Aggregation Rules and Dynamic Weight Optimization in Multi-Source Evaluation Mechanisms: An Institutional Analysis of Ranking Robustness in Competitive Markets

Abstract

This paper addresses the issues of weight structure and aggregation rules in hybrid evaluation mechanisms by constructing an analytical framework that combines backward inference with dynamic optimization. Under conditions of incomplete observational data, the “minimum necessary support” model is introduced. Combined with Monte Carlo simulations to characterize the feasible space of unobservable variables, this approach reveals the structural amplification effect of evaluation source differences under percentage summation rules. Further comparative analysis demonstrates that ranking methods effectively constrain extreme concentration shocks, enhancing result robustness. Building upon this, a multi-objective dynamic weight optimization framework is proposed. It identifies balanced weight intervals via the Pareto frontier and designs a phased dynamic adjustment mechanism to harmonize early-stage responsiveness with late-stage stability. This research integrates reverse inference, distribution simulation, and optimization analysis to provide a universal quantitative basis for designing multi-source information weighting and decision aggregation mechanisms.