For many students encountering mathematical modeling for the first time, the most distinctive feature of the HiMCM (High School Mathematical Contest in Modeling) is that there are no fixed knowledge points and no standard answers.
Instead of a traditional math problem, you are faced with an open-ended real-world issue: it could be urban transportation, logistics distribution, ecological environments, artificial intelligence, energy, healthcare, or public administration.
Because of this, HiMCM is widely considered one of the international high school math competitions closest to the university research experience. It is highly recognized for college applications in majors such as mathematics, computer science, engineering, data science, and economics.
What Does HiMCM Actually Test? Do You Need to Be a Math Genius to Participate?
In fact, HiMCM tests much more than just mathematics. It focuses on whether a student team can utilize existing knowledge to solve real-world problems. The competition truly assesses comprehensive abilities.
These include: mathematical modeling skills, data analysis capabilities, proficiency in tools like Python, MATLAB, and Excel, literature search abilities, English academic writing skills, teamwork, and time management.
The competition is team-based, with 1 to 4 members per team, who must independently complete all work during the 14-day contest period. Teams need to choose either Problem A or Problem B to complete their modeling and ultimately submit an English paper of no more than 25 pages.
Why Is HiMCM Becoming Increasingly Difficult?
Many students think that a modeling competition is just about applying a few standard models. In reality, if you look at the past ten years of HiMCM problems, you will find that the direction of the questions has changed significantly.
Phase 1: 2014-2018
This period emphasized classical mathematical models. Many problems could be solved using classical tools such as optimization models, differential equations, and graph theory. As long as students had a solid mathematical foundation, they could perform well.
For example, logistics optimization, medical resource allocation, scheduling problems, and path planning are typical mathematical modeling problems.
Phase 2: 2019-2021
The competition began to incorporate a large amount of real-world data.
Many problems required teams to find, clean, and verify the reliability of data themselves, and to conduct sensitivity analysis. The evaluation criteria gradually shifted from whether the model was complex to whether the model was reliable and capable of real-world interpretation.
Phase 3: 2022-2025
This is the stage that has seen the most significant changes in recent years. The problems increasingly emphasize interdisciplinary approaches, social issues, and practical applications.
Many problems are no longer purely mathematical; they require a comprehensive understanding of economics, operations research, statistics, artificial intelligence, environmental science, and engineering design.
In many award-winning papers from recent years, what truly sets them apart is not the model itself, but whether the data sources are credible, whether the assumptions are reasonable, whether the logic of the paper is complete, whether the charts are professional, and whether the model interpretation is sufficient. These are aspects that students rarely encounter in regular classroom settings.
How Should You Choose Between Problem A and Problem B?
Based on the characteristics of past papers, it can be simply understood as follows:
Problem A: Usually leans towards continuous modeling.
It emphasizes differential equations, optimization models, simulation analysis, and mathematical derivation. It is suitable for teams with strong mathematical foundations and good physical modeling capabilities.
Problem B: Usually leans towards data analysis.
It emphasizes data processing, graph theory, network analysis, machine learning, and algorithm design. If the team has members with a solid foundation in Python, they are often better suited to choose Problem B.
It is important to note that neither problem is inherently easier to win. What truly determines the final result is whether the team can explain a problem clearly and completely within the limited time.
Summary of HiMCM Preparation Materials
First Category of Core Materials: Past Award-Winning Papers
If you have never read actual award-winning papers, it is easy to be unaware of what the judges are really looking for.
1. Learn the overall structure of the paper.
2. Learn how to express models.
3. Learn the language of academic writing.
Second Category of Core Materials: Past Exam Questions
Because HiMCM does not have a fixed syllabus, the only things that can truly help students understand the problem-setting mindset are the past Problem A and Problem B questions.
By studying past papers, you can quickly understand how the direction of the questions has changed, which types of models appear most frequently, and how to break down different types of problems.
Third Category of Core Materials: Mathematical Modeling Textbooks
After mastering the structure of excellent papers, you need to further build a knowledge system of models. The following textbooks are currently the most valuable classic references for HiMCM preparation.
Mathematical Modeling
This book is an introductory textbook for learning mathematical modeling. Many high-frequency HiMCM models can find corresponding cases in this book.
Statistics
In recent years, HiMCM has increasingly emphasized data-driven analysis, and statistics is the foundation of data analysis.
Multivariate Statistical Analysis
Once students have basic statistical skills, they can further learn multivariate statistical methods, which can help the team process complex data and improve the interpretability of the model. For teams aiming for Meritorious or higher awards, it is highly worth studying in advance.
Machine Learning
Although HiMCM does not require the use of machine learning models, in recent years, more and more outstanding papers have begun to try introducing machine learning methods.
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This is an independent English-language guide to HiMCM for Greater-China students, operated by Hanlin Education. It is not affiliated with, endorsed by, or sponsored by COMAP, the organiser of HiMCM. COMAP is the sole official authority for HiMCM rules, dates, registration and results — contest details change between editions, so always confirm on comap.org before registering or submitting. Confirmed errors are corrected within 7 working days.