Constructing what you are working on essentially involves creating a tree data structure. My recommendation would be to construct it using basic node elements that can hold recursive references to child objects of the same type. The entire tree can be built using just this one type of object, where the main object is known as the root object, from which all users begin their selections. Users begin at the root and navigate through child nodes until they reach a leaf node with no children, meaning there are no further choices to be made.
All nodes follow this pattern:
{
"label": "Choice A",
"question": "Choose subselection?",
"children": [ (an array of similar objects) ]
}
The label represents the name or label for the option, question corresponds to the next question users need to answer to select the next child node, and children consists of more instances of nodes of the same type, where each child's label provides a response possible to this node's question.
For example, consider the following selection tree scenario found in an imaginary online retail store that specializes in two types of products: clothing and food. Within the clothing category, customers can browse jeans, t-shirts, and hoodies of various colors, along with a limited selection of hats. The food section offers options like ramen noodles, assorted flavors of soda, and bananas. In the clothing category, customers have the choice to get a TV show or rock band logo printed on their black t-shirt, or opt for a plain black tee without any logos. Additionally, the TV show logo can also be featured on a blue hoodie, or customers may choose a plain blue hoodie instead.
Thus, the decision-making tree of answer/question pairs presented to customers looks like this:
A: <start>
Q: What product category?
|
|--A: Clothes
| Q: What type of clothing?
| |
| |--A: Jeans
| | Q: Color of jeans?
| | |
| | |--A: Blue
| | | Q: <end>
| | |
| | |--A: Black
| | | Q: <end>
| | |
| | \--A: White
| | Q: <end>
| |
| |--A: Shirt
| | Q: Type of shirt?
| | |
| | |--A: T-shirt
| | | Q: Color of T-shirt?
| | | |
| | | |--A: Red
| | | | Q: <end>
| | | |
| | | |--A: Green
| | | | Q: <end>
| | | ...
...
To simplify even further, we can represent this tree structure in JSON format by mapping each node accordingly. The starting question (<start>) identifying the product category—for instance, clothes or foods—may have its label set to null since it doesn't serve as a direct answer to a preceding question. This initial question acts as the foundation for the entire structure, with all other nodes stemming from it. Leaf nodes, representing endpoints where no further decisions are required (<end>), are marked by having null in place of the question field and the absence of a children array. All intermediate nodes include a label string (representing the top-level question answered by the node), the subsequent question, and an array of potential answers:
{
"label": null,
"question": "What product category?",
"children": [
{
"label": "Clothes",
"question": "What type of clothing?",
"children": [
{
"label": "Jeans",
...
},
...
]
},
{
"label": "Food",
"question": "Type of food?",
"children": [
{
"label": "Ramen noodles",
...
}
...
]
}
]
}
This JSON representation adheres to the proper syntax standards, allowing you to easily visualize the structure level-by-level by utilizing tools such as JSONtree. Ultimately, the key takeaway here is how a potentially intricate system of user selections and pathways can be elegantly constructed using a straightforward core data structure comprised of nodes and leaves. The complexity arises not from the structure itself, but rather from the relationships established between the nodes.