How can we bridge theory and experimental science?
Some scientists focus on developing models that can be tested to see if they can predict new observations. Others focus on collecting careful data which becomes the evidence to understand our world. Sometimes, these two worlds clash, like the vacuum catastrophe where the current state of theory and experiment could hardly be further apart. To improve our knowledge of the world, we have to use a mix of methods suited to the problem. The Meta-Problem Method can help us choose what’s next.
Complex problems are often vague and have many possible solutions. The Meta-Problem Method may lead you far away from the dilemma that started your quest. That’s because the method forces you to clarify what you really want and what you are willing to give up. It enables you to compare objectively the possible pathways and their trade offs. It prevents you locking into solutions mode too early and then doubling down on solving a low-yield problem that does not serve your goals as well as the alternatives. At the end of this process, you will have a better understanding of your priorities and how to achieve them.
Steps in the Meta-Problem Method
Dilemma
The high-level issue you are trying to address
Choose the right balance of theory and experimentation (for any given problem).
Goal
The changes you want to make to address the dilemma. There are usually many options.
Supporting Goals
- More data is collected about how the world works.
- New hypotheses are proposed which may be useful.
Other goals could include the development of scientific theories, minimizing the cost of discovery, and maximizing how useful the results are.
Problem Space
The set of problems you could chose to solve to advance your goals, plus the constraints that hold you back.
Example problems
- How can we collect more data about how the world works? Maybe the problem to solve is “What experiments will generate the most interesting results?”
- Which hypotheses are worth developing? Maybe the problem to solve is “Which scientific questions have models that don’t live up to the available data?”
There are many other potential problems to solve related to bridging theory and experimental science. Each goal has many possible problems we could link to it. Are there other problems linked to these first two goals? Which options come to mind for the other goals?
High-Yield Problems
Sometimes solving one problem helps make progress towards several goals. In this step, we identify these “two-for-the-price-of-one” problems.
Which options will advance more than one goal?
- Using expensive and unique equipment in as many creative ways as possible will generate new data about how the world works, can inform the development of scientific theories, reduces the marginal cost per experiment, and can generate new insights. However, experimental data is most useful in conjunction with models that explain what to expect.
- Developing models of scientific phenomena allows us to predict future observations, guiding future experimental research, contributing to scientific theories, and often costs less than physical experiments. However, without data to test the models, there is no way to know how valid they are.
There are many potential solutions that will have varying effects on the set of goals. Which alternatives improve the most important goals? How might the unknown change the right path forward? What other possible solutions are there to address the dilemma?
Problem Selection
Which of the many possible options in the high-yield problem step is the best set to address the dilemma?
- Which solutions make the most sense when investing in science?
- Which solutions will best address the dilemma?
- Which solutions will deliver the best outcome for the least amount of time, effort and money?
Implement, Learn and Adapt
Check continuously that you are still solving the best problem, as new information emerges.
Observe and learn as you go. As new information reveals itself, check continuously that you’re still solving the right problem.