cooperative game theory: solution...

179
Definitions Stability notions Other solution concepts Subclasses Cooperative Game Theory: Solution concepts Maria Serna Fall 2017 AGT-MIRI Cooperative Game Theory

Upload: others

Post on 09-Jul-2020

7 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Cooperative Game Theory: Solution concepts

Maria Serna

Fall 2017

AGT-MIRI Cooperative Game Theory

Page 2: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

References

G. Chalkiadakis, E. Elkind, M. WooldridgeComputational Aspects of Cooperative Game TheoryMorgan & Claypool, 2012 Wikipedia.

G. OwenGame Theory3rd edition, Academic Press, 1995

AGT-MIRI Cooperative Game Theory

Page 3: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

1 Definitions

2 Stability notions

3 Other solution concepts

4 Subclasses

AGT-MIRI Cooperative Game Theory

Page 4: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C ,S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 5: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C ,S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 6: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C ,S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 7: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C ,S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 8: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).

individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C ,S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 9: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C ,S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 10: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C ,S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 11: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Non-Cooperative versus cooperative Games

Non-cooperative game theory model scenarios where playerscannot make binding agreements.

Cooperative game theory model scenarios, where

agents can benefit by cooperating, andbinding agreements are possible.

In cooperative games, actions are taken by groups of agents,coalitions, and payoffs are given to

the group. Those have to be divided among its members:Transferable utility games (TU).individuals. Non-transferable utility games (NTU).

For the moment we focus on TU games

Notation: N, set of players, C , S ,X ⊆ N are coalitions.

AGT-MIRI Cooperative Game Theory

Page 12: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Notation

For a set A:

CA denotes the subsets of A, i.e., C ⊆ A.PA denotes the partitions of A.

For a set of players N, a coalition is any subset of N.N is the grand coalition.

A partition of N is a splitting of all the players into disjointcoalitions.

AGT-MIRI Cooperative Game Theory

Page 13: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Notation

For a set A:

CA denotes the subsets of A, i.e., C ⊆ A.PA denotes the partitions of A.

For a set of players N, a coalition is any subset of N.N is the grand coalition.

A partition of N is a splitting of all the players into disjointcoalitions.

AGT-MIRI Cooperative Game Theory

Page 14: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Notation

For a set A:

CA denotes the subsets of A, i.e., C ⊆ A.PA denotes the partitions of A.

For a set of players N, a coalition is any subset of N.N is the grand coalition.

A partition of N is a splitting of all the players into disjointcoalitions.

AGT-MIRI Cooperative Game Theory

Page 15: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Notation

For a set A:

CA denotes the subsets of A, i.e., C ⊆ A.PA denotes the partitions of A.

For a set of players N, a coalition is any subset of N.

N is the grand coalition.

A partition of N is a splitting of all the players into disjointcoalitions.

AGT-MIRI Cooperative Game Theory

Page 16: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Notation

For a set A:

CA denotes the subsets of A, i.e., C ⊆ A.PA denotes the partitions of A.

For a set of players N, a coalition is any subset of N.N is the grand coalition.

A partition of N is a splitting of all the players into disjointcoalitions.

AGT-MIRI Cooperative Game Theory

Page 17: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Notation

For a set A:

CA denotes the subsets of A, i.e., C ⊆ A.PA denotes the partitions of A.

For a set of players N, a coalition is any subset of N.N is the grand coalition.

A partition of N is a splitting of all the players into disjointcoalitions.

AGT-MIRI Cooperative Game Theory

Page 18: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games

A characteristic function game is a pair (N, v), where:

N = {1, ..., n} is the set of players andv : CN → R is the characteristic function.

for each coalition of players C ⊆ N, v(C ) is the amount thatthe members of C can earn by working together

usually it is assumed that v is

normalized: v(∅) = 0,non-negative: v(C ) ≥ 0, for any C ⊆ N, andmonotone: v(C ) ≤ v(D), for any C , D such that C ⊆ D

Example: N = {A,B,C} and

CN ∅ A B C AB AC BC ABC

v 0 12 0 0 18 18 18 24

AGT-MIRI Cooperative Game Theory

Page 19: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games

A characteristic function game is a pair (N, v), where:

N = {1, ..., n} is the set of players andv : CN → R is the characteristic function.for each coalition of players C ⊆ N, v(C ) is the amount thatthe members of C can earn by working together

usually it is assumed that v is

normalized: v(∅) = 0,non-negative: v(C ) ≥ 0, for any C ⊆ N, andmonotone: v(C ) ≤ v(D), for any C , D such that C ⊆ D

Example: N = {A,B,C} and

CN ∅ A B C AB AC BC ABC

v 0 12 0 0 18 18 18 24

AGT-MIRI Cooperative Game Theory

Page 20: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games

A characteristic function game is a pair (N, v), where:

N = {1, ..., n} is the set of players andv : CN → R is the characteristic function.for each coalition of players C ⊆ N, v(C ) is the amount thatthe members of C can earn by working together

usually it is assumed that v is

normalized: v(∅) = 0,non-negative: v(C ) ≥ 0, for any C ⊆ N, andmonotone: v(C ) ≤ v(D), for any C , D such that C ⊆ D

Example: N = {A,B,C} and

CN ∅ A B C AB AC BC ABC

v 0 12 0 0 18 18 18 24

AGT-MIRI Cooperative Game Theory

Page 21: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games

A characteristic function game is a pair (N, v), where:

N = {1, ..., n} is the set of players andv : CN → R is the characteristic function.for each coalition of players C ⊆ N, v(C ) is the amount thatthe members of C can earn by working together

usually it is assumed that v is

normalized: v(∅) = 0,non-negative: v(C ) ≥ 0, for any C ⊆ N, andmonotone: v(C ) ≤ v(D), for any C , D such that C ⊆ D

Example: N = {A,B,C} and

CN ∅ A B C AB AC BC ABC

v 0 12 0 0 18 18 18 24

AGT-MIRI Cooperative Game Theory

Page 22: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Partition Function Games

A partition function game is a pair (N, v), where:

N = {1, ..., n} is the set of players andv : CN × PN\S → R is the partition function.

for each subset of players C ⊆ N and partition P, v(C ;P) isthe amount that the members of C can earn by workingtogether assuming N is splitted according to P.

Example: N = {A,B,C} and

PN ∅, ABC AB, C AC, B BC, A A,B,C

C ∅ ABC AB C AC B BC A A B C

v 0 24 18 0 18 0 18 0 12 6 0

AGT-MIRI Cooperative Game Theory

Page 23: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Partition Function Games

A partition function game is a pair (N, v), where:

N = {1, ..., n} is the set of players andv : CN × PN\S → R is the partition function.for each subset of players C ⊆ N and partition P, v(C ;P) isthe amount that the members of C can earn by workingtogether assuming N is splitted according to P.

Example: N = {A,B,C} and

PN ∅, ABC AB, C AC, B BC, A A,B,C

C ∅ ABC AB C AC B BC A A B C

v 0 24 18 0 18 0 18 0 12 6 0

AGT-MIRI Cooperative Game Theory

Page 24: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Partition Function Games

A partition function game is a pair (N, v), where:

N = {1, ..., n} is the set of players andv : CN × PN\S → R is the partition function.for each subset of players C ⊆ N and partition P, v(C ;P) isthe amount that the members of C can earn by workingtogether assuming N is splitted according to P.

Example: N = {A,B,C} and

PN ∅, ABC AB, C AC, B BC, A A,B,C

C ∅ ABC AB C AC B BC A A B C

v 0 24 18 0 18 0 18 0 12 6 0

AGT-MIRI Cooperative Game Theory

Page 25: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games vs.Partition Function Games

In partition function games (PFG) the payoff obtained by acoalition depends on the other coalitions

In characteristic function games (CFG) the payoff of eachcoalition only depends on the action of that coalitionin such games, each coalition can be identified with the profitit obtains by choosing its best action

We restrict in this course to focus on characteristic functiongames, and use the term coalition game to refer to such agame.

AGT-MIRI Cooperative Game Theory

Page 26: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games vs.Partition Function Games

In partition function games (PFG) the payoff obtained by acoalition depends on the other coalitions

In characteristic function games (CFG) the payoff of eachcoalition only depends on the action of that coalitionin such games, each coalition can be identified with the profitit obtains by choosing its best action

We restrict in this course to focus on characteristic functiongames, and use the term coalition game to refer to such agame.

AGT-MIRI Cooperative Game Theory

Page 27: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games vs.Partition Function Games

In partition function games (PFG) the payoff obtained by acoalition depends on the other coalitions

In characteristic function games (CFG) the payoff of eachcoalition only depends on the action of that coalitionin such games, each coalition can be identified with the profitit obtains by choosing its best action

We restrict in this course to focus on characteristic functiongames, and use the term coalition game to refer to such agame.

AGT-MIRI Cooperative Game Theory

Page 28: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Characteristic Function Games vs.Partition Function Games

In partition function games (PFG) the payoff obtained by acoalition depends on the other coalitions

In characteristic function games (CFG) the payoff of eachcoalition only depends on the action of that coalitionin such games, each coalition can be identified with the profitit obtains by choosing its best action

We restrict in this course to focus on characteristic functiongames, and use the term coalition game to refer to such agame.

AGT-MIRI Cooperative Game Theory

Page 29: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 30: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 31: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 32: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 33: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 34: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 35: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 36: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Buying Ice-Cream Game

We have a group of n children, each has some amount ofmoney the i-th child has bi dollars.

There are three types of ice-cream tubs for sale:

Type 1 costs $7, contains 500gType 2 costs $9, contains 750gType 3 costs $11, contains 1kg

The children have utility for ice-cream but do not care aboutmoney.

The payoff of each group is the maximum quantity ofice-cream the members of the group can buy by pooling alltheir money.

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

AGT-MIRI Cooperative Game Theory

Page 37: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Ice-Cream Game: Characteristic Function

Charlie: $6 Marcie: $4 Pattie: $3

w = 500 w = 750 w = 100

p = $7 p = $9 p = $11

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 750, v({C ,P}) = 750, v({M,P}) = 500

v({C ,M,P}) = 1000

AGT-MIRI Cooperative Game Theory

Page 38: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Ice-Cream Game: Characteristic Function

Charlie: $6 Marcie: $4 Pattie: $3

w = 500 w = 750 w = 100

p = $7 p = $9 p = $11

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 750, v({C ,P}) = 750, v({M,P}) = 500

v({C ,M,P}) = 1000

AGT-MIRI Cooperative Game Theory

Page 39: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Ice-Cream Game: Characteristic Function

Charlie: $6 Marcie: $4 Pattie: $3

w = 500 w = 750 w = 100

p = $7 p = $9 p = $11

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 750, v({C ,P}) = 750, v({M,P}) = 500

v({C ,M,P}) = 1000

AGT-MIRI Cooperative Game Theory

Page 40: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Ice-Cream Game: Characteristic Function

Charlie: $6 Marcie: $4 Pattie: $3

w = 500 w = 750 w = 100

p = $7 p = $9 p = $11

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 750, v({C ,P}) = 750, v({M,P}) = 500

v({C ,M,P}) = 1000

AGT-MIRI Cooperative Game Theory

Page 41: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Outcomes

An outcome of a game Γ = (N, v) is a pair (P, x), where:

P = (C1, ...,Ck) ∈ PN is a coalition structure

x = (x1, ..., xn) is a payoff vector, which distributes the valueof each coalition in P:

xi ≥ 0, for all i ∈ N∑i∈C xi = v(C ), for each C ∈ P, feasibility

AGT-MIRI Cooperative Game Theory

Page 42: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Outcomes

An outcome of a game Γ = (N, v) is a pair (P, x), where:

P = (C1, ...,Ck) ∈ PN is a coalition structure

x = (x1, ..., xn) is a payoff vector, which distributes the valueof each coalition in P:

xi ≥ 0, for all i ∈ N∑i∈C xi = v(C ), for each C ∈ P, feasibility

AGT-MIRI Cooperative Game Theory

Page 43: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Outcomes

An outcome of a game Γ = (N, v) is a pair (P, x), where:

P = (C1, ...,Ck) ∈ PN is a coalition structure

x = (x1, ..., xn) is a payoff vector, which distributes the valueof each coalition in P:

xi ≥ 0, for all i ∈ N∑i∈C xi = v(C ), for each C ∈ P,

feasibility

AGT-MIRI Cooperative Game Theory

Page 44: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Outcomes

An outcome of a game Γ = (N, v) is a pair (P, x), where:

P = (C1, ...,Ck) ∈ PN is a coalition structure

x = (x1, ..., xn) is a payoff vector, which distributes the valueof each coalition in P:

xi ≥ 0, for all i ∈ N∑i∈C xi = v(C ), for each C ∈ P, feasibility

AGT-MIRI Cooperative Game Theory

Page 45: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Outcome:example

Suppose v({1, 2, 3}) = 9 and v({4, 5}) = 4

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is an outcome

(({1, 2, 3}, {4, 5}), (2, 3, 2, 3, 3)) is NOT an outcome astransfers between coalitions are not allowed

AGT-MIRI Cooperative Game Theory

Page 46: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Outcome:example

Suppose v({1, 2, 3}) = 9 and v({4, 5}) = 4

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is an outcome

(({1, 2, 3}, {4, 5}), (2, 3, 2, 3, 3)) is NOT an outcome astransfers between coalitions are not allowed

AGT-MIRI Cooperative Game Theory

Page 47: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Outcome:example

Suppose v({1, 2, 3}) = 9 and v({4, 5}) = 4

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is an outcome

(({1, 2, 3}, {4, 5}), (2, 3, 2, 3, 3)) is NOT an outcome astransfers between coalitions are not allowed

AGT-MIRI Cooperative Game Theory

Page 48: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Imputations

An outcome (P, x) is called an imputation if it satisfies individualrationality:

xi ≥ v({i}),

for all i ∈ N.

Notation: we denote∑

i∈A xi by x(A)

AGT-MIRI Cooperative Game Theory

Page 49: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Imputations

An outcome (P, x) is called an imputation if it satisfies individualrationality:

xi ≥ v({i}),

for all i ∈ N.

Notation: we denote∑

i∈A xi by x(A)

AGT-MIRI Cooperative Game Theory

Page 50: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

TU gamesOutcomesImputations

Imputations

An outcome (P, x) is called an imputation if it satisfies individualrationality:

xi ≥ v({i}),

for all i ∈ N.

Notation: we denote∑

i∈A xi by x(A)

AGT-MIRI Cooperative Game Theory

Page 51: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

1 Definitions

2 Stability notions

3 Other solution concepts

4 Subclasses

AGT-MIRI Cooperative Game Theory

Page 52: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

The solutions of a game should provide good outcomes.

Let us present some stability notions related to outcomes orimputations.

To simplify the presentation we consider superadditive games.

AGT-MIRI Cooperative Game Theory

Page 53: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

The solutions of a game should provide good outcomes.

Let us present some stability notions related to outcomes orimputations.

To simplify the presentation we consider superadditive games.

AGT-MIRI Cooperative Game Theory

Page 54: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

The solutions of a game should provide good outcomes.

Let us present some stability notions related to outcomes orimputations.

To simplify the presentation we consider superadditive games.

AGT-MIRI Cooperative Game Theory

Page 55: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

The solutions of a game should provide good outcomes.

Let us present some stability notions related to outcomes orimputations.

To simplify the presentation we consider superadditive games.

AGT-MIRI Cooperative Game Theory

Page 56: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games

A game G = (N, v) is called superadditive if

v(C ∪ D) ≥ v(C ) + v(D),

for any two disjoint coalitions C and D

Example: v(C ) = |C |2

v(C ∪ D) = (|C |+ |D|)2 ≥ |C |2 + |D|2 = v(C ) + v(D)

AGT-MIRI Cooperative Game Theory

Page 57: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games

A game G = (N, v) is called superadditive if

v(C ∪ D) ≥ v(C ) + v(D),

for any two disjoint coalitions C and D

Example: v(C ) = |C |2

v(C ∪ D) = (|C |+ |D|)2 ≥ |C |2 + |D|2 = v(C ) + v(D)

AGT-MIRI Cooperative Game Theory

Page 58: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games

In superadditive games, two coalitions can always mergewithout losing money; hence, we can assume in a stableoutcome P = (N, ∅).Players must form the grand coalition

In superadditive games, we identify outcomes with payoffvectors for the grand coalition

i.e., an outcome is a vector x = (x1, ..., xn) with x(N) = v(N)

AGT-MIRI Cooperative Game Theory

Page 59: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games

In superadditive games, two coalitions can always mergewithout losing money; hence, we can assume in a stableoutcome P = (N, ∅).

Players must form the grand coalition

In superadditive games, we identify outcomes with payoffvectors for the grand coalition

i.e., an outcome is a vector x = (x1, ..., xn) with x(N) = v(N)

AGT-MIRI Cooperative Game Theory

Page 60: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games

In superadditive games, two coalitions can always mergewithout losing money; hence, we can assume in a stableoutcome P = (N, ∅).Players must form the grand coalition

In superadditive games, we identify outcomes with payoffvectors for the grand coalition

i.e., an outcome is a vector x = (x1, ..., xn) with x(N) = v(N)

AGT-MIRI Cooperative Game Theory

Page 61: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games

In superadditive games, two coalitions can always mergewithout losing money; hence, we can assume in a stableoutcome P = (N, ∅).Players must form the grand coalition

In superadditive games, we identify outcomes with payoffvectors for the grand coalition

i.e., an outcome is a vector x = (x1, ..., xn) with x(N) = v(N)

AGT-MIRI Cooperative Game Theory

Page 62: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games

In superadditive games, two coalitions can always mergewithout losing money; hence, we can assume in a stableoutcome P = (N, ∅).Players must form the grand coalition

In superadditive games, we identify outcomes with payoffvectors for the grand coalition

i.e., an outcome is a vector x = (x1, ..., xn) with x(N) = v(N)

AGT-MIRI Cooperative Game Theory

Page 63: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

This is a superadditive game, so outcomes are payoff vectors!How should the players share the ice-cream?(200, 200, 350)?Charlie and Marcie can get more ice-cream by buying a 500g tubon their own, and splitting it equally(200, 200, 350) is not stable!

AGT-MIRI Cooperative Game Theory

Page 64: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

This is a superadditive game, so outcomes are payoff vectors!How should the players share the ice-cream?(200, 200, 350)?Charlie and Marcie can get more ice-cream by buying a 500g tubon their own, and splitting it equally(200, 200, 350) is not stable!

AGT-MIRI Cooperative Game Theory

Page 65: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

This is a superadditive game, so outcomes are payoff vectors!

How should the players share the ice-cream?(200, 200, 350)?Charlie and Marcie can get more ice-cream by buying a 500g tubon their own, and splitting it equally(200, 200, 350) is not stable!

AGT-MIRI Cooperative Game Theory

Page 66: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

This is a superadditive game, so outcomes are payoff vectors!How should the players share the ice-cream?

(200, 200, 350)?Charlie and Marcie can get more ice-cream by buying a 500g tubon their own, and splitting it equally(200, 200, 350) is not stable!

AGT-MIRI Cooperative Game Theory

Page 67: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

This is a superadditive game, so outcomes are payoff vectors!How should the players share the ice-cream?(200, 200, 350)?

Charlie and Marcie can get more ice-cream by buying a 500g tubon their own, and splitting it equally(200, 200, 350) is not stable!

AGT-MIRI Cooperative Game Theory

Page 68: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

This is a superadditive game, so outcomes are payoff vectors!How should the players share the ice-cream?(200, 200, 350)?Charlie and Marcie can get more ice-cream by buying a 500g tubon their own, and splitting it equally

(200, 200, 350) is not stable!

AGT-MIRI Cooperative Game Theory

Page 69: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

What Is a Good Outcome?

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

This is a superadditive game, so outcomes are payoff vectors!How should the players share the ice-cream?(200, 200, 350)?Charlie and Marcie can get more ice-cream by buying a 500g tubon their own, and splitting it equally(200, 200, 350) is not stable!

AGT-MIRI Cooperative Game Theory

Page 70: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 71: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 72: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 73: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 74: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 75: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 76: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 77: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

The core

The core of a game Γ is the set of all stable outcomes, i.e.,outcomes that no coalition wants to deviate from

core(Γ) = {(P, x)|x(C ) ≥ v(C ) for any C ⊆ N}

each coalition earns, according to x , at least as much as it canmake on its own.

Example: v({1, 2, 3}) = 9, v({4, 5}) = 4, v({2, 4}) = 7

(({1, 2, 3}, {4, 5}), (3, 3, 3, 3, 1)) is NOT in the core

as x({2, 4}) = 6 and v({2, 4}) = 7

no subgroup of players can deviate so that each member ofthe subgroup gets more.

AGT-MIRI Cooperative Game Theory

Page 78: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Ice-cream game: Core

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

(200, 200, 350) is not in the core: v({C ,M}) > x({C ,M})(250, 250, 250) is in the core: alone or in pairs do not getmore.

(750, 0, 0) is also in the core:Marcie and Pattie cannot get more on their own!

AGT-MIRI Cooperative Game Theory

Page 79: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Ice-cream game: Core

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

(200, 200, 350)

is not in the core: v({C ,M}) > x({C ,M})(250, 250, 250) is in the core: alone or in pairs do not getmore.

(750, 0, 0) is also in the core:Marcie and Pattie cannot get more on their own!

AGT-MIRI Cooperative Game Theory

Page 80: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Ice-cream game: Core

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

(200, 200, 350) is not in the core: v({C ,M}) > x({C ,M})

(250, 250, 250) is in the core: alone or in pairs do not getmore.

(750, 0, 0) is also in the core:Marcie and Pattie cannot get more on their own!

AGT-MIRI Cooperative Game Theory

Page 81: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Ice-cream game: Core

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

(200, 200, 350) is not in the core: v({C ,M}) > x({C ,M})(250, 250, 250)

is in the core: alone or in pairs do not getmore.

(750, 0, 0) is also in the core:Marcie and Pattie cannot get more on their own!

AGT-MIRI Cooperative Game Theory

Page 82: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Ice-cream game: Core

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

(200, 200, 350) is not in the core: v({C ,M}) > x({C ,M})(250, 250, 250) is in the core: alone or in pairs do not getmore.

(750, 0, 0)

is also in the core:Marcie and Pattie cannot get more on their own!

AGT-MIRI Cooperative Game Theory

Page 83: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Ice-cream game: Core

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

(200, 200, 350) is not in the core: v({C ,M}) > x({C ,M})(250, 250, 250) is in the core: alone or in pairs do not getmore.

(750, 0, 0) is also in the core:

Marcie and Pattie cannot get more on their own!

AGT-MIRI Cooperative Game Theory

Page 84: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Ice-cream game: Core

Charlie: $4 Marcie: $3 Pattie: $3Ice-cream pots: w = (500, 750, 100) and p = ($7, $9, $11)

v(∅) = v({C}) = v({M}) = v({P}) = 0

v({C ,M}) = 500, v({C ,P}) = 500, v({M,P}) = 0

v({C ,M,P}) = 750

(200, 200, 350) is not in the core: v({C ,M}) > x({C ,M})(250, 250, 250) is in the core: alone or in pairs do not getmore.

(750, 0, 0) is also in the core:Marcie and Pattie cannot get more on their own!

AGT-MIRI Cooperative Game Theory

Page 85: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Games with empty core?

Let Γ = (N, v), where N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise.

Consider an outcome (P, x).

We have x1, x2, x3 ≥ 0, x1 + x2 + x3 = 1, and xi + xj = 1, fori 6= jAs, x1 + x2 + x3 ≥ 1, for some i ∈ {1, 2, 3}, xi ≥ 1/3.Assume that i = 1, we have x2 + x3 = 1− x1 ≤ 1− 1/3 ≤ 1!

Thus the core of Γ is empty.

AGT-MIRI Cooperative Game Theory

Page 86: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Games with empty core?

Let Γ = (N, v), where N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise.

Consider an outcome (P, x).

We have x1, x2, x3 ≥ 0, x1 + x2 + x3 = 1, and xi + xj = 1, fori 6= jAs, x1 + x2 + x3 ≥ 1, for some i ∈ {1, 2, 3}, xi ≥ 1/3.Assume that i = 1, we have x2 + x3 = 1− x1 ≤ 1− 1/3 ≤ 1!

Thus the core of Γ is empty.

AGT-MIRI Cooperative Game Theory

Page 87: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Games with empty core?

Let Γ = (N, v), where N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise.

Consider an outcome (P, x).

We have x1, x2, x3 ≥ 0, x1 + x2 + x3 = 1, and xi + xj = 1, fori 6= jAs, x1 + x2 + x3 ≥ 1, for some i ∈ {1, 2, 3}, xi ≥ 1/3.Assume that i = 1, we have x2 + x3 = 1− x1 ≤ 1− 1/3 ≤ 1!

Thus the core of Γ is empty.

AGT-MIRI Cooperative Game Theory

Page 88: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Games with empty core?

Let Γ = (N, v), where N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise.

Consider an outcome (P, x).

We have x1, x2, x3 ≥ 0, x1 + x2 + x3 = 1, and xi + xj = 1, fori 6= jAs, x1 + x2 + x3 ≥ 1, for some i ∈ {1, 2, 3}, xi ≥ 1/3.Assume that i = 1, we have x2 + x3 = 1− x1 ≤ 1− 1/3 ≤ 1!

Thus the core of Γ is empty.

AGT-MIRI Cooperative Game Theory

Page 89: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Games with empty core?

Let Γ = (N, v), where N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise.

Consider an outcome (P, x).

We have x1, x2, x3 ≥ 0, x1 + x2 + x3 = 1, and xi + xj = 1, fori 6= jAs, x1 + x2 + x3 ≥ 1, for some i ∈ {1, 2, 3}, xi ≥ 1/3.Assume that i = 1, we have x2 + x3 = 1− x1 ≤ 1− 1/3 ≤ 1!

Thus the core of Γ is empty.

AGT-MIRI Cooperative Game Theory

Page 90: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Core on payoff vectors

Suppose the game is not necessarily superadditive, but theoutcomes are defined as payoff vectors for the grand coalition.

Then the core may be empty, even if according to thestandard definition it is not.

Γ = (N, v) with N = {1, 2, 3, 4} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

not superadditive: v({1, 2}) + v({3, 4}) = 2 > v({1, 2, 3, 4})no payoff vector for the grand coalition is in the core:either {1, 2} or {3, 4} get less than 1, so can deviateBut (({1, 2}, {3, 4}), (1/2, 1/2, 1/2, 1/2)) is in the core

AGT-MIRI Cooperative Game Theory

Page 91: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Core on payoff vectors

Suppose the game is not necessarily superadditive, but theoutcomes are defined as payoff vectors for the grand coalition.

Then the core may be empty, even if according to thestandard definition it is not.

Γ = (N, v) with N = {1, 2, 3, 4} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

not superadditive: v({1, 2}) + v({3, 4}) = 2 > v({1, 2, 3, 4})no payoff vector for the grand coalition is in the core:either {1, 2} or {3, 4} get less than 1, so can deviateBut (({1, 2}, {3, 4}), (1/2, 1/2, 1/2, 1/2)) is in the core

AGT-MIRI Cooperative Game Theory

Page 92: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Core on payoff vectors

Suppose the game is not necessarily superadditive, but theoutcomes are defined as payoff vectors for the grand coalition.

Then the core may be empty, even if according to thestandard definition it is not.

Γ = (N, v) with N = {1, 2, 3, 4} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

not superadditive: v({1, 2}) + v({3, 4}) = 2 > v({1, 2, 3, 4})no payoff vector for the grand coalition is in the core:either {1, 2} or {3, 4} get less than 1, so can deviateBut (({1, 2}, {3, 4}), (1/2, 1/2, 1/2, 1/2)) is in the core

AGT-MIRI Cooperative Game Theory

Page 93: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Core on payoff vectors

Suppose the game is not necessarily superadditive, but theoutcomes are defined as payoff vectors for the grand coalition.

Then the core may be empty, even if according to thestandard definition it is not.

Γ = (N, v) with N = {1, 2, 3, 4} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

not superadditive: v({1, 2}) + v({3, 4}) = 2 > v({1, 2, 3, 4})no payoff vector for the grand coalition is in the core:either {1, 2} or {3, 4} get less than 1, so can deviateBut (({1, 2}, {3, 4}), (1/2, 1/2, 1/2, 1/2)) is in the core

AGT-MIRI Cooperative Game Theory

Page 94: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Core on payoff vectors

Suppose the game is not necessarily superadditive, but theoutcomes are defined as payoff vectors for the grand coalition.

Then the core may be empty, even if according to thestandard definition it is not.

Γ = (N, v) with N = {1, 2, 3, 4} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

not superadditive: v({1, 2}) + v({3, 4}) = 2 > v({1, 2, 3, 4})

no payoff vector for the grand coalition is in the core:either {1, 2} or {3, 4} get less than 1, so can deviateBut (({1, 2}, {3, 4}), (1/2, 1/2, 1/2, 1/2)) is in the core

AGT-MIRI Cooperative Game Theory

Page 95: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Core on payoff vectors

Suppose the game is not necessarily superadditive, but theoutcomes are defined as payoff vectors for the grand coalition.

Then the core may be empty, even if according to thestandard definition it is not.

Γ = (N, v) with N = {1, 2, 3, 4} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

not superadditive: v({1, 2}) + v({3, 4}) = 2 > v({1, 2, 3, 4})no payoff vector for the grand coalition is in the core:either {1, 2} or {3, 4} get less than 1, so can deviate

But (({1, 2}, {3, 4}), (1/2, 1/2, 1/2, 1/2)) is in the core

AGT-MIRI Cooperative Game Theory

Page 96: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Core on payoff vectors

Suppose the game is not necessarily superadditive, but theoutcomes are defined as payoff vectors for the grand coalition.

Then the core may be empty, even if according to thestandard definition it is not.

Γ = (N, v) with N = {1, 2, 3, 4} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

not superadditive: v({1, 2}) + v({3, 4}) = 2 > v({1, 2, 3, 4})no payoff vector for the grand coalition is in the core:either {1, 2} or {3, 4} get less than 1, so can deviateBut (({1, 2}, {3, 4}), (1/2, 1/2, 1/2, 1/2)) is in the core

AGT-MIRI Cooperative Game Theory

Page 97: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

ε-Core

When the core is empty, we may want to find approximatelystable outcomes.

We need to relax the notion of the core:core: (P, x) : x(C ) ≥ v(C ), for all C ⊆ N

ε-core: {(P, x) : x(C ) ≥ v(C )− ε, for all C ⊆ N}Γ = (N, v), N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3:xi ≥ 1/3, for some i = 1, 2, 3, so x(N \ {i}) ≤ 2/3,v(N \ {i}) = 1

AGT-MIRI Cooperative Game Theory

Page 98: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

ε-Core

When the core is empty, we may want to find approximatelystable outcomes.

We need to relax the notion of the core:core: (P, x) : x(C ) ≥ v(C ), for all C ⊆ N

ε-core: {(P, x) : x(C ) ≥ v(C )− ε, for all C ⊆ N}Γ = (N, v), N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3:xi ≥ 1/3, for some i = 1, 2, 3, so x(N \ {i}) ≤ 2/3,v(N \ {i}) = 1

AGT-MIRI Cooperative Game Theory

Page 99: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

ε-Core

When the core is empty, we may want to find approximatelystable outcomes.

We need to relax the notion of the core:

core: (P, x) : x(C ) ≥ v(C ), for all C ⊆ N

ε-core: {(P, x) : x(C ) ≥ v(C )− ε, for all C ⊆ N}Γ = (N, v), N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3:xi ≥ 1/3, for some i = 1, 2, 3, so x(N \ {i}) ≤ 2/3,v(N \ {i}) = 1

AGT-MIRI Cooperative Game Theory

Page 100: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

ε-Core

When the core is empty, we may want to find approximatelystable outcomes.

We need to relax the notion of the core:core: (P, x) : x(C ) ≥ v(C ), for all C ⊆ N

ε-core: {(P, x) : x(C ) ≥ v(C )− ε, for all C ⊆ N}Γ = (N, v), N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3:xi ≥ 1/3, for some i = 1, 2, 3, so x(N \ {i}) ≤ 2/3,v(N \ {i}) = 1

AGT-MIRI Cooperative Game Theory

Page 101: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

ε-Core

When the core is empty, we may want to find approximatelystable outcomes.

We need to relax the notion of the core:core: (P, x) : x(C ) ≥ v(C ), for all C ⊆ N

ε-core: {(P, x) : x(C ) ≥ v(C )− ε, for all C ⊆ N}

Γ = (N, v), N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3:xi ≥ 1/3, for some i = 1, 2, 3, so x(N \ {i}) ≤ 2/3,v(N \ {i}) = 1

AGT-MIRI Cooperative Game Theory

Page 102: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

ε-Core

When the core is empty, we may want to find approximatelystable outcomes.

We need to relax the notion of the core:core: (P, x) : x(C ) ≥ v(C ), for all C ⊆ N

ε-core: {(P, x) : x(C ) ≥ v(C )− ε, for all C ⊆ N}Γ = (N, v), N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3:xi ≥ 1/3, for some i = 1, 2, 3, so x(N \ {i}) ≤ 2/3,v(N \ {i}) = 1

AGT-MIRI Cooperative Game Theory

Page 103: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

ε-Core

When the core is empty, we may want to find approximatelystable outcomes.

We need to relax the notion of the core:core: (P, x) : x(C ) ≥ v(C ), for all C ⊆ N

ε-core: {(P, x) : x(C ) ≥ v(C )− ε, for all C ⊆ N}Γ = (N, v), N = {1, 2, 3} andv(C ) = 1 if |C | > 1 and v(C ) = 0 otherwise

1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3:xi ≥ 1/3, for some i = 1, 2, 3, so x(N \ {i}) ≤ 2/3,v(N \ {i}) = 1

AGT-MIRI Cooperative Game Theory

Page 104: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 105: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 106: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 107: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.

It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 108: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 109: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 110: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3

The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 111: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Least Core

If an outcome (P, x) is in the ε-core, the deficit v(C )− x(C )of any coalition is at most ε

We are interested in outcomes that minimize the worst-casedeficit

Let ε∗(Γ) = inf{ε|ε-core of Γ is not empty}.It can be shown that, for all Γ, the ε∗(Γ)-core is not empty.

The ε∗(Γ)-core is called the least core of Γ andε∗(Γ) is called the value of the least core

Γ = (N, v), N = {1, 2, 3}, v(C ) = (|C | > 1)1/3-core is non-empty: (1/3, 1/3, 1/3) ∈ 1/3-coreε-core is empty for any ε < 1/3The least core is the 1/3-core.

AGT-MIRI Cooperative Game Theory

Page 112: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Stability vs. Fairness

Outcomes in the core may be unfair.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

(15, 5) is in the core: player 2 cannot benefit by deviating.However, this is unfair since 1 and 2 are symmetric

How do we divide payoffs in a fair way?

AGT-MIRI Cooperative Game Theory

Page 113: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Stability vs. Fairness

Outcomes in the core may be unfair.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

(15, 5) is in the core: player 2 cannot benefit by deviating.

However, this is unfair since 1 and 2 are symmetric

How do we divide payoffs in a fair way?

AGT-MIRI Cooperative Game Theory

Page 114: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Stability vs. Fairness

Outcomes in the core may be unfair.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

(15, 5) is in the core: player 2 cannot benefit by deviating.However, this is unfair since 1 and 2 are symmetric

How do we divide payoffs in a fair way?

AGT-MIRI Cooperative Game Theory

Page 115: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Stability vs. Fairness

Outcomes in the core may be unfair.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

(15, 5) is in the core: player 2 cannot benefit by deviating.However, this is unfair since 1 and 2 are symmetric

How do we divide payoffs in a fair way?

AGT-MIRI Cooperative Game Theory

Page 116: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Marginal Contribution

A fair payment scheme rewards each agent according to hiscontribution.

Attempt: given a game Γ = (N, v), set

xi = v({1, ..., i − 1, i})− v({1, ..., i − 1}).

The payoff to each player is his marginal contribution to thecoalition of his predecessors

We have x1 + ...+ xn = v(N) thus x is a payoff vector

However, payoff to each player depends on the order

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 15

AGT-MIRI Cooperative Game Theory

Page 117: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Marginal Contribution

A fair payment scheme rewards each agent according to hiscontribution.

Attempt:

given a game Γ = (N, v), set

xi = v({1, ..., i − 1, i})− v({1, ..., i − 1}).

The payoff to each player is his marginal contribution to thecoalition of his predecessors

We have x1 + ...+ xn = v(N) thus x is a payoff vector

However, payoff to each player depends on the order

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 15

AGT-MIRI Cooperative Game Theory

Page 118: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Marginal Contribution

A fair payment scheme rewards each agent according to hiscontribution.

Attempt: given a game Γ = (N, v), set

xi = v({1, ..., i − 1, i})− v({1, ..., i − 1}).

The payoff to each player is his marginal contribution to thecoalition of his predecessors

We have x1 + ...+ xn = v(N) thus x is a payoff vector

However, payoff to each player depends on the order

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 15

AGT-MIRI Cooperative Game Theory

Page 119: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Marginal Contribution

A fair payment scheme rewards each agent according to hiscontribution.

Attempt: given a game Γ = (N, v), set

xi = v({1, ..., i − 1, i})− v({1, ..., i − 1}).

The payoff to each player is his marginal contribution to thecoalition of his predecessors

We have x1 + ...+ xn = v(N) thus x is a payoff vector

However, payoff to each player depends on the order

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 15

AGT-MIRI Cooperative Game Theory

Page 120: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Marginal Contribution

A fair payment scheme rewards each agent according to hiscontribution.

Attempt: given a game Γ = (N, v), set

xi = v({1, ..., i − 1, i})− v({1, ..., i − 1}).

The payoff to each player is his marginal contribution to thecoalition of his predecessors

We have x1 + ...+ xn = v(N) thus x is a payoff vector

However, payoff to each player depends on the order

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 15

AGT-MIRI Cooperative Game Theory

Page 121: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Marginal Contribution

A fair payment scheme rewards each agent according to hiscontribution.

Attempt: given a game Γ = (N, v), set

xi = v({1, ..., i − 1, i})− v({1, ..., i − 1}).

The payoff to each player is his marginal contribution to thecoalition of his predecessors

We have x1 + ...+ xn = v(N) thus x is a payoff vector

However, payoff to each player depends on the order

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 15

AGT-MIRI Cooperative Game Theory

Page 122: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Marginal Contribution

A fair payment scheme rewards each agent according to hiscontribution.

Attempt: given a game Γ = (N, v), set

xi = v({1, ..., i − 1, i})− v({1, ..., i − 1}).

The payoff to each player is his marginal contribution to thecoalition of his predecessors

We have x1 + ...+ xn = v(N) thus x is a payoff vector

However, payoff to each player depends on the order

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 15

AGT-MIRI Cooperative Game Theory

Page 123: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Average Marginal Contribution

Idea: Remove the dependence on ordering taking the averageover all possible orderings.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

1, 2: x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 152, 1: y2 = v({2})− v(∅) = 5, y1 = v({1, 2})− v({2}) = 15

z1 = (x1 + y1)/2 = 10, z2 = (x2 + y2)/2 = 10the resulting outcome is fair!

Can we generalize this idea?

AGT-MIRI Cooperative Game Theory

Page 124: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Average Marginal Contribution

Idea: Remove the dependence on ordering taking the averageover all possible orderings.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

1, 2: x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 152, 1: y2 = v({2})− v(∅) = 5, y1 = v({1, 2})− v({2}) = 15

z1 = (x1 + y1)/2 = 10, z2 = (x2 + y2)/2 = 10the resulting outcome is fair!

Can we generalize this idea?

AGT-MIRI Cooperative Game Theory

Page 125: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Average Marginal Contribution

Idea: Remove the dependence on ordering taking the averageover all possible orderings.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

1, 2: x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 152, 1: y2 = v({2})− v(∅) = 5, y1 = v({1, 2})− v({2}) = 15

z1 = (x1 + y1)/2 = 10, z2 = (x2 + y2)/2 = 10the resulting outcome is fair!

Can we generalize this idea?

AGT-MIRI Cooperative Game Theory

Page 126: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Average Marginal Contribution

Idea: Remove the dependence on ordering taking the averageover all possible orderings.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

1, 2: x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 152, 1: y2 = v({2})− v(∅) = 5, y1 = v({1, 2})− v({2}) = 15

z1 = (x1 + y1)/2 = 10, z2 = (x2 + y2)/2 = 10the resulting outcome is fair!

Can we generalize this idea?

AGT-MIRI Cooperative Game Theory

Page 127: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Average Marginal Contribution

Idea: Remove the dependence on ordering taking the averageover all possible orderings.

Γ = ({1, 2}, v) withv(∅) = 0, v({1}) = v({2}) = 5, v({1, 2}) = 20

1, 2: x1 = v({1})− v(∅) = 5, x2 = v({1, 2})− v({1}) = 152, 1: y2 = v({2})− v(∅) = 5, y1 = v({1, 2})− v({2}) = 15

z1 = (x1 + y1)/2 = 10, z2 = (x2 + y2)/2 = 10the resulting outcome is fair!

Can we generalize this idea?

AGT-MIRI Cooperative Game Theory

Page 128: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value

A permutation of {1, ..., n} is a one-to-one mapping from{1, ..., n} to itselfΠ(N) denote the set of all permutations of N

Let Sπ(i) denote the set of predecessors of i in π ∈ Π(N)

For C ⊆ N, let δi (C ) = v(C ∪ {i})− v(C )

The Shapley value of player i in a game Γ = (N, v) with nplayers is

Φi (Γ) =1

n!

∑π∈Π(N)

δi (Sπ(i))

In the previous slide we have Φ1 = Φ2 = 10

AGT-MIRI Cooperative Game Theory

Page 129: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value

A permutation of {1, ..., n} is a one-to-one mapping from{1, ..., n} to itselfΠ(N) denote the set of all permutations of N

Let Sπ(i) denote the set of predecessors of i in π ∈ Π(N)

For C ⊆ N, let δi (C ) = v(C ∪ {i})− v(C )

The Shapley value of player i in a game Γ = (N, v) with nplayers is

Φi (Γ) =1

n!

∑π∈Π(N)

δi (Sπ(i))

In the previous slide we have Φ1 = Φ2 = 10

AGT-MIRI Cooperative Game Theory

Page 130: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value

A permutation of {1, ..., n} is a one-to-one mapping from{1, ..., n} to itselfΠ(N) denote the set of all permutations of N

Let Sπ(i) denote the set of predecessors of i in π ∈ Π(N)

For C ⊆ N, let δi (C ) = v(C ∪ {i})− v(C )

The Shapley value of player i in a game Γ = (N, v) with nplayers is

Φi (Γ) =1

n!

∑π∈Π(N)

δi (Sπ(i))

In the previous slide we have Φ1 = Φ2 = 10

AGT-MIRI Cooperative Game Theory

Page 131: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value

A permutation of {1, ..., n} is a one-to-one mapping from{1, ..., n} to itselfΠ(N) denote the set of all permutations of N

Let Sπ(i) denote the set of predecessors of i in π ∈ Π(N)

For C ⊆ N, let δi (C ) = v(C ∪ {i})− v(C )

The Shapley value of player i in a game Γ = (N, v) with nplayers is

Φi (Γ) =1

n!

∑π∈Π(N)

δi (Sπ(i))

In the previous slide we have Φ1 = Φ2 = 10

AGT-MIRI Cooperative Game Theory

Page 132: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value

A permutation of {1, ..., n} is a one-to-one mapping from{1, ..., n} to itselfΠ(N) denote the set of all permutations of N

Let Sπ(i) denote the set of predecessors of i in π ∈ Π(N)

For C ⊆ N, let δi (C ) = v(C ∪ {i})− v(C )

The Shapley value of player i in a game Γ = (N, v) with nplayers is

Φi (Γ) =1

n!

∑π∈Π(N)

δi (Sπ(i))

In the previous slide we have Φ1 = Φ2 = 10

AGT-MIRI Cooperative Game Theory

Page 133: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value: Probabilistic Interpretation

Φi is i ’s average marginal contribution to the coalition of itspredecessors, over all permutations

Suppose that we choose a permutation of players uniformly atrandom, then Φi is the expected marginal contribution ofplayer i to the coalition of his predecessors

AGT-MIRI Cooperative Game Theory

Page 134: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value: Probabilistic Interpretation

Φi is i ’s average marginal contribution to the coalition of itspredecessors, over all permutations

Suppose that we choose a permutation of players uniformly atrandom, then Φi is the expected marginal contribution ofplayer i to the coalition of his predecessors

AGT-MIRI Cooperative Game Theory

Page 135: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley Value: Probabilistic Interpretation

Φi is i ’s average marginal contribution to the coalition of itspredecessors, over all permutations

Suppose that we choose a permutation of players uniformly atrandom, then Φi is the expected marginal contribution ofplayer i to the coalition of his predecessors

AGT-MIRI Cooperative Game Theory

Page 136: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Player’s properties

Given a game Γ = (N, v)

A player i is a dummy in Γ if

v(C ) = v(C ∪ {i}), for any C ⊆ N

Two players i and j are said to be symmetric in Γ if

v(C ∪ {i}) = v(C ∪ {j}), for any C ⊆ N \ {i , j}

AGT-MIRI Cooperative Game Theory

Page 137: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Player’s properties

Given a game Γ = (N, v)

A player i is a dummy in Γ if

v(C ) = v(C ∪ {i}), for any C ⊆ N

Two players i and j are said to be symmetric in Γ if

v(C ∪ {i}) = v(C ∪ {j}), for any C ⊆ N \ {i , j}

AGT-MIRI Cooperative Game Theory

Page 138: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Player’s properties

Given a game Γ = (N, v)

A player i is a dummy in Γ if

v(C ) = v(C ∪ {i}), for any C ⊆ N

Two players i and j are said to be symmetric in Γ if

v(C ∪ {i}) = v(C ∪ {j}), for any C ⊆ N \ {i , j}

AGT-MIRI Cooperative Game Theory

Page 139: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Player’s properties

Given a game Γ = (N, v)

A player i is a dummy in Γ if

v(C ) = v(C ∪ {i}), for any C ⊆ N

Two players i and j are said to be symmetric in Γ if

v(C ∪ {i}) = v(C ∪ {j}), for any C ⊆ N \ {i , j}

AGT-MIRI Cooperative Game Theory

Page 140: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley value: Axiomatic Characterization

Properties of the Shapley value:

Efficiency: Φ1 + ...+ Φn = v(N)

Dummy: if i is a dummy, Φi = 0

Symmetry: if i and j are symmetric, Φi = Φj

Additivity: Φi (Γ1 + Γ2) = Φi ((Γ1) + Φi (Γ2)

Theorem

The Shapley value is the only payoff distribution scheme that hasproperties (1) - (4)

Γ = Γ1 + Γ2 is the game (N, v) with v(C ) = v1(C ) + v2(C )

AGT-MIRI Cooperative Game Theory

Page 141: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Shapley value: Axiomatic Characterization

Properties of the Shapley value:

Efficiency: Φ1 + ...+ Φn = v(N)

Dummy: if i is a dummy, Φi = 0

Symmetry: if i and j are symmetric, Φi = Φj

Additivity: Φi (Γ1 + Γ2) = Φi ((Γ1) + Φi (Γ2)

Theorem

The Shapley value is the only payoff distribution scheme that hasproperties (1) - (4)

Γ = Γ1 + Γ2 is the game (N, v) with v(C ) = v1(C ) + v2(C )

AGT-MIRI Cooperative Game Theory

Page 142: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computational Issues

We have defined some solution concepts

can we compute them efficiently?

We need to determine how to represent a coalitional gameΓ = (N, v)?

Extensive list values of all coalitionsexponential in the number of players nSuccinct a TM describing the function vsome undecidable questions might arise

We are usually interested in algorithms whose running time ispolynomial in n

So what can we do? subclasses?

AGT-MIRI Cooperative Game Theory

Page 143: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computational Issues

We have defined some solution concepts

can we compute them efficiently?

We need to determine how to represent a coalitional gameΓ = (N, v)?

Extensive list values of all coalitionsexponential in the number of players nSuccinct a TM describing the function vsome undecidable questions might arise

We are usually interested in algorithms whose running time ispolynomial in n

So what can we do? subclasses?

AGT-MIRI Cooperative Game Theory

Page 144: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computational Issues

We have defined some solution concepts

can we compute them efficiently?

We need to determine how to represent a coalitional gameΓ = (N, v)?

Extensive list values of all coalitionsexponential in the number of players nSuccinct a TM describing the function vsome undecidable questions might arise

We are usually interested in algorithms whose running time ispolynomial in n

So what can we do? subclasses?

AGT-MIRI Cooperative Game Theory

Page 145: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computational Issues

We have defined some solution concepts

can we compute them efficiently?

We need to determine how to represent a coalitional gameΓ = (N, v)?

Extensive list values of all coalitionsexponential in the number of players nSuccinct a TM describing the function vsome undecidable questions might arise

We are usually interested in algorithms whose running time ispolynomial in n

So what can we do? subclasses?

AGT-MIRI Cooperative Game Theory

Page 146: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Checking Non-emptiness of the Core: Superadditive Games

An outcome in the core of a superadditive game satisfies thefollowing constraints:

xi ≥ 0 for all i ∈ N∑i∈N

xi = v(N)∑i∈C

xi ≥ v(C ), for any C ⊆ N

A linear feasibility program, with one constraint for eachcoalition: 2n + n + 1 constraints

AGT-MIRI Cooperative Game Theory

Page 147: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Checking Non-emptiness of the Core: Superadditive Games

An outcome in the core of a superadditive game satisfies thefollowing constraints:

xi ≥ 0 for all i ∈ N∑i∈N

xi = v(N)∑i∈C

xi ≥ v(C ), for any C ⊆ N

A linear feasibility program, with one constraint for eachcoalition: 2n + n + 1 constraints

AGT-MIRI Cooperative Game Theory

Page 148: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games: Computing the Least Core

Starting from the linear feasibility problem for the core

min ε

xi ≥ 0 for all i ∈ N∑i∈N

xi = v(N)∑i∈C

xi ≥ v(C )− ε, for any C ⊆ N

A minimization program, rather than a feasibility program

AGT-MIRI Cooperative Game Theory

Page 149: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Superadditive Games: Computing the Least Core

Starting from the linear feasibility problem for the core

min ε

xi ≥ 0 for all i ∈ N∑i∈N

xi = v(N)∑i∈C

xi ≥ v(C )− ε, for any C ⊆ N

A minimization program, rather than a feasibility program

AGT-MIRI Cooperative Game Theory

Page 150: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computing Shapley Value

Φi (Γ) =∑

π∈Π(N) δi (Sπ(i))

Φi (Γ) is the expected marginal contribution of player i to thecoalition of his predecessors

Quick and dirty way:

Use Monte-Carlo method to compute Φi (Γ)

Convergence guaranteed by Law of Large Numbers

AGT-MIRI Cooperative Game Theory

Page 151: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computing Shapley Value

Φi (Γ) =∑

π∈Π(N) δi (Sπ(i))

Φi (Γ) is the expected marginal contribution of player i to thecoalition of his predecessors

Quick and dirty way:

Use Monte-Carlo method to compute Φi (Γ)

Convergence guaranteed by Law of Large Numbers

AGT-MIRI Cooperative Game Theory

Page 152: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computing Shapley Value

Φi (Γ) =∑

π∈Π(N) δi (Sπ(i))

Φi (Γ) is the expected marginal contribution of player i to thecoalition of his predecessors

Quick and dirty way:

Use Monte-Carlo method to compute Φi (Γ)

Convergence guaranteed by Law of Large Numbers

AGT-MIRI Cooperative Game Theory

Page 153: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Core and variationsFairness: Shapley valueComputational Issues

Computing Shapley Value

Φi (Γ) =∑

π∈Π(N) δi (Sπ(i))

Φi (Γ) is the expected marginal contribution of player i to thecoalition of his predecessors

Quick and dirty way:

Use Monte-Carlo method to compute Φi (Γ)

Convergence guaranteed by Law of Large Numbers

AGT-MIRI Cooperative Game Theory

Page 154: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

1 Definitions

2 Stability notions

3 Other solution concepts

4 Subclasses

AGT-MIRI Cooperative Game Theory

Page 155: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Banzhaf index

The Banzhaf index of player i in game Γ = (N, v) is

βi (Γ) =1

2n−1

∑C⊆N

[v(C ∪ {i})− v(C )]

Dummy player, symmetry, additivity, but not efficiency.

AGT-MIRI Cooperative Game Theory

Page 156: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Banzhaf index

The Banzhaf index of player i in game Γ = (N, v) is

βi (Γ) =1

2n−1

∑C⊆N

[v(C ∪ {i})− v(C )]

Dummy player, symmetry, additivity, but not efficiency.

AGT-MIRI Cooperative Game Theory

Page 157: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Nucleolus

The nucleolus is a solution concept that defines a uniqueoutcome for a superadditive game.

Consider Γ = (N, v), C ⊆ N and a payoff vector x .

The deficit of C with respect to x is defined asd(x ,C ) = v(C )− x(C ).Any payoff vector x defines a 2n deficit vectord(x) = (d(x ,C1), . . . , d(x ,C2n)).Let ≤lex denote the lexicographic order

The nucleolus N (Γ) is the setN (Γ) = {x | d(x) ≤lex d(y) for all imputation y}.Can be computed by solving a polynomial number ofexponentially large LPs.

AGT-MIRI Cooperative Game Theory

Page 158: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Nucleolus

The nucleolus is a solution concept that defines a uniqueoutcome for a superadditive game.

Consider Γ = (N, v), C ⊆ N and a payoff vector x .

The deficit of C with respect to x is defined asd(x ,C ) = v(C )− x(C ).

Any payoff vector x defines a 2n deficit vectord(x) = (d(x ,C1), . . . , d(x ,C2n)).Let ≤lex denote the lexicographic order

The nucleolus N (Γ) is the setN (Γ) = {x | d(x) ≤lex d(y) for all imputation y}.Can be computed by solving a polynomial number ofexponentially large LPs.

AGT-MIRI Cooperative Game Theory

Page 159: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Nucleolus

The nucleolus is a solution concept that defines a uniqueoutcome for a superadditive game.

Consider Γ = (N, v), C ⊆ N and a payoff vector x .

The deficit of C with respect to x is defined asd(x ,C ) = v(C )− x(C ).Any payoff vector x defines a 2n deficit vectord(x) = (d(x ,C1), . . . , d(x ,C2n)).

Let ≤lex denote the lexicographic order

The nucleolus N (Γ) is the setN (Γ) = {x | d(x) ≤lex d(y) for all imputation y}.Can be computed by solving a polynomial number ofexponentially large LPs.

AGT-MIRI Cooperative Game Theory

Page 160: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Nucleolus

The nucleolus is a solution concept that defines a uniqueoutcome for a superadditive game.

Consider Γ = (N, v), C ⊆ N and a payoff vector x .

The deficit of C with respect to x is defined asd(x ,C ) = v(C )− x(C ).Any payoff vector x defines a 2n deficit vectord(x) = (d(x ,C1), . . . , d(x ,C2n)).Let ≤lex denote the lexicographic order

The nucleolus N (Γ) is the setN (Γ) = {x | d(x) ≤lex d(y) for all imputation y}.Can be computed by solving a polynomial number ofexponentially large LPs.

AGT-MIRI Cooperative Game Theory

Page 161: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Nucleolus

The nucleolus is a solution concept that defines a uniqueoutcome for a superadditive game.

Consider Γ = (N, v), C ⊆ N and a payoff vector x .

The deficit of C with respect to x is defined asd(x ,C ) = v(C )− x(C ).Any payoff vector x defines a 2n deficit vectord(x) = (d(x ,C1), . . . , d(x ,C2n)).Let ≤lex denote the lexicographic order

The nucleolus N (Γ) is the setN (Γ) = {x | d(x) ≤lex d(y) for all imputation y}.

Can be computed by solving a polynomial number ofexponentially large LPs.

AGT-MIRI Cooperative Game Theory

Page 162: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Nucleolus

The nucleolus is a solution concept that defines a uniqueoutcome for a superadditive game.

Consider Γ = (N, v), C ⊆ N and a payoff vector x .

The deficit of C with respect to x is defined asd(x ,C ) = v(C )− x(C ).Any payoff vector x defines a 2n deficit vectord(x) = (d(x ,C1), . . . , d(x ,C2n)).Let ≤lex denote the lexicographic order

The nucleolus N (Γ) is the setN (Γ) = {x | d(x) ≤lex d(y) for all imputation y}.Can be computed by solving a polynomial number ofexponentially large LPs.

AGT-MIRI Cooperative Game Theory

Page 163: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Kernel

The kernel consists of all outcomes where no player cancredibly demand a fraction of another player’s payoff.

Consider Γ = (N, v), i ∈ N and a payoff vector x .the surplus of i over the player j with respect to x is

Si ,j(x) = max{v(C )− x(C ) | C ⊆ N, i ∈ C , j /∈ C}

The kernel of a superadditive game Γ, K(Γ) is the set of allimputations x such that, for any pair of players (i , j) either:

Si,j(x) = Sj,i (x), orSi,j(x) > Sj,i (x) and xj = v({j}), orSi,j(x) < Sj,i (x) and xi = v({i}).

The kernel always contains de nucleolus, thus it is non-empty.

AGT-MIRI Cooperative Game Theory

Page 164: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Kernel

The kernel consists of all outcomes where no player cancredibly demand a fraction of another player’s payoff.

Consider Γ = (N, v), i ∈ N and a payoff vector x .the surplus of i over the player j with respect to x is

Si ,j(x) = max{v(C )− x(C ) | C ⊆ N, i ∈ C , j /∈ C}

The kernel of a superadditive game Γ, K(Γ) is the set of allimputations x such that, for any pair of players (i , j) either:

Si,j(x) = Sj,i (x), orSi,j(x) > Sj,i (x) and xj = v({j}), orSi,j(x) < Sj,i (x) and xi = v({i}).

The kernel always contains de nucleolus, thus it is non-empty.

AGT-MIRI Cooperative Game Theory

Page 165: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Kernel

The kernel consists of all outcomes where no player cancredibly demand a fraction of another player’s payoff.

Consider Γ = (N, v), i ∈ N and a payoff vector x .the surplus of i over the player j with respect to x is

Si ,j(x) = max{v(C )− x(C ) | C ⊆ N, i ∈ C , j /∈ C}

The kernel of a superadditive game Γ, K(Γ) is the set of allimputations x such that, for any pair of players (i , j) either:

Si,j(x) = Sj,i (x), orSi,j(x) > Sj,i (x) and xj = v({j}), orSi,j(x) < Sj,i (x) and xi = v({i}).

The kernel always contains de nucleolus, thus it is non-empty.

AGT-MIRI Cooperative Game Theory

Page 166: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Kernel

The kernel consists of all outcomes where no player cancredibly demand a fraction of another player’s payoff.

Consider Γ = (N, v), i ∈ N and a payoff vector x .the surplus of i over the player j with respect to x is

Si ,j(x) = max{v(C )− x(C ) | C ⊆ N, i ∈ C , j /∈ C}

The kernel of a superadditive game Γ, K(Γ) is the set of allimputations x such that, for any pair of players (i , j) either:

Si,j(x) = Sj,i (x), orSi,j(x) > Sj,i (x) and xj = v({j}), orSi,j(x) < Sj,i (x) and xi = v({i}).

The kernel always contains de nucleolus, thus it is non-empty.

AGT-MIRI Cooperative Game Theory

Page 167: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Kernel

The kernel consists of all outcomes where no player cancredibly demand a fraction of another player’s payoff.

Consider Γ = (N, v), i ∈ N and a payoff vector x .the surplus of i over the player j with respect to x is

Si ,j(x) = max{v(C )− x(C ) | C ⊆ N, i ∈ C , j /∈ C}

The kernel of a superadditive game Γ, K(Γ) is the set of allimputations x such that, for any pair of players (i , j) either:

Si,j(x) = Sj,i (x), orSi,j(x) > Sj,i (x) and xj = v({j}), orSi,j(x) < Sj,i (x) and xi = v({i}).

The kernel always contains de nucleolus, thus it is non-empty.

AGT-MIRI Cooperative Game Theory

Page 168: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ Cy dominates z (y dom z) if there is a coalition C such that ydominates z via coalition CFor a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 169: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ Cy dominates z (y dom z) if there is a coalition C such that ydominates z via coalition CFor a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 170: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ C

y dominates z (y dom z) if there is a coalition C such that ydominates z via coalition CFor a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 171: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ Cy dominates z (y dom z) if there is a coalition C such that ydominates z via coalition C

For a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 172: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ Cy dominates z (y dom z) if there is a coalition C such that ydominates z via coalition CFor a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 173: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ Cy dominates z (y dom z) if there is a coalition C such that ydominates z via coalition CFor a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 174: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ Cy dominates z (y dom z) if there is a coalition C such that ydominates z via coalition CFor a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 175: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Banzhaf indexNucleolusKernelStable set

Stable set

Consider Γ = (N, v) superadditive and two imputations y , z .

y dominates z via coalition C if y(C ) ≤ v(C ) and yi > zi , forany i ∈ Cy dominates z (y dom z) if there is a coalition C such that ydominates z via coalition CFor a set of imputations JDom(J) = {z | there exists y ∈ J, y dom z}.

A set of imputations J is a stable set of Γ if {J, Dom(J)} is apartition of the set of imputations.

Stable sets form the first solution proposed for cooperativegames [von Neuwmann, Morgensten, 1944].

There are games that have no stable sets [Lucas, 1968].

AGT-MIRI Cooperative Game Theory

Page 176: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

1 Definitions

2 Stability notions

3 Other solution concepts

4 Subclasses

AGT-MIRI Cooperative Game Theory

Page 177: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Some subclasses of cooperative games

Simple gamesv(C ) ∈ {0, 1} and monotone

Weighted voting gamesInfluence games

Combinatorial Optimization gamesv depends on some measure of a formed structure

Induced subgraph gamesNetwork flow gamesMinimum cost spanning tree gamesFacility location games

AGT-MIRI Cooperative Game Theory

Page 178: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Some subclasses of cooperative games

Simple gamesv(C ) ∈ {0, 1} and monotone

Weighted voting gamesInfluence games

Combinatorial Optimization gamesv depends on some measure of a formed structure

Induced subgraph gamesNetwork flow gamesMinimum cost spanning tree gamesFacility location games

AGT-MIRI Cooperative Game Theory

Page 179: Cooperative Game Theory: Solution conceptsmjserna/docencia/agt-miri/17/AGT9-coop-GT.pdfNon-Cooperative versus cooperative Games Non-cooperative game theory model scenarios where players

DefinitionsStability notions

Other solution conceptsSubclasses

Some subclasses of cooperative games

Simple gamesv(C ) ∈ {0, 1} and monotone

Weighted voting gamesInfluence games

Combinatorial Optimization gamesv depends on some measure of a formed structure

Induced subgraph gamesNetwork flow gamesMinimum cost spanning tree gamesFacility location games

AGT-MIRI Cooperative Game Theory