There is a problem-solving method that has been repeatedly validated in practice. First study the known methods for solving this type of problem. Then try one. Observe the results. If it does not work, adjust the approach and try again. Keep iterating until the problem is solved.
This process has corresponding theoretical frameworks in multiple academic fields. The most classic is the PDCA cycle: Plan, Do, Check, Act. The process described above is exactly a standard PDCA closed loop: researching methods is Plan, trying them is Do, identifying problems is Check, adjusting the approach is Act.
In engineering, this corresponds to iterative development: build a framework first, get it running, identify where it breaks, fix it, and keep going. The reason you cannot get it right in one pass is that human cognition islimited. You cannot anticipate all problems at the design stage. The logic of iteration is: small steps, fastvalidation, continuous improvement.
In the scientific method, it corresponds to hypothesis testing: propose a hypothesis, design an experiment to validate it, and if the results do not support the hypothesis,refine it. This is not random trial and error. It is trial and error with direction,documentation, and analysis.
Together, these three frameworks form a complete problem-solvingclosed-loop: exhaust the methods,rapid trial and error, identify problems, optimise and iterate, solve the problem. This process can be applied to almost every domain, from technical development to business decisions to personal learning.
The unique value of a cross-disciplinary perspective is that the same method has different names in different fields, but the underlying logical structure is the same. Once you realise this, you cantransplant a methodology from one domain to another, achieving a cognitive leverage effect.