Research map / Multi-agent systems and games / Games and equilibria
Complexity of Probabilistic Inference in Random Dichotomous Hedonic Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Restrict agents to approving at most one coalition | Random DHGs, welfare-optimal coalition and specified-partition inference · 1-Lists | computational complexity: poly-time | Helped |
| Allow agents to approve at least two coalitions | Random DHGs, welfare-optimal coalition inference · k-Lists (k ≥ 2) | computational complexity: #P-hard | Hurt |
| Test only whether the welfare-optimality probability is positive | Random DHGs with k-lists or poly(n)-lists · Welfare-optimal coalition | computational complexity: FP | Helped |
| Consider welfare-optimality over all possible partitions | Random DHGs · 1-Lists | computational complexity: NP-complete | Hurt |
| Restrict approved coalitions to intervals in a linear ordering | Random DHGs · Candidate intervals | computational complexity: poly-time | Helped |
| Restrict each agent to approving coalitions of one size | Anonymous hedonic games · Specified Nash-stable and perfect partitions | computational complexity: poly-time | Helped |
| Set independent friendship probabilities to c/n | Random majority games · Perfect partitions | triangle-count distribution: Poisson random variable with parameter c3/6 | Helped |
Closest papers by meaning
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- Learning Coalition Structures with Games (AAAI 2024 · similarity 0.87)
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