Which strategy focuses on breaking a large problem down into smaller, easier-to-solve problems?

The strategy that emphasizes breaking a large problem down into smaller, more manageable components is referred to as decomposition and/or subgoaling. This approach allows individuals or teams to simplify complex issues by identifying specific, smaller tasks or sub-problems that are more straightforward to tackle. By addressing these smaller tasks sequentially, one can gradually build solutions to the larger problem. This method is highly effective in engineering and problem-solving contexts, as it helps to clarify the overall process and makes it more approachable.

For example, in a project design, decomposing the overall objective into phases such as research, design, prototyping, and testing allows for focused efforts on each individual component, facilitating easier tracking of progress and resource allocation.

Other strategies, while useful in their own contexts, do not specifically aim at breaking down problems in this manner. Means-end analysis typically involves identifying the end goal and figuring out the means to reach that goal by evaluating possible actions. Searching a knowledge space usually refers to exploring information or various options without necessarily restructuring them into simpler tasks. Recast by analogy involves applying knowledge or solutions from one context to another, rather than segmenting the problem itself. Thus, decomposition and/or subgoaling stands out as the most effective strategy for simplifying complex issues by addressing