This document summarizes a lecture on cooperative game theory given at the 6th Summer School AACIMP in Kyiv, Ukraine from August 8-20, 2011. The lecture covers basic concepts in cooperative game theory including characteristic functions, imputations, the core, balanced games, the Shapley value, and the Weber set. Examples are provided to illustrate these concepts such as a glove game characteristic function and calculations of the core, Shapley value, and Weber set for this game.
This presentation is an attempt to introduce Game Theory in one session. It's suitable for undergraduates. In practice, it's best used as a taster since only a portion of the material can be covered in an hour - topics can be chosen according to the interests of the class.
The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
This presentation is an attempt to introduce Game Theory in one session. It's suitable for undergraduates. In practice, it's best used as a taster since only a portion of the material can be covered in an hour - topics can be chosen according to the interests of the class.
The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
Game theory is the study of mathematical models of strategic interaction between rational decision-makers.The mathematical theory of games was invented by John von Neumann and Oskar Morgenstern (1944). For reasons to be discussed later, limitations in their mathematical framework initially made the theory applicable only under special and limited conditions.Increasingly, companies are utilizing the science of Game Theory to help them make high risk/high reward strategic decisions in highly competitive markets and situations. ... Said another way, each decision maker is a player in the game of business.
This is a presentation by Mr. Muluneh Ayalew, Director, Monetary and Financial Analysis Directorate, National Bank of
Ethiopia, at the 3rd Annual East Africa Finance Summit
Game theory is the study of mathematical models of strategic interaction between rational decision-makers.The mathematical theory of games was invented by John von Neumann and Oskar Morgenstern (1944). For reasons to be discussed later, limitations in their mathematical framework initially made the theory applicable only under special and limited conditions.Increasingly, companies are utilizing the science of Game Theory to help them make high risk/high reward strategic decisions in highly competitive markets and situations. ... Said another way, each decision maker is a player in the game of business.
This is a presentation by Mr. Muluneh Ayalew, Director, Monetary and Financial Analysis Directorate, National Bank of
Ethiopia, at the 3rd Annual East Africa Finance Summit
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1. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Cooperative Game Theory. Operations Research
Games. Applications to Interval Games
Lecture 1: Cooperative Game Theory
Sırma Zeynep Alparslan G¨k
o
S¨leyman Demirel University
u
Faculty of Arts and Sciences
Department of Mathematics
Isparta, Turkey
zeynepalparslan@yahoo.com
August 13-16, 2011
2. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Outline
Introduction
Introduction to cooperative game theory
Basic solution concepts of cooperative game theory
Balanced games
Shapley value and Weber set
Convex games
Population Monotonic Allocation Schemes (pmas)
References
3. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Introduction
Introduction
Game theory is a mathematical theory dealing with models of
conflict and cooperation.
Game Theory has many interactions with economics and with
other areas such as Operations Research and social sciences.
A young field of study:
The start is considered to be the book Theory of Games and
Economic Behaviour by von Neumann and Morgernstern.
Game theory is divided into two parts: non-cooperative and
cooperative.
4. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Introduction
Introduction
Cooperative game theory deals with coalitions who coordinate their
actions and pool their winnings.
Natural questions for individuals or businesses when dealing with
cooperation are:
Which coalitions should form?
How to distribute the collective gains (rewards) or costs
among the members of the formed coalition?
5. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Introduction to cooperative game theory
Cooperative game theory
A cooperative n-person game in coalitional form (TU
(transferable utility) game) is an ordered pair < N, v >, where
N = {1, 2, ..., n} (the set of players) and v : 2N → R is a
map, assigning to each coalition S ∈ 2N a real number, such
that v (∅) = 0.
v is the characteristic function of the game.
v (S) is the worth (or value) of coalition S.
6. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Introduction to cooperative game theory
Example (Glove game)
N = {1, 2, 3}. Players 1 and 2 possess a left-hand glove and the
player 3 possesses a right-hand glove. A single glove is worth
nothing and a right-left pair of glove is worth 10 euros.
Let us construct the characteristic function v of the game
< N, v >.
v (∅) = 0, v ({1}) = v ({2}) = v ({3}) = 0,
v ({1, 2}) = 0, v ({1, 3}) = v ({2, 3}) = 10, v (N) = 10.
7. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Introduction to cooperative game theory
Cooperative game theory
G N : the set of coalitional games with player set N
G N forms a (2|N| − 1)-dimensional linear space equipped with
the usual operators of addition and scalar multiplication of
functions.
A basis of this space is supplied by the unanimity games uT
(or < N, uT >), T ∈ 2N {∅}, which are defined by
1, if T ⊂ S
uT (S) :=
0, otherwise.
The interpretation of the unanimity game uT is that a gain (or
cost savings) of 1 can be obtained if and only if all players in
coalition T are involved in cooperation.
8. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Introduction to cooperative game theory
Example
Since the unanimity games is the basis of coalitional games, each
cooperative game can be written in terms of unanimity games.
Consider the game < N, v > with N = {1, 2}, v ({1}) = 3,
v ({2}) = 4 and v (N) = 9.
Here v = 3u{1} + 4u{2} + 2u{1,2} .
Let us check it
v ({1}) = 3u{1} ({1}) + 4u{2} ({1}) + 2u{1,2} ({1}) = 3 + 0 + 0 = 3.
v ({2}) = 3u{1} ({2}) + 4u{2} ({2}) + 2u{1,2} ({2}) = 0 + 4 + 0 = 4.
v ({1, 2}) = 3u{1} ({1, 2})+4u{2} ({1, 2})+2u{1,2} ({1, 2}) = 3+4+2 = 9.
9. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Basic solution concepts of cooperative game theory
Basic solution concepts of cooperative game theory
A payoff vector x ∈ Rn is called an imputation for the game
< N, v > (the set is denoted by I (v )) if
x is individually rational: xi ≥ v ({i}) for all i ∈ N
n
x is efficient: i=1 xi = v (N)
Example (Glove game continues): The imputation set of the glove
game LLR is the triangle with vertices
f 1 = (10, 0, 0), f 2 = (0, 10, 0), f 3 = (0, 0, 10)
I (v ) = conv {(10, 0, 0), (0, 10, 0), (0, 0, 10)}
(solution of the linear system
x1 + x2 + x3 = 10, x1 ≥ 0, x2 ≥ 0, x3 ≥ 0).
10. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Basic solution concepts of cooperative game theory
The core (Gillies (1959))
The core of a game < N, v > is the set
C (v ) = x ∈ I (v )| xi ≥ v (S) for all S ∈ 2N {∅} .
i∈S
The idea of the core is by giving every coalition S at least their
worth v (S) so that no coalition has an incentive to split off.
If C (v ) = ∅, then elements of C (v ) can easily be obtained,
because the core is defined with the aid of a finite system of
linear inequalities (optimization-linear programming (see
Dantzig(1963))).
The core is a convex set and the core is a polytope (see
Rockafellar (1970)).
11. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Basic solution concepts of cooperative game theory
Example (Glove game continues)...
The core of the LLR game consists of one point (0, 0, 10).
C (v ) = {(0, 0, 10)}
(solution of the linear system x1 + x2 + x3 = 10,
x1 + x2 ≥ 0, x1 + x3 ≥ 10, x2 + x3 ≥ 10, x1 ≥ 0, x2 ≥ 0, x3 ≥ 0.)
12. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Balanced games
Balanced game
A map λ : 2N {∅} → R+ is called a balanced map if
S N
S∈2N {∅} λ(S)e = e .
Here, e S is the characteristic vector for coaliton S with
1, if i ∈ S
eiS :=
0, if i ∈ N S.
An n-person game < N, v > is called a balanced game if for each
balanced map λ : 2N {∅} → R+ we have
λ(S)v (S) ≤ v (N).
S
13. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Balanced games
Example
For N = {1, 2, 3}, the set B = {{1, 2} , {1, 3} , {2, 3}} is balanced
1
and corresponds to the balanced map λ with λ(S) = 2 if |S| = 2.
That is
1 1 1
v ({1, 2}) + v ({1, 3}) + v ({2, 3}) ≤ v (N)
2 2 2
Let us show it:
λ({1, 2})e {1,2} + λ({1, 3})e {1,3} + λ({2, 3})e {2,3} = e N
λ({1, 2})(1, 1, 0) + λ({1, 3})(1, 0, 1) + λ({2, 3})(0, 1, 1) = (1, 1, 1).
Solution of the above system is
1
λ({1, 2}) = λ({1, 3}) = λ({2, 3}) = 2 .
14. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Balanced games
Balanced game
The importance of a balanced game becomes clear by the following
theorem which characterizes games with a non-empty core.
Theorem (Bondareva (1963) and Shapley (1967)): Let < N, v >
be an n-person game. Then the following two assertions are
equivalent:
(i) C (v ) = ∅,
(ii) < N, v > is a balanced game.
15. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Shapley value and Weber set
Marginal contribution
Let v ∈ G N . For each i ∈ N and for each S ∈ 2N with i ∈ S, the
marginal contribution of player i to the coalition S is
Mi (S, v ) := v (S) − v (S {i}).
Let Π(N) be the set of all permutations σ : N → N of N.
The set P σ (i) := r ∈ N|σ −1 (r ) < σ −1 (i) consists of all
predecessors of i with respect to the permutation σ.
Let v ∈ G N and σ ∈ Π(N).
The marginal contribution vector mσ (v ) ∈ Rn with respect to σ
and v has the i-th coordinate the value
miσ (v ) := v (P σ (i) ∪ {i}) − v (P σ (i)) for each i ∈ N.
16. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Shapley value and Weber set
Example
Let < N, v > be the three-person game with v ({i}) = 0 for each
i ∈ N, v ({1, 2}) = 3, v ({1, 3}) = 5, v ({2, 3}) = 7, v (N) = 10.
Then the marginal vectors are given in the following table, where
σ : N → N is identified with (σ(1), σ(2), σ(3)).
σ σ σ σ
m1 (v ) m2 (v ) m3 (v )
(123)
0 3 7
(132)
0 5 5
.
(213)
3 0 7
(231)
3 0 7
(312) 5 5 0
(321) 3 7 0
17. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Shapley value and Weber set
The Shapley value (Shapley (1953)) and the Weber set
(Weber (1988))
The Shapley value φ(v ) of a game v ∈ G N is the average of the
marginal vectors of the game
1 σ (v ).
φ(v ) := n! σ∈Π(N) m
This value associates to each n-person game one (payoff) vector in
Rn .
The Shapley value of the previous example is
1 7 10 13
φ(v ) = (14, 20, 26) = ( , , ).
3! 3 3 3
The Weber set (Weber (1988)) W (v ) of v is defined as the convex
hull of the marginal vectors of v .
Theorem: Let v ∈ G N . Then C (v ) ⊂ W (v ).
18. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Shapley value and Weber set
Example (LLR game)
Marginal vectors can be observed from the following table
σ σ σ σ
m1 (v ) m2 (v ) m3 (v )
(123)
0 0 10
(132)
0 0 10
.
(213)
0 0 10
(231)
0 0 10
(312) 10 0 0
(321) 0 10 0
The Weber set is W (v ) = conv {(0, 0, 10), (10, 0, 0), (0, 10, 0)}.
The Shapley value is φ(v ) = ( 1 , 1 , 3 ).
6 6
2
Note that {(0, 0, 10)} = C (v ) ⊂ W (v ) and φ(v ) ∈ W (v ).
19. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Convex games
Convex games
< N, v > is convex if and only if the supermodularity
condition v (S ∪ T ) + v (S ∩ T ) ≥ v (S) + v (T ) for each
S, T ∈ 2N holds (desirable for reward games).
< N, v > is called concave (or submodular) if and only if
v (S ∪ T ) + v (S ∩ T ) ≤ v (S) + v (T ) for all S, T ∈ 2N
(desirable for cost games).
CG N –The family of all convex games with player set N.
20. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Convex games
Theorem (characterizations of convex games): Let v ∈ G N . The
following five assertions are equivalent:
(i) < N, v > is convex.
(ii) For all S1 , S2 , U ∈ 2N with S1 ⊂ S2 ⊂ N U we have
v (S1 ∪ U) − v (S1 ) ≤ v (S2 ∪ U) − v (S2 ).
(iii) For all S1 , S2 ∈ 2N and i ∈ N such that S1 ⊂ S2 ⊂ N {i} we
have
v (S1 ∪ {i}) − v (S1 ) ≤ v (S2 ∪ {i}) − v (S2 ).
(iv) Each marginal vector mσ (v ) of the game v with respect to
the permutation σ belongs to the core C (v ).
(v) W (v ) = C (v ).
21. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Convex games
For convex games the gain made when individuals or groups
join larger coalitions is higher than when they join smaller
coalitions.
A convex game is balanced and the core of the convex games
is nonempty.
The Shapley value is a core element if the game is convex.
22. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Population Monotonic Allocation Schemes (pmas)
Population Monotonic Allocation Schemes (pmas)
For a game v ∈ G N and a coalition T ∈ 2N {∅}, the subgame
with player set T , (T , vT ), is the game vT defined by
vT (S) := v (S) for all S ∈ 2T .
A game v ∈ G N is called totally balanced if (the game and) all its
subgames are balanced.
The class of totally balanced games includes the class of games
with a population monotonic allocation scheme (pmas) (Sprumont
(1990)).
23. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Population Monotonic Allocation Schemes (pmas)
Let v ∈ G N . A scheme a = (aiS )i∈S,S∈2N {∅} of real numbers is a
pmas of v if
(i) i∈S aiS = v (S) for all S ∈ 2N {∅},
(ii) aiS ≤ aiT for all S, T ∈ 2N {∅} with S ⊂ T and for each
i ∈ S.
Interpretation: in larger coalitions, higher rewards (or in larger
coalitions lower costs).
It is known that for v ∈ CG N the (total) Shapley value
generates population monotonic allocation schemes. Further,
in a convex game all core elements generate pmas.
24. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Population Monotonic Allocation Schemes (pmas)
Example
Let < N, v > be the 3-person game with v ({1}) = 10,
v ({2}) = 20, v ({3}) = 30,
v ({1, 2}) = v ({1, 3}) = v ({2, 3}) = 50, v (N) = 102.
Then a pmas is the (total) Shapley value.
1 2 3
N
29 34 39
{1, 2}
20 30 ∗
Φ({1, 3} , v ) → {1, 3}
15 ∗ 35 .
{2, 3}
∗ 20 30
{1}
10 ∗ ∗
{2} ∗ 20 ∗
{3} ∗ ∗ 30
25. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
Population Monotonic Allocation Schemes (pmas)
For detailed information about Cooperative game theory see
Introduction to Game Theory by Tijs
and
Models in Cooperative Game Theory by Branzei, Dimitrov
and Tijs.
26. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
References
References
[1]Bondareva O.N., Certain applications of the methods of linear
programming to the theory of cooperative games, Problemly
Kibernetiki 10 (1963) 119-139 (in Russian).
[2]Branzei R., Dimitrov D. and Tijs S., Models in Cooperative
Game Theory, Springer (2008).
[3]Dantzig G. B., Linear Programming and Extensions, Princeton
University Press (1963).
[4]Gillies D. B., Solutions to general non-zero-sum games, In:
Tucker, A.W. and Luce, R.D. (Eds.), Contributions to theory of
games IV, Annals of Mathematical Studies 40. Princeton
University Press, Princeton (1959) pp. 47-85.
[6] Rockafellar R.T., Convex Analysis, Princeton University Press,
Princeton, (1970).
27. 6th Summer School AACIMP - Kyiv Polytechnic Institute (KPI) - National Technical University of Ukraine, 8-20 August 2011
References
References
[7]Shapley L.S., On balanced sets and cores, Naval Research
Logistics Quarterly 14 (1967) 453-460.
[8]Shapley L.S., A value for n-person games, Annals of
Mathematics Studies, 28 (1953) 307-317.
[9]Sprumont Y., Population monotonic allocation schemes for
cooperative games with transferable utility, Games and Economic
Behavior, 2 (1990) 378-394.
[10] Tijs S., Introduction to Game Theory, SIAM, Hindustan Book
Agency, India (2003).
[11] von Neumann J. and Morgenstern O. , Theory of Games and
Economic Behavior, Princeton Univ. Press, Princeton NJ (1944).
[12] Weber R., Probabilistic values for games, in Roth A.E. (ed.),
The Shapley Value: Essays in Honour of Lloyd S. Shapley,
Cambridge University Press, Cambridge (1988) 101-119.