PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.
Berdahl, Dora Biro, Giuseppe Car- bone, Ilaria Giannoccaro, Robert L
3 Pith papers cite this work. Polarity classification is still indexing.
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LLM societies in Nomic show non-monotonic collective adaptation peaking at mid-scales, with smaller models rule-inert and larger ones restrictive.
Agent-based simulations reveal that rigid specialist roles in ad-hoc multi-agent teams generate system bottlenecks, workload inequality, fragmented networks, and diminishing returns from added team members due to communication costs.
citing papers explorer
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On the Limits of PAC Learning of Networks from Opinion Dynamics
PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.
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Scale-Dependent Collective Adaptation in Self-Amending LLM Societies: A Cross-Family Study of Emergent Governance
LLM societies in Nomic show non-monotonic collective adaptation peaking at mid-scales, with smaller models rule-inert and larger ones restrictive.
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Too Many Specialists: Emergent Inefficiencies and Bottlenecks for Multi-agent Ad-hoc Collaboration
Agent-based simulations reveal that rigid specialist roles in ad-hoc multi-agent teams generate system bottlenecks, workload inequality, fragmented networks, and diminishing returns from added team members due to communication costs.