I. Define problem
Why is this step is the most important step?
Because defining the problem defines the solution. That is, the diagnosis determines the treatment (if you are diagnosed with the flu, you get a different treatment than if you are diagnosed with a cold).
Quotes illustrating the importance of this step:
- "A problem well-stated is a problem half solved."-- Charles Kettering
- " If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." --unknown, but often attributed to Albert Einstein
Examples of insights due to re-defining the problem
- Animal shelters asking "How can we help owners keep their pets?" rather than asking "How can we get all these abandoned pets adopted?"
- Behaviorists--and clever parents--asking "How can we encourage good behavior?" rather than "How can we stop bad behavior?"
- Defining the problem of saving gasoline (and the environment) by trying to increase our vehicles' gallons per mile rather than trying to increase our miles per gallon.
- People often ask "Would I like x?" when they should be asking "Would buying x make me happier than spending that money on something else--or than saving that money?"
B. 4 pitfalls in defining the problem.
1. Not defining the problem broadly enough, thereby narrowing your options. For example, when confronted with COVID-19, Trump apparently framed the problem as "Can we have a good economy OR fight COVID?"-- when he could have framed the problem as
"How can we have a good economy AND fight COVID?"
He has also been accused of asking
"How can we have a good economy by wrecking the environment?"-- when he could have asked "How can we have a good economy and improve the environment?"
Similarly, many people define problems in "all or none" terms, such as "Should we stay married or should we get a divorce?"Experience a simple example of defining the problem too narrowly: The nine-dot problem
Ways to avoid defining the problem too narrowly
A longer look at reframing the problem
2. Not finding the real source of the problem.
Two reasons we have difficulty with this step:
- Not taking responsibility for our role (Examples: "It's not my fault," "Look at what you made me do!", "You are making me mad", "That's a nasty question," ). One way to deal with this trap is suggested by Timothy Ferris: "...tell my story to myself from the perspective of a victim, then I tell the exact same story from a place of 100 percent responsibility."
- In our chaotic, complex world, Isolating the cause of an effect is difficult. Consequently, we may mistake effects or symptoms for causes. Furthermore, what we think is an important source of the problem may be unimportant (our ancestors would not believe that tiny things like viruses and bacteria could make us sick) whereas a factor that we think is unimportant may turn out to be very important. So, we may need to rely on scientists and experts to determine the most important causes of a problem. Many people refuse to do so; consequently, we have people arguing that cigarettes don't cause cancer and that humans are not contributing to global warming.
3. Not knowing what kind of problem we have, so we try to solve one type of problem when we should be trying to solve a different type of problem.
What rule is determining the sequence of these numbers? 8,5, 4, 9, 1, 6, 7, 10, 3, 2
The digits (eight, five, four, etc.) are in alphabetical order,
Two other examples: Think of the last time you applied the wrong formula to a word-problem or heard of a friend who was misdiagnosed by a doctor.
4. We may ignore the problem: "It's not a problem because I don't want it to be a problem." Examples:
Denying the smoking causes cancer.
Denying global warming.
Denying the threat caused by COVID-19.
II. Generate solutions
A. Using existing solutions:
1. Algorithms: a problem-solving strategy that--if all the steps are followed--is guaranteed to eventually lead to a solution.
Two problems with algorithms:
1. They involve many steps (and doing many steps takes time and uses up the limited space in short-term memory)
2. They only fit problems where there is one right answer. Thus, there are algorithms for solving some math problems and playing certain simple games like tic-tac-toe, but not for problems with human relationships.
2. Heuristics: a general rule that guides problem-solving, but does not guarantee a perfect solution. (Click here for a weather-related heuristic.) Examples of useful heuristics:
- Google it
- Ask "How have I solved similar problems?"
- Ask "How could I make the problem worse?" -- then do the opposite.
- Break the big problem into several smaller little problems.
- Ask a friend what to do.
- Have "SMART" (Specific, Measurable, Achievable, Relevant, Time-Bound) goals.
One type of heuristic: The representativeness heuristic:
a general rule used when people decide whether something is a typical case. If the target matches their memory of a typical instance, they will decide that the target is a typical case. We use the representativeness heuristic to classify people and things.
Examples of the representativeness heuristic: Determining whether someone is a child or an adult based on their appearance matching your memorized examples of children and adults.
Advantages: We can take advantage of our experience. For example, a doctor can quickly diagnose a patient who has a disease that the doctor has seen hundreds of times before.
- Problems may look similar, but be different. So, the representativeness heuristic may lead to stereotyping and overlooking key differences. Remember, "History doesn't repeat, but it may rhyme." (a quote often attributed to Mark Twain, but probably best attributed to John Robert Columbo)
- We may stick with an old solution when we could come up with a better solution.
B. Barriers to generating new solutions
Examples: "COVID will magically go away."
"COVID is a hoax."
"Global warming is a hoax."
2. Set: a rigidity in problem-solving due to wanting to continue to do things the old way.
3. STM's limits-- Because STM is limited, we can't think of many options at once and we can't think of all the pros and cons of a decision at once. One way to deal with this problem is to make a pro-cons table of the options. Another alternative is to have a computer program help you make a decision.
III. Evaluate alternatives ("For every problem, there is a solution that is simple, quick, and wrong" -- Paul Ylvisaker)
Why we "satisfice" (choose the first satisfactory option)
rather than "optimize" (choose the best [optimum] option)
What it takes to optimize:
1. Consider all the optionsTable illustrating complexity of making an optimal choice: An oversimplified example of choosing among apartments. Note that there are probably more than 3 places that you could consider and that you probably care about more than price, proximity to campus, and landlord. For example, you probably care about how quiet it is, how safe it is, how big it is, and how nice it is. However, even this oversimplified example shows you how complicated optimizing is.
2. Consider all the pros and cons of all the options
3. Determine the probabilities of each of those prose and cons
4. Correctly weight the importance of all those pros and cons
5. Combine all the information about the pros and cons of all the options to arrive at the best (optimal) choice
Options Price Score on Price Price's Importance Location Location's Score Location's Importance Landlord's Reputation Landlord's Score Landlord's Importance Total score 1 500/month 3 4 2 miles from campus 2 2 Excellent 5 4 36 (3 * 4) + (2 * 2) + (5 * 4) 2 400/month 4 4 5 miles from campus 1 2 Average 3 4 30 (4 * 4) + (1 * 2) + (3 * 4) 3 700/month 1 4 next to campus 5 2 Poor 1 4 18 (1 * 4) + (5 * 2) + (1 * 4)
Why we fail to optimize (besides the fact that optimizing is stressful):
- Because of the limits of STM, we do poorly at:
Considering all the options (there are too many to fit in STM)
Considering all the pros and cons of each option
Combining all that information
To get around some of the limits of short-term memory, you might just write down all your options as well as their pros and cons.
To get around more of the limits of short-term memory, you could use this decision making program to help you make decisions.
- Because we rely on the availability heuristic
(which should have been called the accessibility heuristic), we are bad at estimating the frequency of events. That is, we estimate how often something happens based on how easy it is to remember examples of that event occurring. The problem is that some events, even if they don't occur very often, are easy to recall (e.g., airplane crashes).
How politicians and some in the media have used the availability heuristic against us.
- In 2016, Trump ran on a vision of America being unsafe due to violent crime, but, in fact, America's violent crime rate was almost half of what it had been in 1990.
- Trump acted like cities near the Mexican border are extremely dangerous places, largely due to undocumented immigrants from Mexico. In fact, it seems that immigrants are less likely to commit crimes and that some southern border towns (e.g., El Paso) are among the safest cities in the country whereas cities far from the Mexican border (e.g., Baltimore and Detroit) are among the most dangerous U.S. cities.
- Trump has convinced some people that ANTIFA are a bunch of murderers. In fact, as of this writing, ANTIFA is responsible for only one death (and that may have been in self-defense). In general, right wing extremists are responsible for much more violence than the left-wing extremists. (link to more recent data).
- Some have argued that police are being gunned down at high rates and that COVID-19 is a hoax. However, recent figures show 101 police officers died from COVID-19 and 82 died from all other causes combined (e.g., car accidents, being shot, etc.).
- Police are about 3/4 as likely to die from a car crash as from a shooting; yet many officers do not wear seat belts.
- Being a police officer is a dangerous job. However, there are at least 18 jobs that are more dangerous. Jobs that are more than 2X as dangerous as being a police officer include commercial fisherman and fisherwomen (more than 7X as dangerous as the police officer job), loggers (more than 6X as dangerous), pilots (more than 3X as dangerous), roofers, steel workers, truck drivers, and garbage collectors.
- We are not nearly as accurate about predicting the future as we think we are. So, we confidently make bad predictions.
(If you came here from reading about the survey, click here to return )
- We are vulnerable to framing effects (the way the problem is worded affects the decision that we will make) because we are loss adverse: we hate to think that we might lose something. We like to gain, but we HATE to lose. Insurance companies and bankers love us for this.
- We have an optimism bias, so we think things will turn out well, and we forget that decisions often have unintended consequences (heroin was once considered a solution to opium addiction). In other words, a "solution" may not work and will probably have side effects. Examples of optimism bias:
- Businesses think that mergers will be successful, even though 84% of merger deals did not boost shareholder return.
- President Trump said that the COVID-19 would go away by April, 2020.
- President Trump urged people to take chloroquine, arguing, essentially, "what do you have to lose." However, chloroquine has side effects and at least one study suggested that people taking chloroquine were more likely to die than those not taking that drug.
- People dying by taking unproven cancer "cures" when they could have been saved by traditional medicine.
- Decision making can be stressful. Possible solutions:
- Don't rush to a decision. Sleep on it.
- Ask "What would I advise a friend to do?"
- Ask "What would happen if I did nothing?"
- Ask "How would I feel about this decision 5 years from now?"
- Have a Plan B.
- Change your options from "should I do x OR y?" to "Could I do X AND Y?"
V. Evaluate: Is it working?
Why we fail to find out whether our solution is effective.
We can't answer this question because