PAC-Learning Framework for Consensus Elicitation in Online Deliberation
A recent study introduces a machine learning method aimed at detecting consensus within online deliberation platforms. The researchers conceptualize consensus as a range in a one-dimensional opinion spectrum, which is extracted from high-dimensional user preference data using embedding and dimensionality reduction techniques. They establish an objective that seeks to maximize anticipated agreement within a specified hypothesis interval while considering the importance of various topics. An effective Empirical Risk Minimization (ERM) algorithm is presented, complete with PAC-learning assurances. Preliminary tests showcase the algorithm's capabilities and investigate more efficient strategies for determining optimal consensus intervals. This research can be found on arXiv under ID 2604.21811.
Key facts
- Paper ID: arXiv:2604.21811
- Published on arXiv
- Proposes PAC-learning framework for consensus elicitation
- Models consensus as interval in one-dimensional opinion space
- Uses embedding and dimensionality reduction
- Objective maximizes expected agreement with topic salience
- Introduces ERM algorithm with PAC guarantees
- Initial experiments show algorithm performance
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Institutions
- arXiv