基于信息不对称的研究生学习质量甄别模型

摘要

Based on the analysis of asymmetric information problems in screening process of graduate studies quality, and in combination with incentive theory, we construct a multi-stage adverse selection model to seek the solutions in accordance with the stage.By analysing graduate students’ course grades in different stages and screening their authenticity, we expatiate on the consequence that the award effects for the students with low-level learning scores will be reduced by pretending high-level of learning scores.By using Bayesian formula and great value theorem, a mathematical model is built up to solve all questions above.The results show that on the condition of asymmetric information, students could be encouraged to do best for showing their true level of learning by reasonable award and compensation designed in advance, and the university’ s reward effects are to be improved as well.%剖析研究生学习质量甄别过程中信息不对称问题，结合激励机制理论，构造多段逆向选择模型，实现按阶段求解。通过分析研究生不同阶段的课程成绩，甄别各阶段成绩真实性，阐述低水平学习质量的研究生假装高水平将会降低奖励效果。通过贝叶斯公式以及极大值定理给出数学模型以及求解。结果表明，在信息不对称情况下，通过预先设计合理的奖励措施和补偿可以使研究生尽可能表现出自己的真实水平，提高研究生培养单位的奖励效果。