Two-Sided Matching Decision Making in Uncertain Environments

Two-sided matching decision making under uncertainty concerns pairing agents from two distinct groups, each with individual preferences that may be incomplete, dynamic or fuzzy. Such scenarios arise in labour markets, service platforms, healthcare systems and resource allocation, where stability and mutual satisfaction are paramount. Uncertain environments introduce challenges in modelling preference information, which may be subject to ambiguity, evolving criteria or behavioural biases. Recent methodological advances integrate fuzzy set theory, behavioural decision frameworks and multi-objective optimisation to capture nuanced attitudes, such as regret or disappointment, while ensuring stable and efficient outcomes. By accommodating social networks, temporal dynamics and evaluator heterogeneity, modern models deliver robust, context-sensitive matching solutions with global relevance.

Research from Nature Portfolio

Hybrid decision frameworks have been advanced to tackle two-sided matching with complex preference structures. One approach combines the Best–Worst Method and TOPSIS within an intuitionistic fuzzy environment, augmented by large-scale group decision making and social network relations. This method systematically elicits criterion weights and ranks candidate–position pairs to optimise person–job fit, as demonstrated in high-level talent recruitment contexts. More recently, a dynamic person–position matching model based on hesitant fuzzy numbers has been proposed, introducing time-dependent satisfaction functions. By calculating initial and growth satisfactions using correlation coefficients and an exponential decay weighting scheme, this model secures stable matches that adapt to evolving preferences, offering a versatile tool for human resource placement.

Research from all publishers

Innovations in two-sided matching continue across diverse application domains. In the property trading sector, a multi-objective model addresses intermediary moral hazard by minimising conflict of interest while maximising mutual benefits, solved via a lexicographic optimisation procedure and validated on real estate platform data. Another line of work tackles incomplete weak preference orderings and heterogeneous fuzzy demand, computing expectation ordinal values and perceived value matrices to formulate an optimisation model that maximises collective satisfaction under stability constraints. In healthcare, a stable matching framework incorporates disappointment theory to translate hospital and patient preference orders into perceived utilities, constructing a multi-objective programme that achieves balanced satisfaction in critical resource allocation.

Two-Sided Matching Decision Making in Uncertain Environments publication trend

The graph below shows the total number of articles in two-sided matching decision making in uncertain environments across all publications each year (not limited to Nature Index journals).

Technical terms

Two-sided matching: Allocation process pairing agents from two groups based on mutual preferences under stability and optimality criteria.

Hesitant fuzzy number: Representation permitting multiple membership degrees to capture uncertainty in evaluation values.

Intuitionistic fuzzy set: Extension of fuzzy sets defined by degrees of membership, non-membership and hesitation.

Best–Worst Method: Multi-criteria decision technique in which the most and least preferred criteria are compared to derive criterion weights.

TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution, ranking options by distance from ideal and nadir reference points.

Disappointment theory: Behavioural model accounting for negative emotions experienced when outcomes fall short of expectations, influencing decision preferences.

References

1. Two-sided matching based on I-BTM and LSGDM applied to high-level overseas talent and job fit problems. Scientific Reports (2021).
2. Dynamic person-position matching decision method based on hesitant fuzzy number information. Scientific Reports (2024).
3. Bilateral matching decision-making in property trading platform: a method considering intermediary moral hazard. International Journal of Strategic Property Management (2023).
4. Two-Sided Matching Decision-Making in an Incomplete and Heterogeneous Context: A Optimization-Based Method. International Journal of Computational Intelligence Systems (2022).
5. Stable Two-Sided Satisfied Matching for Hospitals and Patients Based on the Disappointment Theory. International Journal of Computational Intelligence Systems (2022).