Hi all,
Let’s wrap up the riff off I started earlier in the week for Ken Watanabe’s Problem Solving 101.
5. Improvement means breaking down an issue until certain root causes are determined before building on the specified problem areas. For example, instead of looking at an overall average, it means looking at the components of the average to identify the biggest problem areas.
This thought addresses the difference between an aggregate statistic and the components of the aggregate. When an average goes down, it could mean that every part of it went down in tandem. It could also mean that all the components held steady while one went into freefall.
Speaking more broadly, aggregate measures hide certain truths that are blatantly obvious when examined at the component level. In the early days of TOA, I wrote this post about Simpson’s Paradox, a statistical oddity that gets at this idea in a slightly different way.
4. Sometimes constraints can help pick a solution out of a group. Think of a pepper shaker. How can we increase the amount of pepper that comes out? There are many options. We can increase the size of the top area, add more holes, increase the size of each hole, or reduce the size of the grains.
The constraint referenced here would come about if we were only allowed to consider certain types of changes. If we were told the size of the grains could not be changed, for instance, and that the overall size of the top area had to remain fixed, then our options would be to add holes or change the size of each hole. I think most people are comfortable making decisions within constraints because in these cases the best option almost picks itself.
The important extension to the idea comes when you think about altering the constraints. What if in the above example it was twice as valuable to increase the overall size of the top area? It might be easier to simply add more holes but this choice would have to be compared against the benefit of taking the extra step to alter the constraint and make the other options viable.
3. Setting a goal means clarifying as many details as possible. Saying ‘I want a computer’ isn’t as good as saying ‘I want a $500 model X computer produced by company Y’.
I think this is an important reminder because it is such an easy concept to overlook. It almost always feels better to just get started on something with basic guidelines rather than taking the extra mental effort to clarify as many of the details as possible. But I think in most cases the initial burst of effort pays off down the line.
When I was out of work for two years, I wasted a lot of time early on by not being specific enough about the details. This led me to job offers that, in hindsight, I would never have accepted. I finally got around to clarifying exactly what I was looking for and how I would measure these factors when considering an offer. This allowed me to filter out jobs that I would eventually reject and saved me a lot of time during the latter portion of my search.
2. Great teams don’t automatically mean great growth environments. Teams must work to create such places. In most teams, high performers prevent others from doing the developmental work they need to do in order to cultivate their skills.
This thought could have been its own book. It’s intuitive to think a strong organization or winning team would be the ideal place to learn and grow but it isn’t always guaranteed to work this way. Although everyone accepts the necessity of learning ‘on the job’, every employer’s tolerance for a learning curve is different. The tolerance for errors and ‘growing pains’ dictates how much an employee can learn more so than the success of the organization. The best growth environment aligns developmental work with the goals of the organization or team so that growth is not sacrificed for the sake of meeting an organization’s goals or commitments.
1. It’s forty degrees and I feel like I’m dying…
Again, the theme of thoroughness emerges in this note. A person could die for many reasons but simply citing the temperature isn’t very helpful. The unit of measurement also matters here – if we are talking Celsius, a good problem solver would immediately think of heatstroke, but Fahrenheit would probably lead to additional questions.
0. Is that really from this book???
OK, fine, that’s not from this book, it’s from Courtney Barnett’s ‘Avant Gardener’.
But who better to end a riff off?
Thanks for reading, folks.
Tim
Showing posts with label books - problem solving 101. Show all posts
Showing posts with label books - problem solving 101. Show all posts
Sunday, February 10, 2019
Tuesday, February 5, 2019
reading review - problem solving 101 (riff offs, part 1)
Problem Solving 101 by Ken Watanabe (September 2018)
Problem Solving 101 is a simple, short, and highly popular introduction to the basic process of problem solving. Watanabe’s target audience isbusiness bros kids and his book is filled with the basic examples, cute graphics, and neat conclusions that appeal to business bros kids. Despite being the definition of the target audience outside the target audience, I enjoyed the book and would recommend it to anyone who is interested in a quick refresher on the subject.
I always have a difficult time doing full reading reviews for books like this one. I suspect there are two reasons. First, the book is short and I therefore took only a few notes. Second, the book focuses heavily on examples and this led me to write my notes in a generalized way that allows them to stand on their own without being put in the context of the book.
So, instead of the usual reading review I’ll just riff off the ideas I noted down. Unlike in the past, though, I took a moment to rank these thoughts in reverse order of my preference. We’ll do a few today and come back with the rest later in the week.
Good luck, reader.
Tim
10. The steps of problem solving: (i) understand the situation, (ii) identify the root cause, (iii) develop an action plan, and (iv) execute until the problem is solved, making modifications as needed.
Like I mentioned, this book is very simple…
This note highlights an important feature of good instructional books – they assume nothing about the reader. The result is a very thorough dissection of a skill most people probably already feel they do fairly well. Readers who come into this type of book expecting to learn something new on every page misunderstand the purpose. Rather, what a book like Problem Solving 101 does by its thoroughness is help a reader pick out one or two details to tweak in order to achieve better performance. Most readers will consider this book a series of reminders rather than a mind-blowing instruction manual.
9. Problem solving is a habit that combines certain skills with the right attitude.
This is the thesis statement for the book. From the list in note #10 above, the skills involve (ii) and (iii) while the attitude covers (i) and (iv). To put it another way, the right skills mean knowing how to analyze a root cause and develop a plan while the right attitude means asking questions until you understand the situation and confirming the problem is solved after execution.
8. Asking a series of yes/no questions can help create a list of possible ways to solve a problem. With the right series of questions, it is possible to place every option into a certain category.
This is a good example of the book’s preference for thoroughness. Going through every possibility until you’ve listed all the possible options isn’t necessary for solving every problem, of course, but it is probably the most important thing to know how to do for solving the most difficult problems. I suggest practicing the approach whenever possible so you can be ready to employ the method when you need it.
Most people who informally think of ways to solve a problem call it ‘brainstorming’. This works just fine for most problems. I would recommend simply ‘brainstorming’ whenever everyone involved has a good sense of all the available options. However, Watanabe’s approach of asking yes/no questions is recommended when there are unknowns about the choices because the process of asking the questions forces decision makers to clarify the details involved in the decision.
7. Collecting information and performing analysis just for its own sake is a common trap. Make sure such activity is done to answer a specific question or to achieve a specific goal.
This is the most important thought in the book. We live at a time when information is more easily accessible than at any other point in human history. The temptation is to take all the data, look at it with an unbiased point of view, and ‘let the data do the talking’ (1). Unfortunately, conclusions derived in this manner often fail the strict validations demanded by the field of mathematics or statistics.
Instead, I recommend asking a specific question and considering how information might help answer the question before performing the analysis. This approach might require more work. However, the benefit of the approach is that you will be able to accept the results just as they are and safely move on to the next step of the analysis.
6. A hypothesis must be tested to know the magnitude of a given input on a result. A given input might improve an outcome by 10% but alternate methods could achieve much more.
I’ll try to clarify this sloppy note with an example. Suppose you commute the same way to work every day and that your total time is thirty minutes. You think this is the fastest way. But how do you know for sure? The answer is to form a hypothesis then test it. Maybe you cross the street at the same intersection every day – why not cross at a different intersection for a week? If your commute for the week drops down to twenty-five minutes as a result, well, you might be on to something there, reader.
The point of the note is that testing a series of hypotheses can help clarify what is really going on in almost any situation. The process of properly testing means determining how you would prove or disprove the hypothesis before conducting an experiment under unbiased conditions (2). This process is helpful not just for problem solving but also for ongoing process improvement because it requires the same kind of mentality – if my hypothesis were true, how would I know?
Footnotes / a 24 reference? / hypothesis testing 101
1. But what if the data knew where a bomb was about to go off in an hour?
A metaphor I’ve heard more than once regarding analysis of ‘big data’ compares the process to torture – you just keep poking and proddingand putting your cigarette out on the data until it tells you the truth. My response is always – how come torture is the best comparison we can come up with? Shouldn't we be suspicious of any result derived from torture?
2. One more thought on hypotheses…
Another note I took down brings additional clarity to the ideas I discussed in point #6 above:
It’s helpful to know if your hypothesis is taking on a grouping structure or an argument structure. In a grouping structure, components of the argument are independent of each other and refuting one does not necessarily crush the argument. In an argument structure, each pieces builds on a neighbor and refuting one can break the entire chain.
This thought further emphasizes the important of understanding exactly how to refute the hypothesis before starting the experiment – if this step is ignored, you run the risk of misinterpreting the results.
Problem Solving 101 is a simple, short, and highly popular introduction to the basic process of problem solving. Watanabe’s target audience is
I always have a difficult time doing full reading reviews for books like this one. I suspect there are two reasons. First, the book is short and I therefore took only a few notes. Second, the book focuses heavily on examples and this led me to write my notes in a generalized way that allows them to stand on their own without being put in the context of the book.
So, instead of the usual reading review I’ll just riff off the ideas I noted down. Unlike in the past, though, I took a moment to rank these thoughts in reverse order of my preference. We’ll do a few today and come back with the rest later in the week.
Good luck, reader.
Tim
10. The steps of problem solving: (i) understand the situation, (ii) identify the root cause, (iii) develop an action plan, and (iv) execute until the problem is solved, making modifications as needed.
Like I mentioned, this book is very simple…
This note highlights an important feature of good instructional books – they assume nothing about the reader. The result is a very thorough dissection of a skill most people probably already feel they do fairly well. Readers who come into this type of book expecting to learn something new on every page misunderstand the purpose. Rather, what a book like Problem Solving 101 does by its thoroughness is help a reader pick out one or two details to tweak in order to achieve better performance. Most readers will consider this book a series of reminders rather than a mind-blowing instruction manual.
9. Problem solving is a habit that combines certain skills with the right attitude.
This is the thesis statement for the book. From the list in note #10 above, the skills involve (ii) and (iii) while the attitude covers (i) and (iv). To put it another way, the right skills mean knowing how to analyze a root cause and develop a plan while the right attitude means asking questions until you understand the situation and confirming the problem is solved after execution.
8. Asking a series of yes/no questions can help create a list of possible ways to solve a problem. With the right series of questions, it is possible to place every option into a certain category.
This is a good example of the book’s preference for thoroughness. Going through every possibility until you’ve listed all the possible options isn’t necessary for solving every problem, of course, but it is probably the most important thing to know how to do for solving the most difficult problems. I suggest practicing the approach whenever possible so you can be ready to employ the method when you need it.
Most people who informally think of ways to solve a problem call it ‘brainstorming’. This works just fine for most problems. I would recommend simply ‘brainstorming’ whenever everyone involved has a good sense of all the available options. However, Watanabe’s approach of asking yes/no questions is recommended when there are unknowns about the choices because the process of asking the questions forces decision makers to clarify the details involved in the decision.
7. Collecting information and performing analysis just for its own sake is a common trap. Make sure such activity is done to answer a specific question or to achieve a specific goal.
This is the most important thought in the book. We live at a time when information is more easily accessible than at any other point in human history. The temptation is to take all the data, look at it with an unbiased point of view, and ‘let the data do the talking’ (1). Unfortunately, conclusions derived in this manner often fail the strict validations demanded by the field of mathematics or statistics.
Instead, I recommend asking a specific question and considering how information might help answer the question before performing the analysis. This approach might require more work. However, the benefit of the approach is that you will be able to accept the results just as they are and safely move on to the next step of the analysis.
6. A hypothesis must be tested to know the magnitude of a given input on a result. A given input might improve an outcome by 10% but alternate methods could achieve much more.
I’ll try to clarify this sloppy note with an example. Suppose you commute the same way to work every day and that your total time is thirty minutes. You think this is the fastest way. But how do you know for sure? The answer is to form a hypothesis then test it. Maybe you cross the street at the same intersection every day – why not cross at a different intersection for a week? If your commute for the week drops down to twenty-five minutes as a result, well, you might be on to something there, reader.
The point of the note is that testing a series of hypotheses can help clarify what is really going on in almost any situation. The process of properly testing means determining how you would prove or disprove the hypothesis before conducting an experiment under unbiased conditions (2). This process is helpful not just for problem solving but also for ongoing process improvement because it requires the same kind of mentality – if my hypothesis were true, how would I know?
Footnotes / a 24 reference? / hypothesis testing 101
1. But what if the data knew where a bomb was about to go off in an hour?
A metaphor I’ve heard more than once regarding analysis of ‘big data’ compares the process to torture – you just keep poking and prodding
2. One more thought on hypotheses…
Another note I took down brings additional clarity to the ideas I discussed in point #6 above:
It’s helpful to know if your hypothesis is taking on a grouping structure or an argument structure. In a grouping structure, components of the argument are independent of each other and refuting one does not necessarily crush the argument. In an argument structure, each pieces builds on a neighbor and refuting one can break the entire chain.
This thought further emphasizes the important of understanding exactly how to refute the hypothesis before starting the experiment – if this step is ignored, you run the risk of misinterpreting the results.
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