What Is a Decision Execution Model? A Step‑by‑Step Guide
This article explains decision execution models, defining the concept, outlining its ideal assumptions, comparing it to mathematical induction, and discussing why real‑world constraints make the model impractical while still offering valuable insights for improving decision‑making processes.
Decision Execution Model (Part 1)
In daily life we constantly need to make decisions, so how do we actually decide? This article discusses what a decision model is, the types of decision models, and how analyzing decision execution models helps us understand their strengths and weaknesses, choose suitable models, and make efficient decisions.
Definition : Decision model – a flowchart of the core steps from problem discovery to decision making.
Core steps : indispensable steps.
First, we discuss our first decision model, which we call the Decision Execution Model (Part 1).
We could have drawn this as a program flowchart, but the symbols are not yet defined, so we will later present the model as a diagram, which will be more rigorous and easier for computers to execute.
When a problem arises, we first intuitively estimate how long its solution will take and how many stages it involves. Based on the total time, we allocate time to each stage. After the time for a stage passes, we reach a time node and start the next stage, continuing until all stages are completed and the total time is used up.
This can be considered an ideal model because it assumes each stage finishes exactly at the allotted time—neither early nor late—and the total time matches the completion of the task. More detailed description:
The task is completed exactly within the first allocated time.
As soon as one task finishes, the next task starts and is completed within its allocated time.
This looks a lot like the first two steps of mathematical induction.
However, this situation is almost impossible because the two assumptions rarely hold. First, we cannot guarantee that each task finishes exactly on time; it may finish early or late, as execution does not always go as smoothly as imagined. Second, due to human emotions, people may not shift attention exactly at the time nodes, unlike a robot; for a normal person this is difficult.
Nevertheless, this model is insightful; it reflects how many people think when making decisions. Although it often fails, people keep using it. Moreover, many decision execution models are improvements based on this model, making it a parent model for others.
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".
How this landed with the community
Was this worth your time?
0 Comments
Thoughtful readers leave field notes, pushback, and hard-won operational detail here.