math 464: linear optimization and game

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Math 464: Linear Optimization and Game Haijun Li Department of Mathematics Washington State University Spring 2013

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Page 1: Math 464: Linear Optimization and Game

Math 464: Linear Optimization and Game

Haijun Li

Department of MathematicsWashington State University

Spring 2013

Page 2: Math 464: Linear Optimization and Game

Assumptions in Non-CooperativeGames

Prisoners’ Dilemma, Again

1 Cooperation does not occur in prisoners’ dilemma,because players cannot make binding agreements.

2 But what if binding agreements are possible?3 This is exactly the class of scenarios studied in cooperative

game theory.

Page 3: Math 464: Linear Optimization and Game

Cooperative Games

• Players form coalitions with some binding agreements.• Each coalition chooses its action (collective strategy), and

players can benefit by cooperating within each coalition.• Non-transferable utility (NTU) games: the choice of

coalitional actions (by all coalitions) determines eachplayer’s payoff.

• Transferable utility (TU) games: the choice of coalitionalactions (by all coalitions) determines the payoff of eachcoalition. The members of the coalition then need to dividethis joint payoff.

Page 4: Math 464: Linear Optimization and Game

NTU Games: Joint PublicationExample

• n researchers working at n different universities can formgroups to write papers on probability theory.

• Each group of researchers can work together; thecomposition of a group determines the quality of the paperthey produce.

• Each author receives a payoff from his own university(promotion, bonus, teaching load reduction, etc).

• Payoffs are non-transferable.

Page 5: Math 464: Linear Optimization and Game

TU Games: Ice-Cream Example

Three types of ice-cream tubs are for sale:1 Type 1 (500g) costs $7.2 Type 2 (750g) costs $9.3 Type 3 (1000g) costs $11.

• n children, each has some amount of money (the i-th childhas bi dollars).

• Children have utility for ice-cream, and do not care aboutmoney.

• The payoff of each group: the maximum quantity ofice-cream the members of the group can buy by poolingtheir money.

• The ice-cream can be shared arbitrarily within the group.

Page 6: Math 464: Linear Optimization and Game

How Is a Cooperative Game Played?

• Even though players work together they are still selfish.• The partition into coalitions and payoff distribution should

be such that no player (or group of players) has anincentive to deviate.

• We may also want to ensure that the outcome is fair: thepayoff of each player is proportional to his contribution.

• We will now see how to formalize these ideas.

Page 7: Math 464: Linear Optimization and Game

Cooperative TU Games

• N = {1, . . . , n} is a finite set of n ≥ 2 players.• A subset S ⊆ N (or S ∈ 2N) of players is called a coalition.• N itself is called the grand coalition.• ν(·) : 2N → R is a set function called the characteristic

function.• For each subset of players S, ν(S) is the collective payoff

that the members in S can earn by working together.

DefinitionA cooperative TU game is a pair (N, ν).

Page 8: Math 464: Linear Optimization and Game

Land Development Game

1 Player 1 owns a piece of land with value of $10,000.2 Player 2 is interested in buying and can develop the land

increase its value to $20,000.3 Player 3 is interested in buying and can develop the land

increase its value to $30,000.

Find the characteristic function ν(·) of the game.

• Any coalition that does not contain player 1 has a worth of$0.

• ν(S) = 0 if 1 /∈ S, and

ν({1}) = 10, 000; ν({1, 2}) = 20, 000;

ν({1, 3}) = 30, 000; ν({1, 2, 3}) = 30, 000.

Page 9: Math 464: Linear Optimization and Game

Ice-Cream Example

Three types of ice-cream tubs are for sale:1 Type 1 (500g) costs $7.2 Type 2 (750g) costs $9.3 Type 3 (1000g) costs $11.

Three children 1, 2, 3 have $4, $3, $3 respectively. Find thecharacteristic function ν(·) of the game.

• ν(∅) = ν({1}) = ν({2}) = ν({3}) = 0, and ν({2, 3}) = 0• ν({1, 2}) = ν({1, 3}) = 500, and

ν({1, 2, 3}) = 750

Page 10: Math 464: Linear Optimization and Game

Garbage Game

1 Each of 4 property owners has one bag of garbage andmust dump it on somebody’s property.

2 Owners can form at most two coalitions.3 If b bags of garbage are dumped on a coalition of property

owners, then the coalition receives a reward of 4− b.

Find the characteristic function ν(·) of the game.

• The best strategy for a coalition S is to dump all of theirgarbage on the property of owners who are not in S.

• ν(∅) = 0, and ν({1, 2, 3, 4}) = 0, and

ν(S) = 4− |S| for 1 ≤ |S| ≤ 3,

where |S| denotes the number of players in S.

Page 11: Math 464: Linear Optimization and Game

Monotone Games

DefinitionA game (N, ν) is said to be monotone if

1 ν(·) is normalized; that is, ν(∅) = 0.2 ν(S1) ≤ ν(S2) for all S1 ⊆ S2 ⊆ N. That is, larger coalitions

gain more.

Remark

• ν(S) ≥ 0 for all S ⊆ N.• The land development and ice-cream games are

monotone.• The garbage game is not monotone.

Page 12: Math 464: Linear Optimization and Game

Superadditive Games

DefinitionA game (N, ν) is said to be superadditive if it is monotone and

ν(S1 ∪ S2) ≥ ν(S1) + ν(S2)

for any two disjoint coalitions S1 and S2.

Remark

• A large coalition as a whole is greater than the sum of itsparts.

• The land development and ice-cream games aresuperadditive.

• The garbage game is not superadditive.

Page 13: Math 464: Linear Optimization and Game

Any Measure is a GameThe characteristic function ν(·) of a game is known as anon-additive measure (Gustave Choquet, 1953/54).

DefinitionA set function ν(·) : 2N → R is called a measure on N if• (non-negativity) ν(S) ≥ 0 for all S ⊆ N.• (additivity) ν(S1 ∪ S2) = ν(S1) + ν(S2) for any two disjoint

subsets S1 and S2.

RemarkIf ν(·) is a measure on N, then• (monotonicity) ν(S1) ≤ ν(S2) for all S1 ⊆ S2 ⊆ N;• ν(∅) = 0;• ν(S1 ∪ S2) + ν(S1 ∩ S2) = ν(S1) + ν(S2) for any two subsets

S1 and S2.

Page 14: Math 464: Linear Optimization and Game

Any Probability Measure is a Game

DefinitionA set function ν(·) : 2N → R is called a probability measure onN if ν(·) is a measure with ν(N) = 1.

ExampleLet (x1, . . . , xn) denote a probability distribution on N; that is,

x1 + · · ·+ xn = 1, 0 ≤ xi ≤ 1, ∀i ∈ N.

Define P(S) :=∑

i∈S xi for any S ⊆ N.• P(·) is a probability measure on N.• Probability space (N,P) is an additive game.

Page 15: Math 464: Linear Optimization and Game

Transferable Utility Games: Outcome

• Let C = (C1, . . . ,Ck) be a coalition structure (a partition);that is

∪ki=1Ci = N, Ci ∩ Cj = ∅, ∀ i 6= j.

• Let x = (x1, . . . , xn) be a reward (payoff) vector with

xi ≥ 0, ∀ i ∈ N

DefinitionAn outcome of a TU game (N, ν) is a pair (C, x) with∑

i∈Cj

xi = ν(Cj), ∀ 1 ≤ j ≤ k.

That is, x distributes the value of each coalition in C.

Page 16: Math 464: Linear Optimization and Game

Example

• N = {1, 2, 3, 4, 5} with coalitions {1, 2, 3} and {4, 5}.• ν({1, 2, 3}) = 9 and ν({4, 5}) = 4.• (({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is an outcome.• (({1, 2, 3}, {4, 5}), (3, 1, 5, 2, 2)) is an outcome.• (({1, 2, 3}, {4, 5}), (3, 1, 3, 5, 2)) is not an outcome.• Reward transfers between coalitions are not allowed.

Page 17: Math 464: Linear Optimization and Game

Rationality

Let x = (x1, x2, . . . , xn) be a reward vector such that player ireceives a reward xi, i = 1, . . . , n, in a cooperative environment.

ImputationA reward vector x = (x1, x2, . . . , xn) is called an imputation ofgame ν(·) if it satisfies

1 Group Rationality: ν(N) =∑n

i=1 xi;2 Individual Rationality: ν({xi}) ≤ xi, i = 1, 2, . . . , n.

Land Development GameConsider N = {1, 2, 3}. Then x = (20, 000, 5, 000, 5, 000) is animputation, but y = (5, 000, 10, 000, 15, 000) is not.

Page 18: Math 464: Linear Optimization and Game

Comparison of Imputations

Let x = (x1, x2, . . . , xn) and y = (y1, y2, . . . , yn) be twoimputations of a game ν(·).

DominationLet S ⊆ N be a coalition. The imputation y is said to dominate xthrough the coalition S, written as y >S x, if

1 yi ≥ xi for all i ∈ S (that is, each member of S prefers y to x);2

∑i∈S yi ≤ ν(S) (that is, the members of S can attain the

rewards given by y).

Land Development GameConsider N = {1, 2, 3} and S = {1, 3}. Letx = (19, 000, 1, 000, 10, 000), and y = (19, 800, 100, 10, 100) betwo imputations. Then y >S x.

Page 19: Math 464: Linear Optimization and Game

What Is a Good Outcome?

Ice-Cream GameThree children 1, 2, 3 have $4, $3, $3 respectively.

1 Type 1 (500g) costs $7; Type 2 (750g) costs $9; Type 3(1000g) costs $11.

2 ν(∅) = ν({1}) = ν({2}) = ν({3}) = ν({2, 3}) = 0.3 ν({1, 2}) = ν({1, 3}) = 500, and ν({1, 2, 3}) = 750.

Analysis

• Three children can buy Type 2 and share as (200, 200, 350).• But 1 and 2 can get more ice-cream by buying a 500g tub

on their own, and splitting it equally.• (200, 200, 350) is not stable; e.g.,(250, 250, 250) >{1,2} (200, 200, 350).

Page 20: Math 464: Linear Optimization and Game

The Core: A Stable Set

Definition (Gillies, 1953)The core of an n-person game (N, ν) is the set of allundominated imputations.

Theorem

• I = {x ∈ Rn :∑n

i=1 xi = ν(N), xi ≥ ν({xi}), ∀i = 1, . . . , n}• Core(N, ν) = {x ∈ I :

∑i∈S xi ≥ ν(S), ∀ S ⊂ N}.

Remark

• The core is an intersection of halfspaces, and hence aconvex set.

• The core may be empty.

Page 21: Math 464: Linear Optimization and Game

Example: Ice-Cream

Ice-Cream GameThree children 1, 2, 3 have $4, $3, $3 respectively.

1 Type 1 (500g) costs $7; Type 2 (750g) costs $9; Type 3(1000g) costs $11.

2 ν(∅) = ν({1}) = ν({2}) = ν({3}) = ν({2, 3}) = 0.3 ν({1, 2}) = ν({1, 3}) = 500, and ν({1, 2, 3}) = 750.

• (200, 200, 350) is not in the core.• (250, 250, 250) is in the core because no subgroup of

players can deviate so that each member of the subgroupgets more.

• (750, 0, 0) is in the core because 2 and 3 cannot get moreon their own.

Page 22: Math 464: Linear Optimization and Game

Example: Land Development

• N = {1, 2, 3}.• ν(S) = 0 if 1 /∈ S, and

ν({1}) = 10, 000; ν({1, 2}) = 20, 000;

ν({1, 3}) = 30, 000; ν({1, 2, 3}) = 30, 000.

Imputations and Core

• The set of all imputations is given by

I = {(x1, x2, x3) : x1+x2+x3 = 30, 000, x1 ≥ 10, 000, x2 ≥ 0, x3 ≥ 0}.

• Core(N, ν) = {x : x = (x1, 0, 30, 000− x1), x1 ≥ 10, 000}.

Page 23: Math 464: Linear Optimization and Game

Empty Core

Consider the game:• N = {1, 2, 3}.• ν(S) = 0 if |S| = 1, and

ν({1, 2}) = ν({1, 3}) = ν({2, 3}) = 1, ν({1, 2, 3}) = 1.

Imputations and Core

• The set of all imputations is given by

I = {(x1, x2, x3) : x1 + x2 + x3 = 1, x1 ≥ 0, x2 ≥ 0, x3 ≥ 0}.

• Core(N, ν) = ∅.

Page 24: Math 464: Linear Optimization and Game

ε-Core and Least Core

• The core is a very attractive solution concept.• However, some games have empty cores.• People have developed the concepts of ε-core and least

core to describe approximately stable outcomes.• Another issue about cores is lack of fairness.