

Find the expected payoffs with uncooperative players:

https://wolfram.com/xid/0ixbikw9ixiknab-jf1l22

Find the expected payoffs with a cooperative player and a uncooperative player:

https://wolfram.com/xid/0ixbikw9ixiknab-mb9m1h

Economics Games (2)
The Cournot Oligopoly game describes a situation where a group of firms produces the same good. Each firm must consider the production cost and the quantity that the other firms are producing. Only the firms with the lowest price sell goods. Generate a Cournot Oligopoly game:

https://wolfram.com/xid/0ixbikw9ixiknab-ijl349

Find the expected payoffs when the first two players collude:

https://wolfram.com/xid/0ixbikw9ixiknab-bplnb1

A price war refers a game where multiple firms have an interest in offering the lowest price, but the payoff of any firm is directly correlated to the price chosen. Consider a price war between 3 firms where each firm has a choice between a low price and a high price:

https://wolfram.com/xid/0ixbikw9ixiknab-fgyi0r


https://wolfram.com/xid/0ixbikw9ixiknab-g5fiva

Clearly, cooperating players have an interest in choosing high prices. Show this by comparing the payoffs of multiple strategies:

https://wolfram.com/xid/0ixbikw9ixiknab-iww7o

Military Games (1)
The Colonel Blotto game describes a situation where officers (players) are tasked to simultaneously distribute limited resources over several objects (battlefields). The player devoting the most resources to a battlefield wins that battlefield, and the gain (or payoff) is equal to the total number of battlefields won. Generate a Colonel Blotto game:

https://wolfram.com/xid/0ixbikw9ixiknab-eed1e

Find the expected payoffs for a 50-50 strategy:

https://wolfram.com/xid/0ixbikw9ixiknab-czar2v

Traffic Games (1)
Amazingly, adding one or more roads to a road network can slow overall traffic, known as Braess's paradox. Given a road network from a to b, taking two different paths either via c or d, the roads ac and db take 20 n minutes where n is the number of drivers on the road, cb and ad take 45 minutes independent of the number of drivers. Represent the road network as a graph:

https://wolfram.com/xid/0ixbikw9ixiknab-km0lkx

For two drivers n=2, find all paths from a to b:

https://wolfram.com/xid/0ixbikw9ixiknab-dicqqv

Count the number of drivers for each choice of path, which gives four cases:

https://wolfram.com/xid/0ixbikw9ixiknab-x97ssi

Find the total time taken for each case:

https://wolfram.com/xid/0ixbikw9ixiknab-n4wfg3

Maximize the negative commute time:

https://wolfram.com/xid/0ixbikw9ixiknab-n54ewu

Create a traffic game based on the payoff matrix:

https://wolfram.com/xid/0ixbikw9ixiknab-w08jm1


https://wolfram.com/xid/0ixbikw9ixiknab-hayy4f

The two pure strategies correspond to the commuters picking different paths:

https://wolfram.com/xid/0ixbikw9ixiknab-yvns99

These pure strategies also give the shortest commute time of 65 minutes:

https://wolfram.com/xid/0ixbikw9ixiknab-j58dzd

Following the traffic game above, you can automate the modeling and solving for a general road network. Here start and end are the start and end vertices, and each road segment capacity is encoded using edge weight:

https://wolfram.com/xid/0ixbikw9ixiknab-n7iikj

https://wolfram.com/xid/0ixbikw9ixiknab-ih8u3i
Add a short high-capacity road segment between d and c:

https://wolfram.com/xid/0ixbikw9ixiknab-49zv7g

This situation defines a new traffic game for two drivers:

https://wolfram.com/xid/0ixbikw9ixiknab-id3nvr

https://wolfram.com/xid/0ixbikw9ixiknab-jhq0b1


https://wolfram.com/xid/0ixbikw9ixiknab-o1ljl9

The new possibility of switching between the two previously existing routes resulted in a Prisoner's Dilemma–like situation, where the only stable solution results in a longer commuting time (82 minutes) compared to the restricted network (65 minutes):

https://wolfram.com/xid/0ixbikw9ixiknab-n0i283

Converging Equilibria (1)
Consider the expected payoff for each player in a two-person game:

https://wolfram.com/xid/0ixbikw9ixiknab-chebk1

Find the expression for each player payoff:

https://wolfram.com/xid/0ixbikw9ixiknab-iahw0c

Assume that each player changes their expected payoff following the gradient:

https://wolfram.com/xid/0ixbikw9ixiknab-ij72ix

Plot the gradient as a stream plot:

https://wolfram.com/xid/0ixbikw9ixiknab-gp56rh


https://wolfram.com/xid/0ixbikw9ixiknab-nfo5zf

From this plot, you can see that only the pure strategies {{0,1},{1,0}} and {{1,0},{0,1}} are "stable" in the sense that the trajectories converge to them:

https://wolfram.com/xid/0ixbikw9ixiknab-i9af29

Neat Examples (1)Surprising or curious use cases
Consider three populations of equal size playing Rock Paper Scissors. Each population either always plays Rock, always Paper or always Scissors. For each game played, if player A loses, then player A adopts the strategy of the adversary. To mimic population growth, after each game, all populations are exponentially increased by 1.02. For a set number of games, the population with the highest percentage is declared the winner:

https://wolfram.com/xid/0ixbikw9ixiknab-fbjmem

https://wolfram.com/xid/0ixbikw9ixiknab-vs1fl

Wolfram Research (2025), MatrixGamePayoff, Wolfram Language function, https://reference.wolfram.com/language/ref/MatrixGamePayoff.html.
Text
Wolfram Research (2025), MatrixGamePayoff, Wolfram Language function, https://reference.wolfram.com/language/ref/MatrixGamePayoff.html.
Wolfram Research (2025), MatrixGamePayoff, Wolfram Language function, https://reference.wolfram.com/language/ref/MatrixGamePayoff.html.
CMS
Wolfram Language. 2025. "MatrixGamePayoff." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/MatrixGamePayoff.html.
Wolfram Language. 2025. "MatrixGamePayoff." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/MatrixGamePayoff.html.
APA
Wolfram Language. (2025). MatrixGamePayoff. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/MatrixGamePayoff.html
Wolfram Language. (2025). MatrixGamePayoff. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/MatrixGamePayoff.html
BibTeX
@misc{reference.wolfram_2025_matrixgamepayoff, author="Wolfram Research", title="{MatrixGamePayoff}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/MatrixGamePayoff.html}", note=[Accessed: 16-April-2025
]}
BibLaTeX
@online{reference.wolfram_2025_matrixgamepayoff, organization={Wolfram Research}, title={MatrixGamePayoff}, year={2025}, url={https://reference.wolfram.com/language/ref/MatrixGamePayoff.html}, note=[Accessed: 16-April-2025
]}