Anatomy of Problem Solving (1)

TL’DR: Finding a solution to a problem is something we do countless times per day. Time to spend some thoughts on it.

After writing my last post (RESTwars (2): Some thoughts on goals), I have been asked about the difference between goal and requirement. This is a very common question. But even though it can be quite easily answered on its own, I would like to take the opportunity to answer the question in its right context.

This post will be about the anatomy of problem solving. It will cover the definition of the most important terms and their relationships. In a second post, I will continue the topic and talk about the actual problem solving techniques.

Hint: This post is not part of the RESTwars series and can be read without knowledge of those posts.

Defining The Terms

Let me start with a bold thesis: Human behaviour is problem driven. Everything we say or do has its roots in some kind of problem. To support this thesis, I need to define some terms first.

What Is A Problem?

A problem is the mismatch between a goal and the status quo.

Let me give you an example: Alice wants to be famous, but she is not. For Alice, that states a problem. If Alice did not want to be famous, her not being famous would not be a problem. Also, if she actually was famous, that would be alright as long as she wanted to be.

So far, this is quite obvious. But we can gain a very important information from that definition: A goal and the status quo are both states. Take a moment to think about that.

What Is A Solution?

If the status quo and the goal are both states and the problem is the difference between those states, that makes the solution the transistion from one state (as is) to another (to be).

Problem and Solution

Solution is an unbiased term. A solution can be good or bad, cheap or expensive, simple or complicated. But there is one thing a solution cannot be: without any alternative. When you hear the word “solution”, the word “alternative” should immediately pop up in your head. If someone claims that a solution has no alternative, he has clearly not understood what a solution is. There are virtually infinite possibilities to translate the status-quo into your goal. Some might be hard. Some might be inconvenient. And some might be plain stupid. But they are all alternatives. So stating that “there are no alternatives” either translates to “I know this solution sounds bad, but I did not find a better one.” or is the euphemistic equivalent for “Because I say so!”.

Admittedly, the hard part is to find a solution which does not only master the transition from the status quo to the goal but also matches as many of the characteristics imposed on it by its environment as possible.

These characteristics are called requirements.

So, while the goal is a state, which is part of the problem, the requirements are characteristics which describe the solution.

Alice, who still wants to be famous, might find that becoming a politician is not the right solution for her. It is simply too boring. Also, that would take years and she wants to be famous right now! That means that she can also cross Nobel-Prize winning scientist off the list. So Alice starts thinking about casting shows. She has recently been thrown out of the school choir, because someone threatend the school with an aural-assault charge after her last performance. But she is astonishingly beautiful. So she signs up for “Next Turbo Model 3000”.

While Alice’s goal is “I wanna be, like, totez famous!”, her requirements are “without waitn foreva!” and “like, no singing, duh!”. So far, her solution aligns with her goal and requirements.

Since problem and solution are both defined using Status Quo and Goal, it cannot hurt to take a closer look at those terms, too.

What Is The Status Quo?

Until now, we have seen the status quo as the beginning state in our solution. It has been used to describe the problem and is in that way biased:

Bob might find that his computer is slow. If he tells his friend Carol his acquisition of the status quo, he does not even need to state his problem. She will know from his description that the goal is a fast computer. It is part of the status quo description.

There is a lot of fuss regarding this phenomenon. Some say that a current state analysis has to be formulated neutrally1. Others say that it can be nearly impossible to describe all aspects relevant to the problem without knowing and therefore implying the problem. Also, the more accurately the aspects of a situation are analyzed, the more time consuming the analysis will be2. That means that thanks to Heraclit, the situation might change while we analyze it and thus our extra-accurate analysis is stillborn. Well done, old dude!

Be that as it may, there is something a lot more valuable we can learn from the status quo. A state is the outcome of a transition. So the goal is (hopefully) the outcome of our solution. But the same applies to our status quo. Something lead to Bobs computer being slow. Maybe Bob installed a lot of software. Or he has malware on his system. Or his computer simply grew old.

To find the right solution, it helps to not only take a look at the current state, but also to know how the current state came to be. It is therefore crucial for understanding a problem to know how it came into existence in the first place.

What Is A Goal?

We defined our goal as the to be state before. But even if you had not just read about it, it might sound like a silly question. Everyone knows what goal means. If you teach the meaning of goal to a little child, you might describe it as “something you want”. When you want to get a piece of candy, it is your goal to get a piece of candy.

Still, as I wrote before, there is a lot more to it. Our goals and the problems they induce are the driving force behind the decisions we make.

Doing something involves a certain amount of effort. Usually, we need some kind of incentive in order to invest effort into something. If an incentive works as a solution for a problem, it may be proper cause for us to invest effort into it (Freakonomics3 is a pretty entertaining read on the principle of incentives).

Let us take a look at a simple example: Dave has a family. He wants to be able to support his family and this is one of his highest goals. Unfortunately, his current job does not pay enough to support his family. Because his goal does not match the current state, that introduces a problem. Taking a well-paid job offers enough money to support his family. The money offered by this job is an incentive, which works as a solution to his problem. Since incentive and goal align, Dave is willing to invest a fair amount of his time into the job, in order to support his family. If the job comes with a lot of risks (e.g. working on an oil rig), Dave might not be able to support his family in the long term. Depending on Dave’s foresight, this adds a negative incentive. Since the incentive does no longer work as a solution to Dave’s problem, he might look for a different job.

“Well,” you might say, “what if I do something just for the fun of it?” Then your goal is to have more fun. You could be bored (status quo) and want to be entertained (goal). Of course, this thesis only applies for things we do of our own free will. If Dave goes to jail because he decided to rob a store instead, his action (going to jail) does not really align with his goal (support his family) anymore. But since he decided to rob the store, he did not have the foresight we assumed he had. His solution was therefore unsuitable for his problem in the first place.

I wrote a post on goals not long ago (RESTwars (2): Some thoughts on goals). If your goal is to learn more about goals, clicking the link is an excellent solution.

Where Do We Go From Here?

Now that we know and understand the basic terms, we can find out how to find the right solution to a problem. But that is a topic for another post (link follows).

  1. Cf. Chriss Rupp et al.: Requirements-Engineering und Management (5th Edition, 2009): p. 63 [return]
  2. Cf. Reinhard Haberfellner et al.: Systems Engineering (12th Edition, 2012), p.82 ff. [return]
  3. Steven D. Levitt, Stephen J. Dubner: Freakonomics (2009) [return]