Consensus Models and Group Decision-Making Techniques

Consensus models encompass a suite of mathematical and procedural frameworks designed to guide groups of experts or stakeholders towards a shared decision. These approaches address two core challenges: quantifying the degree of agreement among participants and prescribing mechanisms to elevate divergent opinions toward collective accord. Techniques range from classical voting rules and Delphi‐style iterative surveys to optimisation‐based soft consensus methods that balance consensus degree with associated costs. Fuzzy preference relations and nonlinear scaling functions further enrich these models by accommodating imprecise human judgements and attenuating the influence of extreme views. Across domains as varied as corporate strategy, public policy, emergency response and recommender systems, consensus frameworks enable more robust, transparent and efficient decisions. Central themes include the definition of consensus measures, the role of moderators or algorithmic aggregators, the determination of stopping criteria (consensus thresholds) and the evaluation of consensus‐reaching efficiency. The interplay between theoretical convergence guarantees and real‐world constraints—such as time, resource expenditure and stakeholder satisfaction—underscores the ongoing evolution of group decision‐making techniques on a global scale.

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Recent advances have refined the measurement of consensus cost and efficiency in group decision making. One line of work proposes a soft cost consensus model that simultaneously quantifies the degree of agreement and the moderator’s persuasion cost. By introducing a consensus level function and weighted aggregation operators, this approach delivers dual formulations for minimum cost and maximum return, with numerical examples illustrating its application to peer-to-peer lending scenarios.

An alternative strand emphasises objective determination of consensus thresholds through efficiency benchmarking. By integrating data envelopment analysis into the consensus process, this method offers a theoretically grounded way to set satisfaction levels and compare competing consensus‐reaching procedures. Numerical case studies demonstrate its capacity to balance consensus improvement against incurred costs without relying on subjective judgements.

Broader reviews have synthesised existing consensus models, especially in the context of group recommender systems. These surveys classify techniques according to consensus computation domains, coincidence methods, preference aggregation operators and guidance measures. They highlight emerging trends such as social relationship integration, leader centrality and intuitive visualisations of group consensus states, and they identify open challenges in adapting these frameworks to heterogeneous and large-scale decision settings.

Consensus Models and Group Decision-Making Techniques publication trend

The graph below shows the total number of articles in consensus models and group decision-making techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Consensus model: A mathematical or procedural framework defining how individual preferences are aggregated to achieve group agreement.

Consensus degree: A quantitative measure of alignment among group members’ opinions or preferences.

Soft consensus: An approach that allows for partial agreement by balancing consensus level against the cost or effort required to reach it.

Consensus threshold: A predefined level of agreement at which the group decision process is deemed complete.

Data envelopment analysis: A non-parametric optimisation technique used to assess the relative efficiency of decision-making units, here applied to consensus processes.

Fuzzy preference relation: A representation of individual judgments using degrees of preference rather than binary or linear scales, accommodating vagueness in human evaluations.

References

1. Soft consensus cost models for group decision making and economic interpretations. European Journal of Operational Research (2019).
2. How to determine the consensus threshold in group decision making: a method based on efficiency benchmark using benefit and cost insight. Annals of Operations Research (2021).
3. Nonlinear preferences in group decision‐making. Extreme values amplifications and extreme values reductions. International Journal of Intelligent Systems (2021).
4. An overview of consensus models for group decision-making and group recommender systems. User Modeling and User-Adapted Interaction (2023).