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Bounding the Cost of Stability in Games with Restricted Interaction Reshef Meir, Yair Zick, Edith Elkind and Jeffrey S. Rosenschein COMSOC 2012 (to appear)
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Cooperative TU Games Agents divide into coalitions; generate profit. 1 6 4 5 3 2 Coalition members can freely divide profits. How should profits be divided? $5 $3 $2
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TU Games - Notations Agents: N = {1,…, n } Coalition: S µ N Characteristic function: v : 2 N → R A TU game is simple, if every coalition either wins or loses, i.e. v : 2 N → {0,1} A TU game is monotone, if the value of a coalition can only increase by adding more agents to it.
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Payoffs Agents may freely distribute profits. An outcome is a coalition structure CS and a vector x = ( x 1,…, x n ) such that Σ i 2 S x i = v ( S ) for all S in CS Individual rationality: each agent gets at least what she can make on her own: x i ≥ v ({ i })
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The Core The core is the set of all stable outcomes: for all S µ N we have x ( S ) ¸ v ( S ) May be empty in many games. Example: the 3-majority game. Three players; any set of size two or more has a value of 1; singletons have a value of 0.
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Some coalitions may be impossible or unlikely due to practical reasons an underlying communication network (Myerson’77). agents are nodes. A coalition can form only if its agents are connected. 1 2 3 4 5 6 7 8 9 10 11 12 Restricted Cooperation
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Restricted cooperation - example The coalition {2,9,10,12} is allowed The coalition {3,6,7,8} is not allowed 1 2 3 4 5 6 7 8 9 10 11 12
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Restricted cooperation increases stability Theorem [Demange’04]: If the underlying communication network H is a tree, then the core is non-empty. Moreover, a core outcome can be computed efficiently. 1 2 3 4 5 6 7 8 9 10 11 12
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Using Subsidies to Stabilize the game Originally, we divided OPT ( G ) between the agents. We increase the value of OPT ( G ), creating a “superimputation”. Division of the incremented value α∙ OPT ( G )
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The Cost Of Stability (CoS) Observation: With a big enough payment, any game can be stabilized α ≤ n The Cost of Stability (CoS) is the minimal subsidy α that stabilizes the game. i.e. allows a non-empty core in G (α) (Bachrach et al., SAGT’09)
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Back to our example 3-majority game (core is empty) By distributing a total payoff of 1½ (rather than 1 ), the core of G (1½) is non-empty. x = (½, ½, ½) is a stable superimputation. CoS ( G ) ≤ 1½ This bound is tight! No lower subsidy will stabilize the game. CoS ( G ) = 1½
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CoS with restricted cooperation Recall that by [Demange’04] : if H is a tree, then the core is non-empty (i.e. CoS = 1 ). What is the connection between graph complexity and the cost of stability? Theorem [Meir et al., IJCAI’11] : If H contains a single cycle, then CoS ( G | H ) ≤ 2, and this is tight
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Graphs and tree-width Combinatorial measures to the “complexity” of a graph: Degree Path-width Tree-width Many NP-hard combinatorial problems become easy when the tree-width is bounded. 1 2 3 4 5 6 7 8 9 10 11 1,2,3 2,4 2,5,9 5,9,10 5,8,10 5,6,8 6,7,8 9,11
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Conjectured Connections Conjecture [MRM’11]: Let d be the maximal degree in H, then CoS ( G | H ) ≤ d Conjecture: Let k be the tree-width of H, then CoS ( G | H ) ≤ k There are games on a 3-dimensional grid ( d = 6 ) with unbounded CoS This is “almost” true.
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Our Main Result Theorem: For any G with an interaction graph H CoS ( G | H ) ≤ tw ( H ) + 1 and this bound is tight. Theorem: For any G with an interaction graph H CoS ( G | H ) ≤ tw ( H ) + 1 and this bound is tight. Also, a stable payoff vector can be found efficiently in the case of simple, monotone games.
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Step 1 – Simple Games 1,2,32,4 2,5,9 5,9,10 5,8,10 5,6,8 6,7,8 9,11 {5,6,8,10} 1.Traverse the nodes from the leaves up. 2.Once the subtree contains a winning coalition, pay 1 to all agents in its root. 3.Delete agents. (x1(x1 x2x2 x3x3 x4x4 x5x5 x6x6 x7x7 x8x8 x9x9 x 10 ) ( 0000000000 ) 1 11 9 2,9
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Stability: every winning coalition intersects a node in the tree decomposition that was paid by the algorithm; thus gets at least 1. Lemma: For any simple G with an interaction graph H, the algorithm produces a stable imputation x such that x ( N ) ≤ ( tw ( H ) + 1) OPT ( G | H ) Lemma: For any simple G with an interaction graph H, the algorithm produces a stable imputation x such that x ( N ) ≤ ( tw ( H ) + 1) OPT ( G | H )
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Bounded payoff: let S t be the set of agents that were removed at time t. S t contains a winning coalition W t We can partition the agents into a coalition structure CS = {{ W t } t 2 T*, L }. T* is the set of all times where sets were pruned by the algorithm. The value of CS is at most | T* |. x ( N ) ≤ ( tw ( H ) + 1) | T* | ≤ ( tw ( H ) + 1) OPT ( G | H )
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Step 2 – The General Case 1.Given a general (integer) game, split it into simple games and stabilize each individually. 2.Sum the resulting stable imputations. v ({1}) v ({2}) v ({3}) v ({1,2}) v ({1,3}) v ({2,3}) v(N)v(N)
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Tightness a1a1 a2a2 a4a4 a3a3 c1c1 c2c2 c4c4 c3c3 z1z1 z3z3 z2z2 b1b1 b2b2 b4b4 b3b3 W 1,1 = { z 1 ; a 1 ; a 4 ; b 3 ; b 1 } W 1,2 = { z 1 ; a 2 ; a 3 ; b 2 ; b 4 } W 2,1 = { z 2 ; b 1 ; b 4 ; c 3 ; c 1 } W 2,2 = { z 2 ; b 2 ; b 3 ; c 2 ; c 4 } W 3,1 = { z 3 ; c 1 ; c 4 ; a 3 ; a 1 } W 3,2 = { z 3 ; c 2 ; c 3 ; a 2 ; a 4 } Any two winning coalitions intersect: optimal value is 1.
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Tightness a1a1 a2a2 a4a4 a3a3 c1c1 c2c2 c4c4 c3c3 z1z1 z3z3 z2z2 b1b1 b2b2 b4b4 b3b3 W 1,1 = { z 1 ; a 1 ; a 4 ; b 3 ; b 1 } W 1,2 = { z 1 ; a 2 ; a 3 ; b 2 ; b 4 } W 2,1 = { z 2 ; b 1 ; b 4 ; c 3 ; c 1 } W 2,2 = { z 2 ; b 2 ; b 3 ; c 2 ; c 4 } W 3,1 = { z 3 ; c 1 ; c 4 ; a 3 ; a 1 } W 3,2 = { z 3 ; c 2 ; c 3 ; a 2 ; a 4 } x(W1,1) ¸ 1x(W1,1) ¸ 1 x ( W 1,2 ) ¸ 1 x z 1 ¸ 1 - ½ ( x ( A ) + x ( B )) A B C Z x ( Z ) ¸ 3 - ( x ( A ) + x ( B ) + x ( C )) x ( N ) ¸ 3
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Discussion/Future Work A slightly better (tight) bound holds for the pathwidth of the interaction graph: we can drop the +1. Bounded tree-width does not facilitate computations (e.g. Greco et al.’11) Other graphical models of cooperative games? Non-cooperative games? Other measures of graph complexity?
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Thank you! Questions?
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