Algorithmic aspects of the core in cooperative games over graphs

1. Algorithmic aspects of the core in cooperative games over graphs Vangelis Markakis Athens University of Economics and Business Dept. of Informatics Joint work with: Georgios Chalkiadakis, (University of Southampton) Nicholas R. Jennings (University of Southampton)

2. Our focus Cooperative games with restrictions on the set of allowable coalitions Restrictions defined via a graph structure Computational Complexity issues Can we compute a core element or decide if an element belongs to the core with efficient algorithms?

3. Outline • Cooperative games with restricted cooperation • TU games on lines, trees, and rings Algorithmic and NP-hardness results • Extensions to Partition Function Games • Conclusions / Future work 3

4. The standard model of TU games • Set of players N = {1,…,n} • A TU (Transferable Utility) game is a pair • Value of coalition S: • Usually superadditivity is also assumed (we will not insist on this here) 4

5. The standard model of TU games • Let Π = {C1,…,Ck} be a partition of N • Ι(Π) =imputations of Π: • Core: 5

6. TU games defined on graph structures • In some settings not all coalitions are allowed to form [Aumann-Dreze ’74, Myerson ’77] • Physical limitations on communication, • Legal banishments, • Players with similar expertise need not participate in the same coalition • … • Cooperation structures suggested so far: • Hierarchies (directed trees), general graphs (directed, undirected), antimatroids [van den Brink ’08] • Applications: sensor networks, telecommunication networks, various multi-agent and multi-robot systems

7. TU games defined on graph structures • Let G be an undirected graph • Feasible coalitions: F(G) = connected coalitions, i.e., all sets S such that the subgraph induced by S is connected • Feasible partitions: P(G) = partitions into feasible coalitions • New version of core:

8. TU games defined on graph structures 1 ● Example: 3 2 ● ● ● ● ● ● ● ● 4 5 6 7 8 9 ● • {1,2,3,5,8}F(G), {2,3,5,8}F(G) • ({2,4,5,6,7}, {1,3,8,9,10,11}) P(G) • ({1,2,4}, {5,6,7}, {1,3,8,9,10,11}) P(G) 11 ● 10

9. Algorithmic issues Computational problems: CORE-NONEMPTINESS: Given a game on a graph G, is the core of the game non-empty? CORE-FIND: Given a game, find an element in the coreor output that the core is empty. CORE-MEMBERSHIP: Given (Π,x), does it belong to the core? Polynomial time algorithms / impossibility results ?

10. The state of the art Note: For many classes of general TU-games without restrictions, the problems are known to be NP-hard or co-NP-hard

11. Algorithmic results An algorithm for CORE-FIND on trees [Demange ’04] • Pick a vertex r as the root (think of the tree as oriented from the root downwards to the leaves) • Step 1: We compute a guarantee level gi for every node i. • For leaves, gi is the reservation value. • For an internal node i, where max taken over subtrees starting at i • Step 2: Start from root and go downwards. Pick the coalition T (subtree) where the root achieves its guarantee level. Continue downwards in same manner for remaining nodes, not included in T.

12. Algorithmic results Note: For superadditive games on a line, player i simply receives its marginal contribution of being added to coalition {1,…,i-1} Theorem [Demange ’04]: The outcome of the algorithm belongs to the core.

13. Algorithmic results (Worst case) running time analysis of the algorithm Theorem: • For superadditive games on a line the complexity of the algorithm is O(n) • For non-superadditive games on a line, the complexity is O(n2) • For superadditive games on a tree, the complexity is O(n) • For non-superadditive games on a tree the algorithm requires exponential time

14. Hardness results Theorem: For general games on trees, CORE-FIND is NP-hard and CORE-MEMBERSHIP is co-NP- complete, even when the tree is a star. Proof: By reductions from the PARTITION problem: Given n numbers a1,…,anis there a subset S such that

15. Hardness results–Proof Sketch Given n numbers a1,…,an, we construct a tree with n leaves 0 ● … ● ● ● ● 1 2 n n-1 Finding an element in the core corresponds to finding the “right” subset of the leaves that will join the root

16. Outline Cooperative games with restricted cooperation TU games on lines, trees, and rings Algorithmic and NP-hardness results Extensions to Partition Function Games Conclusions / Future work 16

17. Partition Function Games • Externalities in cooperative games [Thrall, Lucas ’63] • The value of a coalition may depend on the partitioning of the other players • V(S, Π): value of S in partition Π • How should a deviating coalition reason about the behavior of the non-deviators?

18. Partition Function Games • Pessimistic approach: assume the rest of the players will partition themselves so as to hurt you the most • Optimistic approach: assume the rest of the players will form a partition, most profitable for you. • Other approaches have also been considered, each resulting in a different notion of core [Koczy ’07]

19. PFGs on graphs Theorem: For PFGs on trees, • The pessimistic core is always non-empty. • There exist games where the optimistic core is empty.

20. Extensions and future work • Resolve open questions regarding the core • Polynomial time approximation algorithms for the core? • Other cooperative solution concepts? • Other classes of graphs or cooperation structures? • Bayesian Partition Function games

21. Thank you!