Incentivized Network Dynamics in Digital Job Recruitment
Online platforms have transformed the formal job market but continue to struggle with effectively engaging passive candidates-individuals not actively seeking employment but open to compelling opportunities. We introduce the Independent Halting Cascade (IHC) model, a novel framework that integrates...
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Zusammenfassung: | Online platforms have transformed the formal job market but continue to
struggle with effectively engaging passive candidates-individuals not actively
seeking employment but open to compelling opportunities. We introduce the
Independent Halting Cascade (IHC) model, a novel framework that integrates
complex network diffusion dynamics with economic game theory to address this
challenge. Unlike traditional models that focus solely on information
propagation, the IHC model empowers network agents to either disseminate a job
posting or halt its spread by applying for the position themselves. By
embedding economic incentives into agent decision-making processes, the model
creates a dynamic interplay between maximizing information spread and promoting
application. Our analysis uncovers distinct behavioral regimes within the IHC
model, characterized by critical thresholds in recommendation and application
probabilities. Extensive simulations on both synthetic and real-world network
topologies demonstrate that the IHC model significantly outperforms traditional
direct-recommendation systems in recruiting suitable passive candidates.
Specifically, the model achieves up to a 30% higher hiring success rate
compared to baseline methods. These findings offer strategic insights into
leveraging economic incentives and network structures to enhance recruitment
efficiency. The IHC model thus provides a robust framework for modernizing
recruitment strategies, particularly in engaging the vast pool of passive
candidates in the job market. |
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DOI: | 10.48550/arxiv.2410.09698 |