Solving complex problems requires, among other things, gathering information, interpreting it, and drawing conclusions. Doing so, it is easy to tend to operate on the assumption that the more information, the better. However, we would be better advised to favor quality over quantity, leaving out peripheral information to focus on the critical one.
Our approach to information should be straightforward; after all, it feels that any data related to the problem that we are trying to solve should be beneficial. But, in practice, this isn’t the case. More information is not necessarily better. In fact, gathering more data is time consuming; it may provide you with unwarranted confidence (Oskamp, 1965), (Son & Kornell, 2010), (Bastardi & Shafir, 1998); and it dilutes the diagnosticity of other information items (Arkes & Kajdasz, 2011). More information is not necessarily better.
- Adopt a top-down approach to gathering information.
That is, for the bulk of your effort, once you have clarified the question that you want to answer (or the hypothesis that you want to test), identify which information you need to obtain to help you answer it. Only then should you look for that information, obtain it, and process it. This information should have a high diagnosticity; that is, the likelihood of observing it will be significantly different depending on whether the hypothesis is right or wrong. This is only advisable to an extent, because if you follow this approach blindly, you might not see important information that you might encounter by chance during your analysis. Therefore, you should retain some flexibility in your approach and embrace serendipity.
- Transcend “that’s interesting” by understanding the “so what?” of each item of information.
“That’s interesting,” by itself, isn’t really flattering. Isn’t it what you replied the last time a friend asked you your opinion about that painting of theirs that you didn’t really like? In the context of problem solving, forcing you to understand why you think something is interesting (or not) and formulating it, ideally in writing, forces you to analyze it in depth and be accountable for your thinking.
- Create an environment where important data sticks out.
If you have managed to follow some of the recent political “debates” for the primaries of the US presidential elections, you have seen firsthand how to create an environment that doesn’tsupport the exchange and analysis of ideas: talk over one another, call each other names, deflect the conversation toward peripheral or non-critical aspects.
Contrast this with how members of the Manhattan Project team were working (as related by Physics Nobel Laureate Richard Feynman (1997)) when deciding how they were going to separate uranium to extract the fissionable isotope:
In these discussions one man would make a point. Then [Arthur] Compton, for example, would explain a different point of view. He would say it should be this way, and he would be perfectly right. Another guy would say, well, maybe, but there’s this other possibility we have to consider against it.
I’m jumping! Compton should say it again! So everybody is disagreeing, all around the table. Finally, at the end, [Richard] Tolman, who’s the chairman, would say, “Well, having heard all these arguments, I guess it’s true that Compton’s argument is the best of all, and now we have to go ahead.”
It was such a shock to me to see that a committee of men could present a whole lot of ideas, each one thinking of a new facet, while remembering what the other fellow said, so that, at the end, the decision is made as to which idea was the best—summing it all up without having to say it three times. So that was a shock. These were very great men indeed.
Integrating these ideas, evidence gathering requires you to continuously oscillate between analyzing minute aspects of your problem and stepping back to understand their implications on the big picture. That latter part can easily be lost in the details after weeks or months of diving deep into one aspect of your problem. Yet it remains a critical component of successful problem-solving and deserves your continuous attention.
Featured image credit: Supernova SN2014J (NASA, Chandra, 08/22/14), from NASA’s Marshall Space Flight Center via Flickr.