Variables in mathematical decision models: Most quantitative models used for decision support include which categories of variables as core elements?

Introduction / Context:
Decision models structure problems using variables. Understanding these categories clarifies what the decision maker can set, what the environment imposes, and what outcomes result from those choices.

Given Data / Assumptions:

• Decision variables are the controllable choices (for example, price, order quantity).
• Uncontrollable variables are exogenous inputs (for example, demand, lead time).
• Dependent variables are outputs determined by the model relationships (for example, profit, service level).

Concept / Approach:
Models relate these variable types through equations or logical constraints. The aim is to select decision variables to optimize an objective, given uncontrollable variables and constraints, producing dependent outcomes for evaluation.

Step-by-Step Solution:
Classify variables by control: controllable vs. exogenous.Identify outputs that depend on inputs and decisions.Recognize that all three categories are standard components.

Verification / Alternative check:
Linear programming, inventory models, and simulation all use this taxonomy: decision (x), parameters (exogenous), and responses (outcomes).

Why Other Options Are Wrong:
Each single category is necessary but incomplete; only “All of the above” captures the full structure.

Common Pitfalls:
Confusing parameters with decisions; treating stochastic inputs as controllable when they are not.

Final Answer:
All of the above