Explaining Your HiMCM Model: Using Fictional Data and Visual Insights
This guide outlines how to build and explain a HiMCM 2020 A problem model by creating diverse fictional personas, generating appropriate data, interpreting results, and leveraging charts such as radar, bar, and tables to clearly demonstrate model usefulness and adaptability.
1 Explanation of the Model
This article discusses the explanation part of the 2020 HiMCM A problem model, referencing the competition analysis articles.
The core requirement of the 2020 A problem is item 2: "Use your factors to develop a model or algorithm ... for a high school student to evaluate their summer job options based on their own situation and preferences as inputs to your model."
Item 1 provides background, and item 3 asks to test the model with at least ten fictional persons, explain their creation, and analyze the results. Using fictional personas helps illustrate how the model works, similar to how examples clarify theory.
2 Requirements for Virtual Data
To demonstrate the model’s practicality, the examples must be diverse, highlight the model’s core purpose, and include some special cases to show adaptability.
Examples should cover a wide range to reflect broad model applicability.
They should emphasize the model’s main function.
Special cases can demonstrate the model’s flexibility.
Thus, generating virtual data must be done carefully, selecting characters with varied traits (gender, motivations, personalities, etc.). The model must be able to process the data; otherwise, discuss limitations in the “strengths and weaknesses” section.
3 Interpreting Results Is Crucial
After feeding virtual data into the model and obtaining the most suitable job, one should reverse‑interpret why that job fits, using common sense as a double‑check.
4 Charts Enhance Communication
Visuals such as radar charts, bar charts, and tables can convey rich information efficiently.
Bar charts can compare job scores.
Tables can compare results of different models.
Various chart types are useful in modeling competitions.
5 Final Point
Explaining the model and its results is as important as building the tool itself; otherwise, only the creator understands it. Statistics show that award‑winning papers average five figures, so beginners should not overlook the role of charts.
Data Download
The referenced papers can be obtained by replying with 2020A10550 , 2020A10549 , and 2020A10656 in the public account chat.
Model Perspective
Insights, knowledge, and enjoyment from a mathematical modeling researcher and educator. Hosted by Haihua Wang, a modeling instructor and author of "Clever Use of Chat for Mathematical Modeling", "Modeling: The Mathematics of Thinking", "Mathematical Modeling Practice: A Hands‑On Guide to Competitions", and co‑author of "Mathematical Modeling: Teaching Design and Cases".
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