ARCHIVES

Contemporary organisations face a persistent difficulty in distinguishing genuinely capable candidates from those whose documented credentials overstate their practical abilities. Industry surveys consistently highlight inadequacies in conventional hiring signals: a 2022 LinkedIn workforce study found that over half of skills listed across hundreds of millions of professional profiles could not be independently confirmed, while a McKinsey executive survey indicated that the large majority of global enterprises already face, or expect to face, significant skill shortfalls in the near term [1, 2]. This paper introduces WEPING, a reward-incentivized, cooperative problem-solving marketplace that allows individuals to establish expertise through direct participation in authentic industry challenges under verifiable, competitive conditions. The system incorporates four original technical contributions: an Adaptive Skill Credibility Scoring (ASCS) model that synthesizes five distinct performance dimensions into a single composite metric; an exponential score-decay mechanism that progressively discounts solver reputations during periods of verified inactivity; a cosine-similarity recommendation engine that pairs problems with the most suitably qualified solvers; and a milestone-based escrow payment architecture with an integrated dispute-resolution pathway. Controlled simulation trials across 500 participants, 120 problem instances, and 40 specialist mentors show that WEPING yields a talent identification accuracy of 83 %—substantially exceeding the 64 % recorded by freelance rating benchmarks and the 49 % characteristic of conventional resume screening—confirming a statistically meaningful gain in competency assessment reliability.