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How can Engineers think better with Scientists to solve “Hard” Problems?

Updated: Sep 2

Start with asking the right question – this is No. 1 in a series of 3 articles.....


Introduction

Last week, I lead a webinar for Engineers Ireland on how engineers can think better with scientists to solve “hard” problems. Here is a summary in case you missed it -

“Hard” problems look a lot like this Escher picture. It is a struggle to pick out any concrete elements – is it a bird, is it a plane? with many shades of grey – so which facts are most relevant and why? It is tough to frame our task as we lack the regular patterns of a chess game with lots of possible roads, yet many leading nowhere. Welcome to the world of hard problems and the foremost soft skill we need is to ask the right questions. Below are my Top 6:

1. “What else?”

Science offers a powerful tool to deal with “hard” problems. With methods and repeatable procedures, we can test observables and make fine distinctions. However, that is helpful once we know what we are looking at. Empiricism can actually limit us if we only focus on what we are measuring right now. To avoid WYSIATI bias (“What You See Is All There Is), ask “what else could this be?”. As Mark Twain said, “it’s not what we don’t know that gets us in trouble but what we do know” so be open to considering other possibilities.

2. “Who else?”

One way of discovering “what else?” is asking “who else?”. Engineers are often cognitively central in solving problems, integrating all the information that we already know – at least, all that we think we know so far. Involve people who don’t know this information and not directly involved. Those on the periphery aren’t bogged down in the detail and ask us “obvious” questions that might at least open up some possible roads that we thought were cul-de-sacs. Even people in a different field may see parallels that are analogies for your hard problem and provide unexpected clues. Research on innovation shows that this is where breakthroughs are often likely to emerge.

3. “What next?”

You need to be a bit of a time traveller to deal with the risk inherent within “hard” problems and a challenge in solving them well. Imagine as quantum mechanics allows for, that your problem is in three different states at once. The precedents are important as your solution should sit well not just technically but with rationale you have used in the past. Even more importantly, you need to future-map the consequences of your actions. A problem solution that gives you an excellent technical outcome 80% of the time is not attractive if 20% of the time, the implications for your customer or patient are irrecoverable or difficult to predict.

4. “How do we know?”

Hold your assumptions up to this test – is the information you have based them on reliable? Ask “says who?”. Have you teased out your hypothesis well or are you jumping too quickly to a conclusion even in how you define the problem? Often, we only seek that which confirms what we hope to find. Instead, use the “falsification principle” of science and try to disprove your assumptions. Contradictory findings can be really useful if they reveal even one unique insight into a problem – like why are the fields in this picture mutating? One less moving part is helpful.


5. “Why me?”

Sometimes solving hard problems is stressful and even overwhelming. The most important thing is to remember is that you don’t need to know everything to succeed. In fact, knowing the limits of your knowledge (and others) might be crucial. Twain was only half right as what we don’t know can get us into lots of trouble but doesn’t have to. Like a scientist, classify and categorise your “unknown’s” by the risk they present including : 1. “Unknown–knowns” (you know a thing exists but not yet its possible impact), 2. “Known–unknowns (you know it will have an impact but the data you need is not currently available to characterize the thing itself). Concentrate on converting 2.’s into 1.’s and our 1.’s into “known known’s”. Remember, the more honest you are about what you don’t know, the better.


6. “What’s not?”

When really flummoxed, it works well to use inverted thinking to achieve clarity on what (hopefully) we are not dealing with. A worst-case approach can establish a safe boundary, confirming what won’t result at least. Be careful to note why you have eliminated any worst-case outputs. This is the “precautionary approach” of science in action and it serves us well where these possible outcomes have potentially significant adverse effects. As these feedback loops are so heavy, we should focus on how to ensure these are clear and short. Inversion is an ancient thinking approach, originating with the Stoics. Thinking as a philosopher is like being given a compass when you’re trying to find routes (and root-causes) within mist-covered terrain.

So that’s the good news – even “hard” problems can be solved. The bad news is that we may not just have hard problems to tackle in the future but “wicked” ones too. If you want to know more about the Deliverable's page on this site and sign up to my newsletter on my contact page for more tips like these. T hanks for reading.


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