The roundtable: an abstract model of conversation dynamics

Lucas Lacasa; Martin Schmidt; Massimo Mastrangeli

The roundtable: an abstract model of conversation dynamics

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1010.2943 v1 pith:XHCXBBHZ submitted 2010-10-14 physics.soc-ph cs.SI

Massimo Mastrangeli , Martin Schmidt , Lucas Lacasa This is my paper · ORCID

classification physics.soc-phcs.SI

keywords conversationmodelabstractdynamicsconversationalfindincludingindividual

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

Is it possible to abstract a formal mechanism originating schisms and governing the size evolution of social conversations? In this work a constructive solution to such problem is proposed: an abstract model of a generic N-party turn-taking conversation. The model develops from simple yet realistic assumptions derived from experimental evidence, abstracts from conversation content and semantics while including topological information, and is driven by stochastic dynamics. We find that a single mechanism - namely the dynamics of conversational party's individual fitness, as related to conversation size - controls the development of the self-organized schisming phenomenon. Potential generalizations of the model - including individual traits and preferences, memory effects and more elaborated conversational topologies - may find important applications also in other fields of research, where dynamically-interacting and networked agents play a fundamental role.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction Forecasting
cs.LG 2025-01 conditional novelty 4.0 of 10

Social Process models, which treat each conversation group as a meta-learning task and condition forecasts on a short context of the same group, can interpolate to unseen synthetic group dynamics but do not extrapolat...

Pith tools