Make your constraints explicit before solving

Write out every assumption and rule you are treating as fixed — most problem-solvers carry invisible constraints they never surface.

Why it works

Implicit constraints operate automatically; they shape the solution search without ever being consciously examined. Making them explicit moves them from procedural to declarative memory, where they can be evaluated and challenged. Research on representational change shows that insight is often triggered not by adding new information but by recognizing that an assumed constraint is not actually required by the problem.

How to do it

1. Write your problem statement at the top of a page.
2. For 5 minutes, list every rule, assumption, boundary, and "it must be this way" you are taking for granted.
3. For each, write: "Is this constraint stated explicitly in the problem, or am I assuming it?"
4. Circle every assumed constraint — those are your first candidates for relaxation.

Evidence

Representational change theory (Ohlsson, 1992) and Knoblich et al.’s (1999) experimental work on matchstick algebra problems showed that the difficulty of insight problems correlates with the tightness of the constraints subjects imposed on their representations. Knoblich et al. (1999) directly manipulated constraint tightness across matchstick-arithmetic problem types, providing the experimental basis for the claim that surfacing and relaxing an assumed constraint — rather than adding information — is what unlocks the solution. (observational)

Most experimental evidence uses controlled insight puzzles (matchsticks, remote associates); how much the effect size transfers to open-ended real-world problems is not precisely established.

Sources

• Knoblich et al. (1999), constraint relaxation and chunk decomposition in insight problem solving, Journal of Experimental Psychology: Learning, Memory, and Cognition
• Ohlsson (1992), Information-processing explanations of insight and related phenomena
• Knoblich, G., Ohlsson, S., Haider, H., & Rhenius, D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(6), 1534-1555.
• Ohlsson, S. (1992). Information-processing explanations of insight and related phenomena. In M. T. Keane & K. J. Gilhooly (Eds.), Advances in the Psychology of Thinking (Vol. 1, pp. 1-44). Hemel Hempstead, UK: Harvester Wheatsheaf.

Common mistake

Listing only practical constraints (time, money) and missing the deeper representational ones: what counts as a valid solution, who is allowed to be involved, what domain the solution must come from.