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 extrapolate beyond training variety.
The roundtable: an abstract model of conversation dynamics
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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.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction Forecasting
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 extrapolate beyond training variety.