In the context of marginal analysis, when is separable programming most applicable?

• A. When marginal returns are constant
• B. When there are increasing or decreasing marginal returns
• C. Only when marginal returns are increasing
• D. Only when marginal returns are decreasing

Answer: (B)

Verified step by step guidance

1. Understand the concept of separable programming: it is a mathematical optimization technique used when a problem can be broken down into smaller, independent subproblems that can be solved separately and then combined.
2. Recall that marginal analysis involves examining how changes in input affect output, particularly focusing on marginal returns, which can be constant, increasing, or decreasing.
3. Recognize that separable programming is most useful when the objective function or constraints exhibit non-linear behavior, such as increasing or decreasing marginal returns, because it allows these non-linearities to be handled piecewise.
4. Note that if marginal returns were constant, the problem would be linear and simpler methods could be used, so separable programming is not specifically needed in that case.
5. Conclude that separable programming is most applicable when there are increasing or decreasing marginal returns, as it effectively manages the complexity arising from these non-linear marginal returns.