Generating consensus and dissent on massive discussion platforms with an $O(N)$ semantic-vector model
This provides a controllable method for balancing cohesion and diversity in collective intelligence platforms, though it is incremental as it applies an existing physics model to a new domain.
The paper tackled the problem of managing consensus and dissent on massive discussion platforms by introducing a dynamical system based on an O(N) model to drive aggregation of semantically similar ideas, showing that coupling parameters can control transitions between global consensus and maximum dissent states.
Reaching consensus on massive discussion networks is critical for reducing noise and achieving optimal collective outcomes. However, the natural tendency of humans to preserve their initial ideas constrains the emergence of global solutions. To address this, Collective Intelligence (CI) platforms facilitate the discovery of globally superior solutions. We introduce a dynamical system based on the standard $O(N)$ model to drive the aggregation of semantically similar ideas. The system consists of users represented as nodes in a $d=2$ lattice with nearest-neighbor interactions, where their ideas are represented by semantic vectors computed with a pretrained embedding model. We analyze the system's equilibrium states as a function of the coupling parameter $β$. Our results show that $β> 0$ drives the system toward a ferromagnetic-like phase (global consensus), while $β< 0$ induces an antiferromagnetic-like state (maximum dissent), where users maximize semantic distance from their neighbors. This framework offers a controllable method for managing the tradeoff between cohesion and diversity in CI platforms.